research topics for correlational research

• Write my thesis
• Thesis writers
• Buy thesis papers
• Bachelor thesis
• Master's thesis
• Thesis editing services
• Thesis proofreading services
• Buy a thesis online
• Write my dissertation
• Dissertation proposal help
• Pay for dissertation
• Custom dissertation
• Dissertation help online
• Buy dissertation online
• Cheap dissertation
• Dissertation editing services
• Write my research paper
• Buy research paper online
• Pay for research paper
• Research paper help
• Order research paper
• Custom research paper
• Cheap research paper
• Research papers for sale
• Thesis subjects
• How It Works

130+ Correlational Research Topics: Great Ideas For Students

The correlational research example title you decide to write will determine the uniqueness of your research paper. Choose a well-thought title that brings out the best of your expertise. Are you confused about which topic suits you? This article will let you know the best correlational research topics for students.

What is Correlation Research?

Correlational research involves looking at the affiliation between two or more study variables. The results of the study will have either a positive, negative, or zero correlation. More so, the research can either be quantitative or qualitative.

Now that you have the answer to “what are correlational studies,” we’ll focus on the various example topics students can use to write excellent papers.

Correlational Research Titles Examples for Highschool Students

Correlation topic examples for stem students, correlational research examples in education, correlational research questions in nursing, examples of correlational research topics in technology, correlational quantitative research topic examples in economics, correlational research topics in psychology, correlational research titles about business, correlational research sample title examples for statistics essays, correlational research examples for sociology research papers.

If you want your high school correlational research paper to stand out, go for creative and fun titles. Get a correlation research example below.

• How can you relate bullying and academic performance?
• Study habits vs academic grades
• Evaluating the link between student success and parents’ involvement
• Discuss test scores and study time
• Physical and mental health: The correlation
• Nutrition and study concentration
• The connection between good results and video games
• Clarifying the relationship between personality traits and subject preference
• The relationship between study time and poor grades
• The correlation between trainers’ support and students’ mental health
• The association between school bullying and absenteeism
• The effects of academic degrees on students’ career development
• Is there a correlation between teaching styles and students’ learning ability

These research topics for STEM students are game-changers. However, try any of the titles below regarding correlation in research.

The connection between:

• Food and drug efficacy
• Exercise and sleep
• Sleep patterns and heart rate
• Weather seasons and body immunity
• Wind speed and energy supply
• Rainfall extent and crop yields
• Respiratory health and air pollution
• Carbon emissions and global warming
• Stress and mental health
• Bridge capacity and preferred design
• Building quality and insulation capability
• Fuel efficiency and vehicle weight
• 19 th and 20 th Century approaches to stem subjects

As you learn more about the thesis statement about social media , keep a keen eye on each example of the correlational research paper we list below.

• The correlation between parental guidance and career decision
• Differences between student grades and career choice
• Teachers’ qualifications and students’ success in class
• The connection between teachers’ age and students’ performance
• Clarifying students’ workload and subject choice
• The link between teachers’ morale and students’ grades
• Discuss school location and performance metrics
• Clarifying the relationship between school curriculum and performance
• Relating school programs to students’ absenteeism
• Academic success vs teachers’ gender
• The association between parental income and school selection
• The effects of many subjects on students’ career choice
• The relationship between school grading and dropout rates

In addition to biochemistry topics and anatomy research paper topics , it also helps to know correlational research topics in nursing. Some of them include the following:

• Is there a relationship between sleep quality and post-surgery management?
• Is there a correlation between patient healing and the choice of drugs?
• Is there a link between physical activity levels and depression?
• Is there an association between nurse-patient communication and patient recovery?
• What is the correlation between age and child mortality in mothers?
• Is there a correlation between patient education and prompt recovery?
• What is the correlation between spirituality and the use of drugs?
• What is the link between patient adherence to drugs and age?
• What is the correlation between routine nursing and back pain?
• Is there a correlation between chemotherapy and fatigue?
• Is there a relationship between age and cholesterol levels?
• Is there a relationship between blood pressure and sleep disturbances?
• What is the link between drug use and organ failure?

A technology research-oriented paper should show your prowess in any area you tackle. Pick any example of a correlational research question from the list below for your research.

• Is there a relationship between screen time and eye strain?
• What is the link between video games and IQ levels?
• Is there a correlation between loneliness and tech dependence?
• What is the link between wireless technology and infertilities
• Is there a relationship between smartphone usage and sleep quality?
• Is there a correlation between academic performance and technology exposure?
• Is there a relationship between technology and physical activity levels?
• What is the correlation between self-esteem and technology?
• What is the link between technology and memory sharpness?
• What is the correlation between screen time and headaches?
• Is there a correlation between technology and anxiety?
• Is there a link between a sedentary lifestyle and technology?
• What is the correlation between tech dependence and communication skills?

The best example of correlational design in quantitative research will help you kickstart your research paper. In your paper, focus on discussing the relationship between the following:

• Inflation and unemployment rates
• Financial liberation and foreign aid
• Trade policies and foreign investors
• Income and nation’s well being
• Salary levels and education levels
• Urbanization and economic progress
• Economy growth rate and national budget
• Marital status and employed population
• Early retirements and the country’s growth
• Energy prices and economic growth
• Employee satisfaction and job retention
• Small-scale businesses and exploitative loans
• Educated population and nation’s economic levels

Depending on the preferred correlation method in research, your paper approach will vary. As you look at these social issues research topics , psychology correlational topics also come in handy.

Discuss the link between the following in your paper:

• Racism and population size
• Propaganda and marketing
• Cults and social class
• Bullying and skin color
• Child abuse and marriages
• Aging and hormones
• Leadership and communication
• Depression and discrimination
• Cognitive behavior therapy and age
• Eating disorders and genetics
• Attention and kids’ gender
• Speech disorder and tech dependence
• Perception and someone’s age

Business and economics research paper topics vary, but you should always go for the best. Here are some ideal topics for your correlation research paper in business.

Assess the link between:

• Remote employees and business growth
• Business ethic laws and productivity
• Language and business growth
• Foreign investments and cultural differences
• Monopoly and businesses closure
• Cultural practices and business survival
• Customer behaviors and products choice
• Advertising and business innovations
• Labor laws and taxation
• Technology and business trends
• Tourism and local economies
• Business sanctions and currency value
• Immigration and unemployment

You’ve probably encountered social media research topics and wondered whether you could get some focusing on statistics. Below examples will get you sorted.

Clarifying the relationship between:

• Rent costs and population
• COVID-19 vaccination and health budget
• Technology and data sample collection
• Education costs and income
• Education levels and job satisfaction
• Local trade volumes and dollar exchange rates
• Loans and small businesses’ growth rate
• Online and offline surveys
• Wage analysis and employee age
• National savings and employment rates
• Poverty and income inequality
• Trade and economic growth
• Interest rates and consumer borrowing behavior trends

In sociology, there are so many argumentative essay topics to write about. But when it comes to correlational topics, many students have a problem.

Write a sociology correlational research paper focusing on the association between:

• Social media and kids’ behaviors in school
• Food culture and modern lifestyle diseases
• Health equity and deaths
• Gender stereotypes and unemployment
• Women’s behaviors and mainstream media programs
• Age differences and abusive marriages
• Children’s obesity and social class
• Infertility and mental health among couples
• Bullying and past violence encounters in kids
• Genetically modified foods and lifestyle diseases
• Religious education and improving technology
• Social media and modern friendships
• Divorce and children education

Let’s now help you write your research paper on time. Whether it’s on sociology, economics, nursing or any other course, we are here for you. Our expert writers offer the best help on correlational research paper writing .

Leave a Reply Cancel reply

150+ Correlational Research Topics: Best Ideas For Students

Welcome to our blog, Correlational Research Topics! Research about connections is important for understanding how changes in one thing can relate to changes in another. But it does not mean one thing causes the other. This blog will cover the basics of research on connections. 

This includes what connections mean and different types of connections. We’ll also discuss what impacts connections and why carefully picking research topics matters. Plus, we’ll give examples of connection research topics in different fields. We’ll show why they’re important and could make a difference.

Whether you’re a student looking for research ideas or want to know about connections in the real world, this blog aims to give helpful ideas and motivation for your journey into connection research. Let’s dive in to learn correlational research topics!

What is Correlational Research?

Table of Contents

Correlation research studies how changes in one thing relate to changes in another. It looks at how two things are connected and if they change together. For example, studying whether people’s income and their level of education are correlated. 

Correlation research does not prove cause and effect. It shows relationships between things but not why they are related. More studies are needed to determine if one thing causes the other. Correlation research helps reveal trends and patterns between variables.

How to Select Correlational Research Topics

Here are some simple tips for choosing a good topic for correlational research:

• Pick two things you think are related, like age and memory or exercise and mood.
• Ensure you can measure these things with numbers, like hours exercised per week or the number of words remembered.
• Don’t try to prove one thing causes another; just look at how they are related.
• Pick timely topics that matter right now.
• Look at past research to get ideas and find gaps to fill.
• Think about questions you have about how certain things are connected.
• Look through research databases to find studies on relationships you’re curious about.
• Choose things that naturally connect in the real world, not random things.

The main goal is to pick two things you can measure that somehow seem to relate to each other. Spend time thinking of ideas before settling on a topic.

150+ Correlational Research Topics For Students

Here are over 150 correlational research topics categorized into different fields for students:

• The correlation between self-esteem and educational achievement among high school students.
• Relationship between self-esteem and social media usage in college students.
• Correlation between personality traits and career success.
• Impact of parental attachment styles on romantic relationships in young adults.
• Relationship between stress levels and sleep quality among university students.
• Correlation between emotional intelligence and leadership effectiveness.
• The connection between involvement of parents and academic performance in elementary school children.
• Correlation between anxiety levels and academic performance in college students.
• Relationship between attachment styles and childhood trauma in adulthood.
• Correlation between mindfulness practices and stress reduction among college students.

• The correlation between teacher-student rapport and student engagement in the classroom.
• Relationship between homework completion rates and academic achievement.
• Correlation between classroom environment and student motivation.
• Impact of involvement of parents in education on student performance.
• Relationship between school climate and student behavior.
• Correlation between extracurricular activities and academic success.
• The relationship between teacher feedback and pupil learning outcomes.
• Correlation between technology usage and academic performance.
• Relationship between school resources and student achievement.
• Correlation between bullying experiences and academic performance.
• The correlation between the status of socioeconomic and access to healthcare.
• Relationship between family structure and juvenile delinquency rates.
• Correlation between media representation and cultural perceptions.
• Impact of community involvement on crime rates.
• Relationship between religion and political affiliation.
• Correlation between social support networks and mental health outcomes.
• Relationship between gender roles and career choices.
• Correlation between immigration rates and cultural assimilation.
• Relationship between income inequality and social mobility.
• Correlation between social media usage and social interaction patterns.
• The correlation between growth of GDP and unemployment rates.
• Relationship between inflation rates and consumer spending.
• Correlation between government spending and economic growth.
• Impact of trade policies on economic development.
• Relationship between interest rates and investment behavior.
• Correlation between income inequality and economic stability.
• Relationship between education levels and income disparity.
• Correlation between taxation policies and income distribution.
• Impact of globalization on income inequality.
• Relationship between poverty rates and access to healthcare.

Health and Medicine

• The correlation between exercise frequency and mental health outcomes.
• Relationship between diet quality and cardiovascular health.
• Correlation between habits of smoking and lung cancer rates.
• Impact of sleep duration on physical health.
• Relationship between anxiety levels and immune system function.
• Relationship between vaccination rates and disease prevalence.
• Correlation between air pollution and respiratory diseases.
• Impact of social support networks on recovery from illness.
• Relationship between alcohol consumption and liver health.

Environmental Science

• The correlation between deforestation and biodiversity loss.
• Relationship between greenhouse gas emissions and world temperatures.
• Correlation between water pollution levels and aquatic biodiversity.
• Impact of urbanization on air quality.
• Relationship between waste management practices and environmental sustainability.
• Correlation between agricultural practices and soil erosion rates.
• Relationship between renewable energy usage and carbon emissions.
• Correlation between climate change and natural disasters.
• Impact of plastic pollution on marine ecosystems.
• Relationship between population growth and resource depletion.

Business and Management

• The correlation between employee satisfaction and productivity.
• Relationship between leadership styles and team performance.
• Correlation between employee training programs and job satisfaction.
• Impact of organizational culture on employee turnover rates.
• Relationship between customer satisfaction and business profitability.
• Correlation between marketing strategies and customer retention.
• Relationship between the corporate social responsibility and brand reputation.
• Correlation between employee diversity and innovation.
• Impact of supply chain management practices on company performance.
• Relationship between economic indicators and stock market fluctuations.

Technology and Society

• The correlation between social media usage and loneliness feelings.
• Relationship between screen time and attention span in children.
• Correlation between video game usage and aggression levels.
• Impact of smartphone usage on sleep quality.
• Relationship between the online concerns of privacy and social media usage.
• Correlation between digital literacy skills and academic performance.
• Relationship between technology adoption rates and generational differences.
• Correlation between Internet access and economic development.
• Relationship between online shopping habits and environmental sustainability.
• Correlation between technology usage and mental health outcomes.

Sports and Exercise Science

• The correlation between physical activity levels and cardiovascular health.
• Relationship between nutrition habits and athletic performance.
• Correlation between training intensity and muscle growth.
• Impact of sleep quality on athletic recovery.
• Relationship between exercise frequency and mental well-being.
• Correlation between sports participation and academic performance.
• Relationship between injuries in sports and long-term health outcomes.
• Correlation between coaching styles and athlete motivation.
• Impact of sports specialization on injury risk.
• Relationship between exercise adherence and weight management.

Media and Communication

• The correlation between media consumption habits and political beliefs.
• Relationship between advertising exposure and consumer behavior.
• Correlation between news coverage and public opinion.
• Influence of social media influencers on buying decisions.
• The connection between critical thinking skills and media literacy.
• Correlation between television viewing habits and body image issues.
• Relationship between media representation and societal norms.
• Correlation between online communication and interpersonal relationships.
• Relationship between media exposure and aggression in children.
• Correlation between streaming services usage and traditional media consumption.

Arts and Culture

• The correlation between education in arts and academic achievement.
• Relationship between cultural experiences and empathy levels.
• Correlation between music preferences and personality traits.
• Impact of cultural diversity on creative industries.
• Relationship between art participation and mental health outcomes.
• Correlation between museum attendance and community engagement.
• Relationship between literature consumption and empathy development.
• Correlation between cultural events attendance and social cohesion.
• Impact of arts funding on community development.
• Relationship between artistic expression and emotional well-being.

Political Science

• The correlation between voter turnout and socioeconomic status.
• Relationship between political ideology and environmental policies.
• Correlation between campaign spending and election outcomes.
• Impact of political polarization on civic engagement.
• Relationship between media bias and public perception of political issues.
• Correlation between government transparency and public trust.
• Relationship between political party cooperation and attitudes towards immigration.
• Correlation between political rhetoric and hate crime rates.
• Relationship between political knowledge and participation in democratic processes.
• Correlation between lobbying efforts and policy outcomes.

Law and Justice

• The correlation between socioeconomic status and incarceration rates.
• Relationship between sentencing disparities and racial identity.
• Correlation between police presence and crime rates in urban areas.
• Impact of therapeutic programs of justices on recidivism rates.
• Relationship between access to legal representation and court outcomes.
• Correlation between mandatory sentencing laws and prison overcrowding.
• Relationship between drug policy enforcement and addiction rates.
• Correlation between control laws on guns and firearm-related deaths.
• Relationship between immigration policies and crime rates.
• Correlation between juvenile justice interventions and rehabilitation outcomes.

History and Anthropology

• The correlation between archaeological findings and historical narratives.
• Relationship between language diversity and cultural preservation.
• Correlation between migration patterns and cultural diffusion.
• Impact of colonialism on indigenous cultures.
• Relationship between cultural practices and social hierarchy.
• Correlation between climate change and human migration.
• Relationship between trade routes and cultural exchange.
• Correlation between artistic expressions and societal values.
• Relationship between religious beliefs and cultural traditions.
• Correlation between technological advancements and societal change.

Gender Studies

• The correlation between gender stereotypes and career choices.
• Relationship between media representation and gender norms.
• Correlation between gender wage gap and educational attainment.
• Impact of gender individuality on mental health outcomes.
• Relationship between gender roles and domestic responsibilities.
• Correlation between workplace discrimination and gender diversity.
• Relationship between feminism and political participation.
• Correlation between LGBTQ+ rights advocacy and social acceptance.
• Relationship between gender-based violence and cultural attitudes.
• Correlation between gender equity policies and workplace satisfaction.

Miscellaneous

• The correlation between pet ownership and mental health.
• Relationship between travel experiences and cultural awareness.
• Correlation between volunteering activities and life satisfaction.
• Impact of hobbies on stress management.
• Relationship between religious beliefs and charitable giving.
• Correlation between language proficiency and cognitive abilities.
• Relationship between parenting styles and child development results.
• Correlation between financial literacy and money management skills.
• Correlation between social network size and happiness levels.

These correlational research topics cover a wide range of areas and can inspire students looking to conduct correlational research in various fields.

Challenges and Limitations

Here are some simple challenges with correlational research:

• It can’t prove one thing causes another, only that things are related.
• Other factors could affect the relationship you see between the two things you’re studying.
• Hard to know which thing impacts the other or if they impact each other.
• Just because two things are correlated does not mean they have a strong relationship. The correlation could be weak.
• Uses observational data, so there is less control than in experiments.
• This might not apply to everyone, only the group studied.
• People may not be honest or accurate if they self-report data like in surveys.

In summary, correlational research can only show two things that relate in some way but can’t prove causation or account for other factors that might affect the relationship. The results may only apply to the sample studied, too. These are good limitations to be aware of.

Best Practices for Correlational Research

Here are some best practices for conducting quality correlational research:

• Use a large random sample representing the population you want to generalize to. This strengthens the external validity of your findings.
• Measure variables accurately and reliably using validated instruments. Poor measurement can obscure relationships.
• Collect data prospectively, if possible, rather than retrospectively. This avoids reliance on recollection.
• Use multiple data points over time (longitudinal data) rather than a single data collection. This provides more insight into relationships.
• Examine curvilinear relationships in addition to linear ones. The correlation may only occur at certain levels.
• Control statistically for potential third variables that may influence the relationship. This provides a clearer assessment of the relationship.
• Assess directionality and potential interactive or reciprocal relationships using path analysis or longitudinal data. This provides greater understanding.
• Use multiple regression techniques to model more complex relationships among many variables.
• Report effect sizes and confidence intervals, not just statistical significance. Effect size indicates practical importance.
• Cautiously interpret results and do not overstate causality claims. Correlation does not equal causation.
• Replicate findings using different samples to assess generalizability and consistency.

Following best practices strengthens correlational research’s rigor, analysis, and interpretation. Adhering to these can produce higher-quality studies.

Final Remarks

Studying correlational research topics can help us learn much about how different things are related. Psychology, education, and business students can pick topics to research and find interesting connections. They can learn if certain things appear to go up or down together. This can give useful information to help make decisions or create policies.

When students carefully choose a correlational research topic and study the data, they can add to what we know about real-world relationships. For example, they may find links between sleep and grades, exercise and mood, or class size and learning.

Doing correlational research allows students to spot patterns between things and practice research skills. As they choose their topics, students can find exciting areas to explore. Uncovering correlations teaches us more about the complicated links between things in the world around us. With simple hard work, students can use correlational research to reveal new insights.

Similar Articles

Top 100 Research Topics In Commerce Field

The world of commerce is rapidly evolving. With new technologies, globalization, and changing consumer behaviors, many exciting research topics exist…

Top 30+ Mini Project Ideas For Computer Engineering Students

Mini projects are really important for computer engineering students. They help students learn by doing practical stuff alongside their regular…

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed .

120+ Great Correlational Research Topics For Students In 2024

Want to know the best correlational research topics for students? Ever wondered about the connections between things? That’s what correlation research is all about! In this article, we’ll dive into correlation research topics for students, explaining and offering a big list of interesting topics. Whether you’re a high school student starting a science project or a college student looking for a thesis idea, there’s something here for everyone.

Also Like To Read: Business Research Topics for College Students

Table of Contents

What Is Correlational Research?

Correlation research is about exploring connections between different things. It helps determine if changes in one thing are linked to changes in another. But remember, just because things are linked doesn’t mean one causes the other. It’s like finding patterns without saying one thing makes the other happen.

How To Choose Great Correlational Research Topics For Students

Picking the right topic is crucial for a good study. Here are some tips:

• Pick What Interests You:  Choose topics that you find interesting. It makes studying more enjoyable.
• Look Around:  Think about things happening around you or in the news. What’s interesting or important?
• Read Some Studies:  Check out what others have studied. Is there something missing or not clear? That could be your topic.
• Brainstorm Ideas:  Make a list of ideas. Big ideas and small ideas – anything that comes to mind.

List of Interesting Correlational Research Topics For Students

Now, let’s explore a variety of topics you can dig into across different areas:

Cool Correlational Research Topics For High School Students

• How does bullying relate to academic performance?
• Do good study habits connect to better grades?
• Exploring the link between student success and parents’ involvement.
• Discussing test scores and study time.
• Understanding the correlation between physical and mental health.
• Examining nutrition and its impact on study concentration.
• Investigated the correlation between video games and good grades. 
• Relationship between personality traits and subject preference.
• The link between study time and poor grades.
• How does trainers’ support connect to students’ mental health?

Most Recent Correlation Research Topics for STEM Students

• Exploring the connection between food and drug efficacy.
• Investigating the correlation between exercise and sleep.
• Understanding sleep patterns and heart rate.
• Examining the link between weather seasons and body immunity.
• Connecting wind speed and energy supply.
• Investigating rainfall extent and crop yields.
• Exploring respiratory health and air pollution.
• Correlation between carbon emissions and global warming.
• Stress and its connection to mental health.
• Bridge capacity and preferred design.

Examples in Correlational Research For College Students

• The correlation between parental guidance and career decisions.
• Differences between student grades and career choices.
• A Teacher’ qualifications and students’ success example in class.
• Major Link between teachers’ age and students’ performance.
• Example of Clarifying students’ workload and subject choice.
• Difference between teachers’ morale and students’ grades.
• Example in School location and performance metrics.
• Relationship between school curriculum and performance.
• Relating school programs to students’ absenteeism.
• Difference In Academic success vs teachers’ gender

Nursing-Related Correlation Questions

• Relationship between sleep quality and post-surgery management
• Does patient healing correlate with the choice of drugs?
• What is the difference between physical activity levels and depression?
• How does nurse-patient communication connect to patient recovery?
• The correlation between age and child mortality in mothers.
• Does patient education correlate with prompt recovery?
• The connection between spirituality and drug use.
• How does adherence to drugs correlate with age?
• Major Correlation between routine nursing and back pain.
• Is there a connection between chemotherapy and fatigue?

Technology Ralted Correlation Research Topics For Students

• Relationship between screen time and eye strain
• The link between video games and IQ levels
• Does loneliness correlate with tech dependence?
• The connection between wireless technology and infertility.
• Relationship between smartphone usage and sleep quality
• Does academic performance correlate with technology exposure?
• Relationship between technology and physical activity levels
• Correlation between self-esteem and technology
• The link between technology and memory sharpness.
• Is there a correlation between screen time and headaches?

Qualitative Correlational Research Topics For Students in Economics

• Inflation and unemployment rates correlation.
• Financial liberation and foreign aid connection.
• Trade policies and foreign investors’ correlation.
• Income and a nation’s well-being link.
• Salary levels and education levels correlation.
• Urbanization and economic progress connection.
• Economy growth rate and national budget correlation.
• Marital status and employed population link.
• Early retirements and the country’s growth connection.
• Energy prices and economic growth correlation.

Quantitative Correlational Research Questions in Nursing

• Correlation between racism and population size.
• Propaganda and marketing connection.
• Cults and social class correlation.
• Bullying and skin color connection.
• Child abuse and marriages correlation.
• Aging and hormones connection.
• Leadership and communication correlation.
• Depression and discrimination connection.
• Cognitive behavior therapy and age correlation.
• Eating disorders and genetics connection.

Correlational Research Titles About Business

• Remote employees and business growth correlation.
• Business ethic laws and productivity connection.
• Language and business growth correlation.
• Foreign investments and cultural differences link.
• Monopoly and businesses closure correlation.
• Cultural practices and business survival connection.
• Customer behaviors and product choice correlation.
• Advertising and business innovations connection.
• Labor laws and taxation correlation.
• Technology and business trends link.

Best Correlational Research Sample Title Examples for Statistics Essays

• Rent costs and population correlation.
• COVID-19 vaccination and health budget connection.
• Technology and data sample collection correlation.
• Education costs and income connection.
• Education levels and job satisfaction correlation.
• Local trade volumes and dollar exchange rates connection.
• Loans and small businesses’ growth rate correlation.
• Online and offline surveys connection.
• Wage analysis and employee age correlation.
• National savings and employment rates connection.

Good Correlational Research Examples for Sociology Research Papers

• Social media and kids’ behaviors in school correlation.
• Food culture and modern lifestyle diseases connection.
• Health equity and deaths correlation.
• Gender stereotypes and unemployment connection .
• Women’s behaviors and mainstream media programs correlation.
• Age differences and abusive marriages connection.
• Children’s obesity and social class correlation.
• Infertility and mental health among couples connection.
• Bullying and past violence encounters in kids correlation.
• Genetically modified foods and lifestyle diseases connection.

Exciting Correlational Research Topic & Title Examples

• The relationship between social media use and levels of anxiety in adolescents.
• Correlation between sleep patterns and academic performance in college students.

Correlational Research Topics For Students

• The connection between parental involvement and students’ academic achievement.
• Relationship between technology use in the classroom and student engagement.

Hot Correlational Research Topics For Students In Sociology

• Correlation between income levels and access to healthcare services.
• The impact of social media usage on interpersonal relationships.

Most Interesting Correlational Research Topics For Health Sciences

• Relationship between exercise frequency and mental health in adults.
• Correlation between diet and the prevalence of chronic diseases.

Correlational Research Topics About Business In The Philippines

• The relationship between employee job satisfaction and organizational productivity.
• Correlation between leadership styles and team performance in the workplace.

Environmental Science Correlational Research Topics

• The connection between air quality and respiratory health in urban areas.
• Relationship between waste disposal practices and environmental sustainability.

Economics Correlational Research Topics For Students

• Correlation between inflation rates and consumer spending habits.
• The impact of education levels on individual income and economic growth.

Good Correlational Research Topics For Students About Political Science

• Relationship between political ideologies and voting behavior.
• Correlation between government transparency and public trust.

Communication-Related Correlational Research Topics

• The connection between media consumption and political opinions.
• Relationship between communication styles and workplace conflicts.

Linguistics-Related Correlational Research Topics For Students

• Correlation between bilingualism and cognitive abilities in children.
• The impact of language diversity on team collaboration in multinational companies.

Anthropology Correlational Research Topics For Students

• Relationship between cultural diversity and mental health outcomes.
• Correlation between traditional practices and community well-being.

Greatest Correlational Research Topics For Criminal Justice

• The connection between socioeconomic status and crime rates.
• Relationship between community policing and trust in law enforcement.

Best Correlational Research Topics For Students In Nursing and Healthcare

• Correlation between nurse-patient communication and patient satisfaction.
• The impact of nurse staffing levels on patient outcomes.

Computer Science-Related Correlational Research Topics

• Relationship between smartphone usage and productivity in the tech industry.
• Correlation between programming skills and job success in the IT field.

Engineering Correlational Research Topics For Students

• The connection between environmental engineering practices and pollution levels.
• Relationship between project management strategies and construction project success.

What Are The Best Topics For Correlational Research About Accountancy, Business, And Management Students?

Here are some correlational research topics for Accountancy, Business, and Management students:

So that’s all about the best correlational research topics for students. You can explore its essence and present many captivating topics spanning various disciplines. From psychology to business, education to STEM, a wealth of intriguing correlations is waiting to be uncovered. Remember, correlation does not imply causation, but with careful analysis and interpretation, correlational research can offer valuable insights into the interconnectedness of phenomena.

So, whether you’re a high school student embarking on a science project or a seasoned researcher seeking inspiration, the world of correlation research awaits your exploration.

Related Posts

149+ Most Interesting Civil Engineering Research Topics For Undergraduates

189+ Top-Rated Quantitative Research Topics For Accounting Students [Updated 2024]

Leave a comment cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Correlational Research | When & How to Use

Correlational Research | When & How to Use

Published on July 7, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Table of contents

Correlational vs. experimental research, when to use correlational research, how to collect correlational data, how to analyze correlational data, correlation and causation, other interesting articles, frequently asked questions about correlational research.

Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in data collection methods and the types of conclusions you can draw.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Correlational research is ideal for gathering data quickly from natural settings. That helps you generalize your findings to real-life situations in an externally valid way.

There are a few situations where correlational research is an appropriate choice.

To investigate non-causal relationships

You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.

Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.

To explore causal relationships between variables

You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.

Correlational research can provide initial indications or additional support for theories about causal relationships.

To test new measurement tools

You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .

Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.

There are many different methods you can use in correlational research. In the social and behavioral sciences, the most common data collection methods for this type of research include surveys, observations , and secondary data.

It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without research bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by mail, by phone, or in person.

Surveys are a quick, flexible way to collect standardized data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.

Naturalistic observation

Naturalistic observation is a type of field research where you gather data about a behavior or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorizing actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analyzed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalize your results to real world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.

Secondary data

Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.

Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.

After collecting data, you can statistically analyze the relationship between variables using correlation or regression analyses, or both. You can also visualize the relationships between variables with a scatterplot.

Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .

Correlation analysis

Using a correlation analysis, you can summarize the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.

The Pearson product-moment correlation coefficient , also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.

Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.

Regression analysis

With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.

You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other for a few reasons.

Directionality problem

If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.

Third variable problem

A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.

In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). Correlational Research | When & How to Use. Scribbr. Retrieved August 7, 2024, from https://www.scribbr.com/methodology/correlational-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, what is quantitative research | definition, uses & methods, correlation vs. causation | difference, designs & examples, correlation coefficient | types, formulas & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• Privacy Policy

Home » Correlational Research – Methods, Types and Examples

Correlational Research – Methods, Types and Examples

Table of Contents

Correlational Research

Correlational Research is a type of research that examines the statistical relationship between two or more variables without manipulating them. It is a non-experimental research design that seeks to establish the degree of association or correlation between two or more variables.

Types of Correlational Research

There are three types of correlational research:

Positive Correlation

A positive correlation occurs when two variables increase or decrease together. This means that as one variable increases, the other variable also tends to increase. Similarly, as one variable decreases, the other variable also tends to decrease. For example, there is a positive correlation between the amount of time spent studying and academic performance. The more time a student spends studying, the higher their academic performance is likely to be. Similarly, there is a positive correlation between a person’s age and their income level. As a person gets older, they tend to earn more money.

Negative Correlation

A negative correlation occurs when one variable increases while the other decreases. This means that as one variable increases, the other variable tends to decrease. Similarly, as one variable decreases, the other variable tends to increase. For example, there is a negative correlation between the number of hours spent watching TV and physical activity level. The more time a person spends watching TV, the less physically active they are likely to be. Similarly, there is a negative correlation between the amount of stress a person experiences and their overall happiness. As stress levels increase, happiness levels tend to decrease.

Zero Correlation

A zero correlation occurs when there is no relationship between two variables. This means that the variables are unrelated and do not affect each other. For example, there is zero correlation between a person’s shoe size and their IQ score. The size of a person’s feet has no relationship to their level of intelligence. Similarly, there is zero correlation between a person’s height and their favorite color. The two variables are unrelated to each other.

Correlational Research Methods

Correlational research can be conducted using different methods, including:

Surveys are a common method used in correlational research. Researchers collect data by asking participants to complete questionnaires or surveys that measure different variables of interest. Surveys are useful for exploring the relationships between variables such as personality traits, attitudes, and behaviors.

Observational Studies

Observational studies involve observing and recording the behavior of participants in natural settings. Researchers can use observational studies to examine the relationships between variables such as social interactions, group dynamics, and communication patterns.

Archival Data

Archival data involves using existing data sources such as historical records, census data, or medical records to explore the relationships between variables. Archival data is useful for investigating the relationships between variables that cannot be manipulated or controlled.

Experimental Design

While correlational research does not involve manipulating variables, researchers can use experimental design to establish cause-and-effect relationships between variables. Experimental design involves manipulating one variable while holding other variables constant to determine the effect on the dependent variable.

Meta-Analysis

Meta-analysis involves combining and analyzing the results of multiple studies to explore the relationships between variables across different contexts and populations. Meta-analysis is useful for identifying patterns and inconsistencies in the literature and can provide insights into the strength and direction of relationships between variables.

Data Analysis Methods

Correlational research data analysis methods depend on the type of data collected and the research questions being investigated. Here are some common data analysis methods used in correlational research:

Correlation Coefficient

A correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. The correlation coefficient ranges from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation, and 0 indicating no correlation. Researchers use correlation coefficients to determine the degree to which two variables are related.

Scatterplots

A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents a single observation. The x-axis represents one variable, and the y-axis represents the other variable. The pattern of data points on the plot can provide insights into the strength and direction of the relationship between the two variables.

Regression Analysis

Regression analysis is a statistical method used to model the relationship between two or more variables. Researchers use regression analysis to predict the value of one variable based on the value of another variable. Regression analysis can help identify the strength and direction of the relationship between variables, as well as the degree to which one variable can be used to predict the other.

Factor Analysis

Factor analysis is a statistical method used to identify patterns among variables. Researchers use factor analysis to group variables into factors that are related to each other. Factor analysis can help identify underlying factors that influence the relationship between two variables.

Path Analysis

Path analysis is a statistical method used to model the relationship between multiple variables. Researchers use path analysis to test causal models and identify direct and indirect effects between variables.

Applications of Correlational Research

Correlational research has many practical applications in various fields, including:

• Psychology : Correlational research is commonly used in psychology to explore the relationships between variables such as personality traits, behaviors, and mental health outcomes. For example, researchers may use correlational research to examine the relationship between anxiety and depression, or the relationship between self-esteem and academic achievement.
• Education : Correlational research is useful in educational research to explore the relationships between variables such as teaching methods, student motivation, and academic performance. For example, researchers may use correlational research to examine the relationship between student engagement and academic success, or the relationship between teacher feedback and student learning outcomes.
• Business : Correlational research can be used in business to explore the relationships between variables such as consumer behavior, marketing strategies, and sales outcomes. For example, marketers may use correlational research to examine the relationship between advertising spending and sales revenue, or the relationship between customer satisfaction and brand loyalty.
• Medicine : Correlational research is useful in medical research to explore the relationships between variables such as risk factors, disease outcomes, and treatment effectiveness. For example, researchers may use correlational research to examine the relationship between smoking and lung cancer, or the relationship between exercise and heart health.
• Social Science : Correlational research is commonly used in social science research to explore the relationships between variables such as socioeconomic status, cultural factors, and social behavior. For example, researchers may use correlational research to examine the relationship between income and voting behavior, or the relationship between cultural values and attitudes towards immigration.

Examples of Correlational Research

• Psychology : Researchers might be interested in exploring the relationship between two variables, such as parental attachment and anxiety levels in young adults. The study could involve measuring levels of attachment and anxiety using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying potential risk factors for anxiety in young adults, and in developing interventions that could help improve attachment and reduce anxiety.
• Education : In a correlational study in education, researchers might investigate the relationship between two variables, such as teacher engagement and student motivation in a classroom setting. The study could involve measuring levels of teacher engagement and student motivation using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying strategies that teachers could use to improve student motivation and engagement in the classroom.
• Business : Researchers might explore the relationship between two variables, such as employee satisfaction and productivity levels in a company. The study could involve measuring levels of employee satisfaction and productivity using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying factors that could help increase productivity and improve job satisfaction among employees.
• Medicine : Researchers might examine the relationship between two variables, such as smoking and the risk of developing lung cancer. The study could involve collecting data on smoking habits and lung cancer diagnoses, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying risk factors for lung cancer and in developing interventions that could help reduce smoking rates.
• Sociology : Researchers might investigate the relationship between two variables, such as income levels and political attitudes. The study could involve measuring income levels and political attitudes using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in understanding how socioeconomic factors can influence political beliefs and attitudes.

How to Conduct Correlational Research

Here are the general steps to conduct correlational research:

• Identify the Research Question : Start by identifying the research question that you want to explore. It should involve two or more variables that you want to investigate for a correlation.
• Choose the research method: Decide on the research method that will be most appropriate for your research question. The most common methods for correlational research are surveys, archival research, and naturalistic observation.
• Choose the Sample: Select the participants or data sources that you will use in your study. Your sample should be representative of the population you want to generalize the results to.
• Measure the variables: Choose the measures that will be used to assess the variables of interest. Ensure that the measures are reliable and valid.
• Collect the Data: Collect the data from your sample using the chosen research method. Be sure to maintain ethical standards and obtain informed consent from your participants.
• Analyze the data: Use statistical software to analyze the data and compute the correlation coefficient. This will help you determine the strength and direction of the correlation between the variables.
• Interpret the results: Interpret the results and draw conclusions based on the findings. Consider any limitations or alternative explanations for the results.
• Report the findings: Report the findings of your study in a research report or manuscript. Be sure to include the research question, methods, results, and conclusions.

Purpose of Correlational Research

The purpose of correlational research is to examine the relationship between two or more variables. Correlational research allows researchers to identify whether there is a relationship between variables, and if so, the strength and direction of that relationship. This information can be useful for predicting and explaining behavior, and for identifying potential risk factors or areas for intervention.

Correlational research can be used in a variety of fields, including psychology, education, medicine, business, and sociology. For example, in psychology, correlational research can be used to explore the relationship between personality traits and behavior, or between early life experiences and later mental health outcomes. In education, correlational research can be used to examine the relationship between teaching practices and student achievement. In medicine, correlational research can be used to investigate the relationship between lifestyle factors and disease outcomes.

Overall, the purpose of correlational research is to provide insight into the relationship between variables, which can be used to inform further research, interventions, or policy decisions.

When to use Correlational Research

Here are some situations when correlational research can be particularly useful:

• When experimental research is not possible or ethical: In some situations, it may not be possible or ethical to manipulate variables in an experimental design. In these cases, correlational research can be used to explore the relationship between variables without manipulating them.
• When exploring new areas of research: Correlational research can be useful when exploring new areas of research or when researchers are unsure of the direction of the relationship between variables. Correlational research can help identify potential areas for further investigation.
• When testing theories: Correlational research can be useful for testing theories about the relationship between variables. Researchers can use correlational research to examine the relationship between variables predicted by a theory, and to determine whether the theory is supported by the data.
• When making predictions: Correlational research can be used to make predictions about future behavior or outcomes. For example, if there is a strong positive correlation between education level and income, one could predict that individuals with higher levels of education will have higher incomes.
• When identifying risk factors: Correlational research can be useful for identifying potential risk factors for negative outcomes. For example, a study might find a positive correlation between drug use and depression, indicating that drug use could be a risk factor for depression.

Characteristics of Correlational Research

Here are some common characteristics of correlational research:

• Examines the relationship between two or more variables: Correlational research is designed to examine the relationship between two or more variables. It seeks to determine if there is a relationship between the variables, and if so, the strength and direction of that relationship.
• Non-experimental design: Correlational research is typically non-experimental in design, meaning that the researcher does not manipulate any variables. Instead, the researcher observes and measures the variables as they naturally occur.
• Cannot establish causation : Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. Instead, it only provides information about the relationship between the variables.
• Uses statistical analysis: Correlational research relies on statistical analysis to determine the strength and direction of the relationship between variables. This may include calculating correlation coefficients, regression analysis, or other statistical tests.
• Observes real-world phenomena : Correlational research is often used to observe real-world phenomena, such as the relationship between education and income or the relationship between stress and physical health.
• Can be conducted in a variety of fields : Correlational research can be conducted in a variety of fields, including psychology, sociology, education, and medicine.
• Can be conducted using different methods: Correlational research can be conducted using a variety of methods, including surveys, observational studies, and archival studies.

Advantages of Correlational Research

There are several advantages of using correlational research in a study:

• Allows for the exploration of relationships: Correlational research allows researchers to explore the relationships between variables in a natural setting without manipulating any variables. This can help identify possible relationships between variables that may not have been previously considered.
• Useful for predicting behavior: Correlational research can be useful for predicting future behavior. If a strong correlation is found between two variables, researchers can use this information to predict how changes in one variable may affect the other.
• Can be conducted in real-world settings: Correlational research can be conducted in real-world settings, which allows for the collection of data that is representative of real-world phenomena.
• Can be less expensive and time-consuming than experimental research: Correlational research is often less expensive and time-consuming than experimental research, as it does not involve manipulating variables or creating controlled conditions.
• Useful in identifying risk factors: Correlational research can be used to identify potential risk factors for negative outcomes. By identifying variables that are correlated with negative outcomes, researchers can develop interventions or policies to reduce the risk of negative outcomes.
• Useful in exploring new areas of research: Correlational research can be useful in exploring new areas of research, particularly when researchers are unsure of the direction of the relationship between variables. By conducting correlational research, researchers can identify potential areas for further investigation.

Limitation of Correlational Research

Correlational research also has several limitations that should be taken into account:

• Cannot establish causation: Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. This is because it is not possible to control all possible confounding variables that could affect the relationship between the variables being studied.
• Directionality problem: The directionality problem refers to the difficulty of determining which variable is influencing the other. For example, a correlation may exist between happiness and social support, but it is not clear whether social support causes happiness, or whether happy people are more likely to have social support.
• Third variable problem: The third variable problem refers to the possibility that a third variable, not included in the study, is responsible for the observed relationship between the two variables being studied.
• Limited generalizability: Correlational research is often limited in terms of its generalizability to other populations or settings. This is because the sample studied may not be representative of the larger population, or because the variables studied may behave differently in different contexts.
• Relies on self-reported data: Correlational research often relies on self-reported data, which can be subject to social desirability bias or other forms of response bias.
• Limited in explaining complex behaviors: Correlational research is limited in explaining complex behaviors that are influenced by multiple factors, such as personality traits, situational factors, and social context.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Explanatory Research – Types, Methods, Guide

Applied Research – Types, Methods and Examples

Ethnographic Research -Types, Methods and Guide

Descriptive Research Design – Types, Methods and...

Textual Analysis – Types, Examples and Guide

Mixed Methods Research – Types & Analysis

• Correlational Research Designs: Types, Examples & Methods

The human mind is a powerful tool that allows you to sift through seemingly unrelated variables and establish a connection about a specific subject at hand. This skill is what comes into play when we talk about correlational research.

Did you know that Correlational research is something that you do every day; think about how you establish a connection between the doorbell ringing at a particular time and your Amazon package’s arrival. This is why you need to understand and know the different types of correlational research that are available and more importantly, how to go about it.

What is Correlational Research?

Correlational research is a type of research method that involves observing two variables in order to establish a statistically corresponding relationship between them. The aim of correlational research is to identify variables that have some sort of relationship to the extent that a change in one creates some change in the other. 

This type of research is descriptive, unlike experimental research which relies entirely on scientific methodology and hypothesis. For example, correlational research may reveal the statistical relationship between high-income earners and relocation; that is, the more people earn, the more likely they are to relocate or not. 

Correlational research is a way of studying two things to see if they’re related. For example, you might do a correlational study to see if there’s a relationship between how much time people spend on social media and how lonely they feel. Correlational research can’t prove that one thing causes the other, but it can show that there’s a link between them.

This type of research is descriptive, unlike  experimental research  which relies entirely on scientific methodology and hypothesis. For example, correlational research may reveal the statistical relationship between high-income earners and relocation; that is, the more people earn, the more likely they are to relocate or not.

What are the Types of Correlational Research?

Essentially, there are 3 types of correlational research which are positive correlational research, negative correlational research, and no correlational research. Each of these types is defined by peculiar characteristics. 

• Positive Correlational Research

Positive correlational research is a research method involving 2 variables that are statistically corresponding where an increase or decrease in 1 variable creates a like change in the other. An example is when an increase in workers’ remuneration results in an increase in the prices of goods and services and vice versa.

• Negative Correlational Research

Negative correlational research is a research method involving 2 variables that are statistically opposite where an increase in one of the variables creates an alternate effect or decrease in the other variable. An example of a negative correlation is if the rise in goods and services causes a decrease in demand and vice versa.

• Zero Correlational Research

Zero correlational research is a type of correlational research that involves 2 variables that are not necessarily statistically connected. In this case, a change in one of the variables may not trigger a corresponding or alternate change in the other variable.

Zero correlational research caters for variables with vague statistical relationships. For example, wealth and patience can be variables under zero correlational research because they are statistically independent. 

Sporadic change patterns that occur in variables with zero correlational are usually by chance and not as a result of corresponding or alternate mutual inclusiveness. 

Correlational research can also be classified based on data collection methods. Based on these, there are 3 types of correlational research: Naturalistic observation research, survey research and archival research. 

What are the Data Collection Methods in Correlational research? 

Data collection methods in correlational research are the research methodologies adopted by persons carrying out correlational research in order to determine the linear statistical relationship between 2 variables. These data collection methods are used to gather information in correlational research. 

The 3 methods of data collection in correlational research are naturalistic observation method, archival data method, and the survey method. All of these would be clearly explained in the subsequent paragraphs. 

• Naturalistic Observation

Naturalistic observation is a correlational research methodology that involves observing people’s behaviors as shown in the natural environment where they exist, over a period of time. It is a type of research-field method that involves the researcher paying closing attention to natural behavior patterns of the subjects under consideration.

This method is extremely demanding as the researcher must take extra care to ensure that the subjects do not suspect that they are being observed else they deviate from their natural behavior patterns. It is best for all subjects under observation to remain anonymous in order to avoid a breach of privacy. 

The major advantages of the naturalistic observation method are that it allows the researcher to fully observe the subjects (variables) in their natural state. However, it is a very expensive and time-consuming process plus the subjects can become aware of this act at any time and may act contrary. 

• Archival Data

Archival data is a type of correlational research method that involves making use of already gathered information about the variables in correlational research. Since this method involves using data that is already gathered and analyzed, it is usually straight to the point.

For this method of correlational research, the research makes use of earlier studies conducted by other researchers or the historical records of the variables being analyzed. This method helps a researcher to track already determined statistical patterns of the variables or subjects. 

This method is less expensive, saves time and provides the researcher with more disposable data to work with. However, it has the problem of data accuracy as important information may be missing from previous research since the researcher has no control over the data collection process. 

• Survey Method

The survey method is the most common method of correlational research; especially in fields like psychology. It involves random sampling of the variables or the subjects in the research in which the participants fill a questionnaire centered on the subjects of interest.

This method is very flexible as researchers can gather large amounts of data in very little time. However, it is subject to survey response bias and can also be affected by biased survey questions or under-representation of survey respondents or participants. 

These would be properly explained under data collection methods in correlational research. 

Examples of Correlational Research

There are a lot of examples of correlational research, and they all show how a correlational study can be used to figure out the statistical behavioural trend of the variables being studied. Here are 3 examples:

• You want to know if wealthy people are less likely to be patient. From your experience, you believe that wealthy people are impatient. However, you want to establish a statistical pattern that proves or disproves your belief. In this case, you can carry out correlational research to identify a trend that links both variables.
• You want to know if there’s a correlation between how much people earn and the number of children that they have. You do not believe that people with more spending power have more children than people with less spending power.

You think that how much people earn hardly determines the number of children that they have. Yet, carrying out correlational research on both variables could reveal any correlational relationship that exists between them. 

• You believe that domestic violence causes a brain hemorrhage. You cannot carry out an experiment as it would be unethical to deliberately subject people to domestic violence.

However, you can carry out correlational research to find out if victims of domestic violence suffer brain hemorrhage more than non-victims. 

What are the Characteristics of Correlational Research? 

• Correlational Research is non-experimental

Correlational research is non-experimental as it does not involve manipulating variables using a scientific methodology in order to agree or disagree with a hypothesis. In correlational research, the researcher simply observes and measures the natural relationship between 2 variables; without subjecting either of the variables to external conditioning.

• Correlational Research is Backward-looking

Correlational research doesn’t take the future into consideration as it only observes and measures the recent historical relationship that exists between 2 variables. In this sense, the statistical pattern resulting from correlational research is backward-looking and can seize to exist at any point, going forward.

Correlational research observes and measures historical patterns between 2 variables such as the relationship between high-income earners and tax payment. Correlational research may reveal a positive relationship between the aforementioned variables but this may change at any point in the future. 

• Correlational Research is Dynamic

Statistical patterns between 2 variables that result from correlational research are ever-changing. The correlation between 2 variables changes on a daily basis and such, it cannot be used as a fixed data for further research.

For example, the 2 variables can have a negative correlational relationship for a period of time, maybe 5 years. After this time, the correlational relationship between them can become positive; as observed in the relationship between bonds and stocks. 

• Data resulting from correlational research are not constant and cannot be used as a standard variable for further research.

What is the Correlation Coefficient? 

A correlation coefficient is an important value in correlational research that indicates whether the inter-relationship between 2 variables is positive, negative or non-existent. It is usually represented with the sign [r] and is part of a range of possible correlation coefficients from -1.0 to +1.0. 

The strength of a correlation between quantitative variables is typically measured using a statistic called Pearson’s Correlation Coefficient (or Pearson’s r) . A positive correlation is indicated by a value of 1.0, a perfect negative correlation is indicated by a value of -1.0 while zero correlation is indicated by a value of 0.0. 

It is important to note that a correlation coefficient only reflects the linear relationship between 2 variables; it does not capture non-linear relationships and cannot separate dependent and independent variables. The correlation coefficient helps you to determine the degree of statistical relationship that exists between variables. 

What are the Advantages of Correlational Research?

• In cases where carrying out experimental research is unethical, correlational research  can be used to determine the relationship between 2 variables. For example, when studying humans, carrying out an experiment can be seen as unsafe or unethical; hence, choosing correlational research would be the best option.
• Through correlational research, you can easily determine the statistical relationship between 2 variables.
• Carrying out correlational research is less time-consuming and less expensive than experimental research. This becomes a strong advantage when working with a minimum of researchers and funding or when keeping the number of variables in a study very low.
• Correlational research allows the researcher to carry out shallow data gathering using different methods such as a short survey. A short survey does not require the researcher to personally administer it so this allows the researcher to work with a few people.

What are the Disadvantages of Correlational Research? 

• Correlational research is limiting in nature as it can only be used to determine the statistical relationship between 2 variables. It cannot be used to establish a relationship between more than 2 variables.
• It does not account for cause and effect between 2 variables as it doesn’t highlight which of the 2 variables is responsible for the statistical pattern that is observed. For example, finding that education correlates positively with vegetarianism doesn’t explain whether being educated leads to becoming a vegetarian or whether vegetarianism leads to more education.
• Reasons for either can be assumed, but until more research is done, causation can’t be determined. Also, a third, unknown variable might be causing both. For instance, living in the state of Detroit can lead to both education and vegetarianism.
• Correlational research depends on past statistical patterns to determine the relationship between variables. As such, its data cannot be fully depended on for further research.
• In correlational research, the researcher has no control over the variables. Unlike experimental research, correlational research only allows the researcher to observe the variables for connecting statistical patterns without introducing a catalyst.
• The information received from correlational research is limited. Correlational research only shows the relationship between variables and does not equate to causation.

What are the Differences between Correlational and Experimental Research?  

• Methodology

The major difference between correlational research and experimental research is methodology. In correlational research, the researcher looks for a statistical pattern linking 2 naturally-occurring variables while in experimental research, the researcher introduces a catalyst and monitors its effects on the variables.

• Observation

In correlational research, the researcher passively observes the phenomena and measures whatever relationship that occurs between them. However, in experimental research, the researcher actively observes phenomena after triggering a change in the behavior of the variables.

In experimental research, the researcher introduces a catalyst and monitors its effects on the variables, that is, cause and effect. In correlational research, the researcher is not interested in cause and effect as it applies; rather, he or she identifies recurring statistical patterns connecting the variables in research.

• Number of Variables

research caters to an unlimited number of variables. Correlational research, on the other hand, caters to only 2 variables.

• Experimental research is causative while correlational research is relational.
• Correlational research is preliminary and almost always precedes experimental research.
• Unlike correlational research, experimental research allows the researcher to control the variables.

How to Use Online Forms for Correlational Research

One of the most popular methods of conducting correlational research is by carrying out a survey which can be made easier with the use of an online form. Surveys for correlational research involve generating different questions that revolve around the variables under observation and, allowing respondents to provide answers to these questions. 

Using an online form for your correlational research survey would help the researcher to gather more data in minimum time. In addition, the researcher would be able to reach out to more survey respondents than is plausible with printed correlational research survey forms . 

In addition, the researcher would be able to swiftly process and analyze all responses in order to objectively establish the statistical pattern that links the variables in the research. Using an online form for correlational research also helps the researcher to minimize the cost incurred during the research period. 

To use an online form for a correlational research survey, you would need to sign up on a data-gathering platform like Formplus . Formplus allows you to create custom forms for correlational research surveys using the Formplus builder. 

You can customize your correlational research survey form by adding background images, new color themes or your company logo to make it appear even more professional. In addition, Formplus also has a survey form template that you can edit for a correlational research study. 

You can create different types of survey questions including open-ended questions , rating questions, close-ended questions and multiple answers questions in your survey in the Formplus builder. After creating your correlational research survey, you can share the personalized link with respondents via email or social media.

Formplus also enables you to collect offline responses in your form.

Conclusion 

Correlational research enables researchers to establish the statistical pattern between 2 seemingly interconnected variables; as such, it is the starting point of any type of research. It allows you to link 2 variables by observing their behaviors in the most natural state. 

Unlike experimental research, correlational research does not emphasize the causative factor affecting 2 variables and this makes the data that results from correlational research subject to constant change. However, it is quicker, easier, less expensive and more convenient than experimental research. 

It is important to always keep the aim of your research at the back of your mind when choosing the best type of research to adopt. If you simply need to observe how the variables react to change then, experimental research is the best type to subscribe for. 

It is best to conduct correlational research using an online correlational research survey form as this makes the data-gathering process, more convenient. Formplus is a great online data-gathering platform that you can use to create custom survey forms for correlational research. 

Connect to Formplus, Get Started Now - It's Free!

• characteristics of correlational research
• types of correlational research
• what is correlational research
• busayo.longe

You may also like:

Projective Techniques In Surveys: Definition, Types & Pros & Cons

Introduction When you’re conducting a survey, you need to find out what people think about things. But how do you get an accurate and...

What is Network Sampling In Surveys?

Introduction Network sampling is a valuable technique in survey research that involves leveraging existing networks to select and...

Subgroup Analysis: What It Is + How to Conduct It

Introduction Clinical trials are an integral part of the drug development process. They aim to assess the safety and efficacy of a new...

Netnography in Research: What it is, Important, Pros & Cons

Currently, social media and digital communication are the top channels for communication. So, you need to adapt their marketing...

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Correlational Research: What it is with Examples

Our minds can do some brilliant things. For example, it can memorize the jingle of a pizza truck. The louder the jingle, the closer the pizza truck is to us. Who taught us that? Nobody! We relied on our understanding and came to a conclusion. We don’t stop there, do we? If there are multiple pizza trucks in the area and each one has a different jingle, we would memorize it all and relate the jingle to its pizza truck.

This is what correlational research precisely is, establishing a relationship between two variables, “jingle” and “distance of the truck” in this particular example. The correlational study looks for variables that seem to interact with each other. When you see one variable changing, you have a fair idea of how the other variable will change.

What is Correlational research?

Correlational research is a type of non-experimental research method in which a researcher measures two variables and understands and assesses the statistical relationship between them with no influence from any extraneous variable. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

Correlational Research Example

The correlation coefficient shows the correlation between two variables (A correlation coefficient is a statistical measure that calculates the strength of the relationship between two variables), a value measured between -1 and +1. When the correlation coefficient is close to +1, there is a positive correlation between the two variables. If the value is relative to -1, there is a negative correlation between the two variables. When the value is close to zero, then there is no relationship between the two variables.

Let us take an example to understand correlational research.

Consider hypothetically, a researcher is studying a correlation between cancer and marriage. In this study, there are two variables: disease and marriage. Let us say marriage has a negative association with cancer. This means that married people are less likely to develop cancer.

However, this doesn’t necessarily mean that marriage directly avoids cancer. In correlational research, it is not possible to establish the fact, what causes what. It is a misconception that a correlational study involves two quantitative variables. However, the reality is two variables are measured, but neither is changed. This is true independent of whether the variables are quantitative or categorical.

Types of correlational research

Mainly three types of correlational research have been identified:

1. Positive correlation: A positive relationship between two variables is when an increase in one variable leads to a rise in the other variable. A decrease in one variable will see a reduction in the other variable. For example, the amount of money a person has might positively correlate with the number of cars the person owns.

2. Negative correlation: A negative correlation is quite literally the opposite of a positive relationship. If there is an increase in one variable, the second variable will show a decrease, and vice versa.

For example, being educated might negatively correlate with the crime rate when an increase in one variable leads to a decrease in another and vice versa. If a country’s education level is improved, it can lower crime rates. Please note that this doesn’t mean that lack of education leads to crimes. It only means that a lack of education and crime is believed to have a common reason – poverty.

3. No correlation: There is no correlation between the two variables in this third type . A change in one variable may not necessarily see a difference in the other variable. For example, being a millionaire and happiness are not correlated. An increase in money doesn’t lead to happiness.

Characteristics of correlational research

Correlational research has three main characteristics. They are: 

• Non-experimental : The correlational study is non-experimental. It means that researchers need not manipulate variables with a scientific methodology to either agree or disagree with a hypothesis. The researcher only measures and observes the relationship between the variables without altering them or subjecting them to external conditioning.
• Backward-looking : Correlational research only looks back at historical data and observes events in the past. Researchers use it to measure and spot historical patterns between two variables. A correlational study may show a positive relationship between two variables, but this can change in the future.
• Dynamic : The patterns between two variables from correlational research are never constant and are always changing. Two variables having negative correlation research in the past can have a positive correlation relationship in the future due to various factors.

Data collection

The distinctive feature of correlational research is that the researcher can’t manipulate either of the variables involved. It doesn’t matter how or where the variables are measured. A researcher could observe participants in a closed environment or a public setting.

Researchers use two data collection methods to collect information in correlational research.

01. Naturalistic observation

Naturalistic observation is a way of data collection in which people’s behavioral targeting is observed in their natural environment, in which they typically exist. This method is a type of field research. It could mean a researcher might be observing people in a grocery store, at the cinema, playground, or in similar places.

Researchers who are usually involved in this type of data collection make observations as unobtrusively as possible so that the participants involved in the study are not aware that they are being observed else they might deviate from being their natural self.

Ethically this method is acceptable if the participants remain anonymous, and if the study is conducted in a public setting, a place where people would not normally expect complete privacy. As mentioned previously, taking an example of the grocery store where people can be observed while collecting an item from the aisle and putting in the shopping bags. This is ethically acceptable, which is why most researchers choose public settings for recording their observations. This data collection method could be both qualitative and quantitative . If you need to know more about qualitative data, you can explore our newly published blog, “ Examples of Qualitative Data in Education .”

02. Archival data

Another approach to correlational data is the use of archival data. Archival information is the data that has been previously collected by doing similar kinds of research . Archival data is usually made available through primary research .

In contrast to naturalistic observation, the information collected through archived data can be pretty straightforward. For example, counting the number of people named Richard in the various states of America based on social security records is relatively short.

Use the correlational research method to conduct a correlational study and measure the statistical relationship between two variables. Uncover the insights that matter the most. Use QuestionPro’s research platform to uncover complex insights that can propel your business to the forefront of your industry.

Research to make better decisions. Start a free trial today. No credit card required.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Life@QuestionPro: Thomas Maiwald-Immer’s Experience

Aug 9, 2024

Top 13 Reporting Tools to Transform Your Data Insights & More

Aug 8, 2024

Employee Satisfaction: How to Boost Your  Workplace Happiness?

Aug 7, 2024

Jotform vs Formstack: Which Form Builder Should You Choose?

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

7.2 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research.

What Is Correlational Research?

Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research.

The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). But because they could not manipulate the number of daily hassles their participants experienced, they had to settle for measuring the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 7.2 “Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists” shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 7.2 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data. A third, survey research, is discussed in its own chapter.

Naturalistic Observation

Naturalistic observation is an approach to data collection that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). It could involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are often not aware that they are being studied. Ethically, this is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

Researchers Robert Levine and Ara Norenzayan used naturalistic observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in the United States and Japan covered 60 feet in about 12 seconds on average, while people in Brazil and Romania took close to 17 seconds.

Because naturalistic observation takes place in the complex and even chaotic “real world,” there are two closely related issues that researchers must deal with before collecting data. The first is sampling. When, where, and under what conditions will the observations be made, and who exactly will be observed? Levine and Norenzayan described their sampling process as follows:

Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities. (p. 186)

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.

The second issue is measurement. What specific behaviors will be observed? In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance. Often, however, the behaviors of interest are not so obvious or objective. For example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979). But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

Naturalistic observation has revealed that bowlers tend to smile when they turn away from the pins and toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

sieneke toering – bowling big lebowski style – CC BY-NC-ND 2.0.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This is the issue of interrater reliability. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

Archival Data

Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005). In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988). In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as college students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as college students, the healthier they were as older men. Pearson’s r was +.25.

This is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

• Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
• Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behavior in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.

Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why.

• An educational researcher compares the academic performance of students from the “rich” side of town with that of students from the “poor” side of town.
• A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
• A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
• An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
• A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
• A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4 , 1–39.

Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553.

Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205.

Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110.

Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Correlational Research | Guide, Design & Examples

Correlational Research | Guide, Design & Examples

Published on 5 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Table of contents

Correlational vs experimental research, when to use correlational research, how to collect correlational data, how to analyse correlational data, correlation and causation, frequently asked questions about correlational research.

Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in how data is collected and the types of conclusions you can draw.

Prevent plagiarism, run a free check.

Correlational research is ideal for gathering data quickly from natural settings. That helps you generalise your findings to real-life situations in an externally valid way.

There are a few situations where correlational research is an appropriate choice.

To investigate non-causal relationships

You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.

Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.

To explore causal relationships between variables

You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.

Correlational research can provide initial indications or additional support for theories about causal relationships.

To test new measurement tools

You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .

Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.

There are many different methods you can use in correlational research. In the social and behavioural sciences, the most common data collection methods for this type of research include surveys, observations, and secondary data.

It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by post, by phone, or in person.

Surveys are a quick, flexible way to collect standardised data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.

Naturalistic observation

Naturalistic observation is a type of field research where you gather data about a behaviour or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorising actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analysed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalise your results to real-world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.

Secondary data

Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.

Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete, or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.

After collecting data, you can statistically analyse the relationship between variables using correlation or regression analyses, or both. You can also visualise the relationships between variables with a scatterplot.

Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .

Correlation analysis

Using a correlation analysis, you can summarise the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.

The Pearson product-moment correlation coefficient, also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.

Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.

Regression analysis

With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.

You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.

It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other, for a few reasons.

Directionality problem

If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.

Third variable problem

A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.

In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, December 05). Correlational Research | Guide, Design & Examples. Scribbr. Retrieved 5 August 2024, from https://www.scribbr.co.uk/research-methods/correlational-research-design/

Is this article helpful?

Pritha Bhandari

Other students also liked, a quick guide to experimental design | 5 steps & examples, quasi-experimental design | definition, types & examples, qualitative vs quantitative research | examples & methods.

• How it works

Correlational Research – Steps & Examples

Published by Carmen Troy at August 14th, 2021 , Revised On August 29, 2023

In correlational  research design , a researcher measures the association between two or more variables or sets of scores. A researcher doesn’t have control over the  variables .

Example:  Relationship between income and age.

Types of Correlations

Based on the number of variables

Based on the direction of change of variables

When to Use Correlation Design?

Correlation research design is used when experimental studies are difficult to design. 

Example: You want to know the impact of tobacco on people’s health and the extent of their addiction. You can’t distribute tobacco among your participants to understand its effect and addiction level. Instead of it, you can collect information from the people who are already addicted to tobacco and affected by it.

It is used to identify the association between two or more variables.

Example: You want to find out whether there is a correlation between the increasing population and poverty among the people. You don’t think that an increasing population leads to unemployment, but identifying a relationship can help you find a better answer to your study.

Example: You want to find out whether high income causes obesity. However, you don’t see any relationship. However, you can still find out the association between the lifestyle, age, and eating patterns of the people to make predictions of your research question.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

How to Conduct Correlation Research?

Step 1: select the problem.

You can select the issues according to the requirement of your research. There are three common types of problems as follows;

• Is there any relationship between the two variables?
• How well does a variable predict another variable?
• What could be the association between a large number of variables and what predictions you can make?

Step 2: Select the Sample

You need to  select the sample  carefully and randomly if necessary. Your sample size should not be more than 30.

Step 3: Collect the Data

There are  various types of data collection methods  used in correlational research. The most common methods used for data collection are as follows:

Surveys  are the most frequently used method for collecting data. It helps find the association between variables based on the participants’ responses selected for the study. You can carry out the surveys online, face-to-face, and on the phone. 

Example: You want to find out the association between poverty and unemployment. You need to distribute a questionnaire about the sources of income and expenses among the participants. You can analyse the information obtained to identify whether unemployment leads to poverty.

Naturalistic Observation

In the naturalistic observation method, you need to collect the participants’ data by observing them in their natural surroundings. You can consider it as a type of field research. You can observe people and gather information from them in various public places such as stores, malls, parks, playgrounds, etc. The participants are not informed about the research. However, you need to ensure the anonymity of the participants. It includes both qualitative and quantitative data.

Example: You want to find out the correlation between the price hike of vegetables and whether changes. You need to visit the market and talk to vegetable vendors to collect the required information.  You can categorise the information according to the price, whether change effects and challenges the vendors/farmers face during such periods.

Archival Data

Archival data is a type of data or information that already exists. Instead of collecting new data, you can use the existing data in your research if it fulfills your research requirements. Generally, previous studies or theories, records, documents, and transcripts are used as the primary source of information. This type of research is also called retrospective research.

Example: Suppose you want to find out the relation between exercise and weight loss. You can use various scholarly journals, health records, and scientific studies and discoveries based on people’s age and gender. You can identify whether exercise leads to significant weight loss among people of various ages and gender.

What is Causation?

The association between cause and effect is called  causation . You can identify the correlation between the two variables, but they may not influence each other. It can be considered as the limitation of correlation research.

Example: You’ve found that people who exercise regularly lost maximum weight. However, it doesn’t prove that people who don’t use will gain weight. There could be many other possible variables, such as a healthy diet, age, stress, gender, and health condition, impacting people’s weight. You can’t find out the causation of your research problem. Still, you can collect and analyse data to support the theory. You can only predict the possibilities of the method, phenomena, or problem you are studying.

Frequently Asked Questions

How to describe correlational research.

Correlational research examines the relationship between two or more variables. It doesn’t imply causation but measures the strength and direction of association. Statistical analysis determines if changes in one variable correspond to changes in another, helping understand patterns and predict outcomes.

You May Also Like

Inductive and deductive reasoning takes into account assumptions and incidents. Here is all you need to know about inductive vs deductive reasoning.

This article presents the key advantages and disadvantages of secondary research so you can select the most appropriate research approach for your study.

This post provides the key disadvantages of secondary research so you know the limitations of secondary research before making a decision.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

What is Correlational Research? (+ Design, Examples)

Appinio Research · 04.03.2024 · 30min read

Ever wondered how researchers explore connections between different factors without manipulating them? Correlational research offers a window into understanding the relationships between variables in the world around us. From examining the link between exercise habits and mental well-being to exploring patterns in consumer behavior, correlational studies help us uncover insights that shape our understanding of human behavior, inform decision-making, and drive innovation. In this guide, we'll dive into the fundamentals of correlational research, exploring its definition, importance, ethical considerations, and practical applications across various fields. Whether you're a student delving into research methods or a seasoned researcher seeking to expand your methodological toolkit, this guide will equip you with the knowledge and skills to conduct and interpret correlational studies effectively.

What is Correlational Research?

Correlational research is a methodological approach used in scientific inquiry to examine the relationship between two or more variables. Unlike experimental research , which seeks to establish cause-and-effect relationships through manipulation and control of variables, correlational research focuses on identifying and quantifying the degree to which variables are related to one another. This method allows researchers to investigate associations, patterns, and trends in naturalistic settings without imposing experimental manipulations.

Importance of Correlational Research

Correlational research plays a crucial role in advancing scientific knowledge across various disciplines. Its importance stems from several key factors:

• Exploratory Analysis :  Correlational studies provide a starting point for exploring potential relationships between variables. By identifying correlations, researchers can generate hypotheses and guide further investigation into causal mechanisms and underlying processes.
• Predictive Modeling :  Correlation coefficients can be used to predict the behavior or outcomes of one variable based on the values of another variable. This predictive ability has practical applications in fields such as economics, psychology, and epidemiology, where forecasting future trends or outcomes is essential.
• Diagnostic Purposes:  Correlational analyses can help identify patterns or associations that may indicate the presence of underlying conditions or risk factors. For example, correlations between certain biomarkers and disease outcomes can inform diagnostic criteria and screening protocols in healthcare.
• Theory Development:  Correlational research contributes to theory development by providing empirical evidence for proposed relationships between variables. Researchers can refine and validate theoretical models in their respective fields by systematically examining correlations across different contexts and populations.
• Ethical Considerations:  In situations where experimental manipulation is not feasible or ethical, correlational research offers an alternative approach to studying naturally occurring phenomena. This allows researchers to address research questions that may otherwise be inaccessible or impractical to investigate.

Correlational vs. Causation in Research

It's important to distinguish between correlation and causation in research. While correlational studies can identify relationships between variables, they cannot establish causal relationships on their own. Several factors contribute to this distinction:

• Directionality:  Correlation does not imply the direction of causation. A correlation between two variables does not indicate which variable is causing the other; it merely suggests that they are related in some way. Additional evidence, such as experimental manipulation or longitudinal studies , is needed to establish causality.
• Third Variables:  Correlations may be influenced by third variables, also known as confounding variables, that are not directly measured or controlled in the study. These third variables can create spurious correlations or obscure true causal relationships between the variables of interest.
• Temporal Sequence:  Causation requires a temporal sequence, with the cause preceding the effect in time. Correlational studies alone cannot establish the temporal order of events, making it difficult to determine whether one variable causes changes in another or vice versa.

Understanding the distinction between correlation and causation is critical for interpreting research findings accurately and drawing valid conclusions about the relationships between variables. While correlational research provides valuable insights into associations and patterns, establishing causation typically requires additional evidence from experimental studies or other research designs.

Key Concepts in Correlation

Understanding key concepts in correlation is essential for conducting meaningful research and interpreting results accurately.

Correlation Coefficient

The correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. It's denoted by the symbol  r  and ranges from -1 to +1.

• A correlation coefficient of  -1  indicates a perfect negative correlation, meaning that as one variable increases, the other decreases in a perfectly predictable manner.
• A coefficient of  +1  signifies a perfect positive correlation, where both variables increase or decrease together in perfect sync.
• A coefficient of  0  implies no correlation, indicating no systematic relationship between the variables.

Strength and Direction of Correlation

The strength of correlation refers to how closely the data points cluster around a straight line on the scatterplot. A correlation coefficient close to -1 or +1 indicates a strong relationship between the variables, while a coefficient close to 0 suggests a weak relationship.

• Strong correlation:  When the correlation coefficient approaches -1 or +1, it indicates a strong relationship between the variables. For example, a correlation coefficient of -0.9 suggests a strong negative relationship, while a coefficient of +0.8 indicates a strong positive relationship.
• Weak correlation:  A correlation coefficient close to 0 indicates a weak or negligible relationship between the variables. For instance, a coefficient of -0.1 or +0.1 suggests a weak correlation where the variables are minimally related.

The direction of correlation determines how the variables change relative to each other.

• Positive correlation:  When one variable increases, the other variable also tends to increase. Conversely, when one variable decreases, the other variable tends to decrease. This is represented by a positive correlation coefficient.
• Negative correlation:  In a negative correlation, as one variable increases, the other variable tends to decrease. Similarly, when one variable decreases, the other variable tends to increase. This relationship is indicated by a negative correlation coefficient.

Scatterplots

A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents the values of both variables for a single observation. By plotting the data points on a Cartesian plane, you can visualize patterns and trends in the relationship between the variables.

• Interpretation:  When examining a scatterplot, observe the pattern of data points. If the points cluster around a straight line, it indicates a strong correlation. However, if the points are scattered randomly, it suggests a weak or no correlation.
• Outliers:  Identify any outliers or data points that deviate significantly from the overall pattern. Outliers can influence the correlation coefficient and may warrant further investigation to determine their impact on the relationship between variables.
• Line of Best Fit:  In some cases, you may draw a line of best fit through the data points to visually represent the overall trend in the relationship. This line can help illustrate the direction and strength of the correlation between the variables.

Understanding these key concepts will enable you to interpret correlation coefficients accurately and draw meaningful conclusions from your data.

How to Design a Correlational Study?

When embarking on a correlational study, careful planning and consideration are crucial to ensure the validity and reliability of your research findings.

Research Question Formulation

Formulating clear and focused research questions is the cornerstone of any successful correlational study. Your research questions should articulate the variables you intend to investigate and the nature of the relationship you seek to explore. When formulating your research questions:

• Be Specific:  Clearly define the variables you are interested in studying and the population to which your findings will apply.
• Be Testable:  Ensure that your research questions are empirically testable using correlational methods. Avoid vague or overly broad questions that are difficult to operationalize.
• Consider Prior Research:  Review existing literature to identify gaps or unanswered questions in your area of interest. Your research questions should build upon prior knowledge and contribute to advancing the field.

For example, if you're interested in examining the relationship between sleep duration and academic performance among college students, your research question might be: "Is there a significant correlation between the number of hours of sleep per night and GPA among undergraduate students?"

Participant Selection

Selecting an appropriate sample of participants is critical to ensuring the generalizability and validity of your findings. Consider the following factors when selecting participants for your correlational study:

• Population Characteristics:  Identify the population of interest for your study and ensure that your sample reflects the demographics and characteristics of this population.
• Sampling Method:  Choose a sampling method that is appropriate for your research question and accessible, given your resources and constraints. Standard sampling methods include random sampling, stratified sampling, and convenience sampling.
• Sample Size:   Determine the appropriate sample size based on factors such as the effect size you expect to detect, the desired level of statistical power, and practical considerations such as time and budget constraints.

For example, suppose you're studying the relationship between exercise habits and mental health outcomes in adults aged 18-65. In that case, you might use stratified random sampling to ensure representation from different age groups within the population.

Variables Identification

Identifying and operationalizing the variables of interest is essential for conducting a rigorous correlational study. When identifying variables for your research:

• Independent and Dependent Variables:  Clearly distinguish between independent variables (factors that are hypothesized to influence the outcome) and dependent variables (the outcomes or behaviors of interest).
• Control Variables:  Identify any potential confounding variables or extraneous factors that may influence the relationship between your independent and dependent variables. These variables should be controlled for in your analysis.
• Measurement Scales:  Determine the appropriate measurement scales for your variables (e.g., nominal, ordinal, interval, or ratio) and select valid and reliable measures for assessing each construct.

For instance, if you're investigating the relationship between socioeconomic status (SES) and academic achievement, SES would be your independent variable, while academic achievement would be your dependent variable. You might measure SES using a composite index based on factors such as income, education level, and occupation.

Data Collection Methods

Selecting appropriate data collection methods is essential for obtaining reliable and valid data for your correlational study. When choosing data collection methods:

• Quantitative vs. Qualitative :  Determine whether quantitative or qualitative methods are best suited to your research question and objectives. Correlational studies typically involve quantitative data collection methods like surveys, questionnaires, or archival data analysis.
• Instrument Selection:  Choose measurement instruments that are valid, reliable, and appropriate for your variables of interest. Pilot test your instruments to ensure clarity and comprehension among your target population.
• Data Collection Procedures :  Develop clear and standardized procedures for data collection to minimize bias and ensure consistency across participants and time points.

For example, if you're examining the relationship between smartphone use and sleep quality among adolescents, you might administer a self-report questionnaire assessing smartphone usage patterns and sleep quality indicators such as sleep duration and sleep disturbances.

Crafting a well-designed correlational study is essential for yielding meaningful insights into the relationships between variables. By meticulously formulating research questions , selecting appropriate participants, identifying relevant variables, and employing effective data collection methods, researchers can ensure the validity and reliability of their findings.

With Appinio , conducting correlational research becomes even more seamless and efficient. Our intuitive platform empowers researchers to gather real-time consumer insights in minutes, enabling them to make informed decisions with confidence.

Experience the power of Appinio and unlock valuable insights for your research endeavors. Schedule a demo today and revolutionize the way you conduct correlational studies!

Book a Demo

How to Analyze Correlational Data?

Once you have collected your data in a correlational study, the next crucial step is to analyze it effectively to draw meaningful conclusions about the relationship between variables.

How to Calculate Correlation Coefficients?

The correlation coefficient is a numerical measure that quantifies the strength and direction of the relationship between two variables. There are different types of correlation coefficients, including Pearson's correlation coefficient (for linear relationships), Spearman's rank correlation coefficient (for ordinal data ), and Kendall's tau (for non-parametric data). Here, we'll focus on calculating Pearson's correlation coefficient (r), which is commonly used for interval or ratio-level data.

To calculate Pearson's correlation coefficient (r), you can use statistical software such as SPSS, R, or Excel. However, if you prefer to calculate it manually, you can use the following formula:

r = Σ((X - X̄)(Y - Ȳ)) / ((n - 1) * (s_X * s_Y))

• X  and  Y  are the scores of the two variables,
• X̄  and  Ȳ  are the means of X and Y, respectively,
• n  is the number of data points,
• s_X  and  s_Y  are the standard deviations of X and Y, respectively.

Interpreting Correlation Results

Once you have calculated the correlation coefficient (r), it's essential to interpret the results correctly. When interpreting correlation results:

• Magnitude:  The absolute value of the correlation coefficient (r) indicates the strength of the relationship between the variables. A coefficient close to 1 or -1 suggests a strong correlation, while a coefficient close to 0 indicates a weak or no correlation.
• Direction:  The sign of the correlation coefficient (positive or negative) indicates the direction of the relationship between the variables. A positive correlation coefficient indicates a positive relationship (as one variable increases, the other tends to increase), while a negative correlation coefficient indicates a negative relationship (as one variable increases, the other tends to decrease).
• Statistical Significance :  Assess the statistical significance of the correlation coefficient to determine whether the observed relationship is likely to be due to chance. This is typically done using hypothesis testing, where you compare the calculated correlation coefficient to a critical value based on the sample size and desired level of significance (e.g.,  α =0.05).

Statistical Significance

Determining the statistical significance of the correlation coefficient involves conducting hypothesis testing to assess whether the observed correlation is likely to occur by chance. The most common approach is to use a significance level (alpha,  α ) of 0.05, which corresponds to a 5% chance of obtaining the observed correlation coefficient if there is no true relationship between the variables.

To test the null hypothesis that the correlation coefficient is zero (i.e., no correlation), you can use inferential statistics such as the t-test or z-test. If the calculated p-value is less than the chosen significance level (e.g.,  p <0.05), you can reject the null hypothesis and conclude that the correlation coefficient is statistically significant.

Remember that statistical significance does not necessarily imply practical significance or the strength of the relationship. Even a statistically significant correlation with a small effect size may not be meaningful in practical terms.

By understanding how to calculate correlation coefficients, interpret correlation results, and assess statistical significance, you can effectively analyze correlational data and draw accurate conclusions about the relationships between variables in your study.

Correlational Research Limitations

As with any research methodology, correlational studies have inherent considerations and limitations that researchers must acknowledge and address to ensure the validity and reliability of their findings.

Third Variables

One of the primary considerations in correlational research is the presence of third variables, also known as confounding variables. These are extraneous factors that may influence or confound the observed relationship between the variables under study. Failing to account for third variables can lead to spurious correlations or erroneous conclusions about causality.

For example, consider a correlational study examining the relationship between ice cream consumption and drowning incidents. While these variables may exhibit a positive correlation during the summer months, the true causal factor is likely to be a third variable—such as hot weather—that influences both ice cream consumption and swimming activities, thereby increasing the risk of drowning.

To address the influence of third variables, researchers can employ various strategies, such as statistical control techniques, experimental designs (when feasible), and careful operationalization of variables.

Causal Inferences

Correlation does not imply causation—a fundamental principle in correlational research. While correlational studies can identify relationships between variables, they cannot determine causality. This is because correlation merely describes the degree to which two variables co-vary; it does not establish a cause-and-effect relationship between them.

For example, consider a correlational study that finds a positive relationship between the frequency of exercise and self-reported happiness. While it may be tempting to conclude that exercise causes happiness, it's equally plausible that happier individuals are more likely to exercise regularly. Without experimental manipulation and control over potential confounding variables, causal inferences cannot be made.

To strengthen causal inferences in correlational research, researchers can employ longitudinal designs, experimental methods (when ethical and feasible), and theoretical frameworks to guide their interpretations.

Sample Size and Representativeness

The size and representativeness of the sample are critical considerations in correlational research. A small or non-representative sample may limit the generalizability of findings and increase the risk of sampling bias .

For example, if a correlational study examines the relationship between socioeconomic status (SES) and educational attainment using a sample composed primarily of high-income individuals, the findings may not accurately reflect the broader population's experiences. Similarly, an undersized sample may lack the statistical power to detect meaningful correlations or relationships.

To mitigate these issues, researchers should aim for adequate sample sizes based on power analyses, employ random or stratified sampling techniques to enhance representativeness and consider the demographic characteristics of the target population when interpreting findings.

Ensure your survey delivers accurate insights by using our Sample Size Calculator . With customizable options for margin of error, confidence level, and standard deviation, you can determine the optimal sample size to ensure representative results. Make confident decisions backed by robust data.

Reliability and Validity

Ensuring the reliability and validity of measures is paramount in correlational research. Reliability refers to the consistency and stability of measurement over time, whereas validity pertains to the accuracy and appropriateness of measurement in capturing the intended constructs.

For example, suppose a correlational study utilizes self-report measures of depression and anxiety. In that case, it's essential to assess the measures' reliability (e.g., internal consistency, test-retest reliability) and validity (e.g., content validity, criterion validity) to ensure that they accurately reflect participants' mental health status.

To enhance reliability and validity in correlational research, researchers can employ established measurement scales, pilot-test instruments, use multiple measures of the same construct, and assess convergent and discriminant validity.

By addressing these considerations and limitations, researchers can enhance the robustness and credibility of their correlational studies and make more informed interpretations of their findings.

Correlational Research Examples and Applications

Correlational research is widely used across various disciplines to explore relationships between variables and gain insights into complex phenomena. We'll examine examples and applications of correlational studies, highlighting their practical significance and impact on understanding human behavior and societal trends across various industries and use cases.

Psychological Correlational Studies

In psychology, correlational studies play a crucial role in understanding various aspects of human behavior, cognition, and mental health. Researchers use correlational methods to investigate relationships between psychological variables and identify factors that may contribute to or predict specific outcomes.

For example, a psychological correlational study might examine the relationship between self-esteem and depression symptoms among adolescents. By administering self-report measures of self-esteem and depression to a sample of teenagers and calculating the correlation coefficient between the two variables, researchers can assess whether lower self-esteem is associated with higher levels of depression symptoms.

Other examples of psychological correlational studies include investigating the relationship between:

• Parenting styles and academic achievement in children
• Personality traits and job performance in the workplace
• Stress levels and coping strategies among college students

These studies provide valuable insights into the factors influencing human behavior and mental well-being, informing interventions and treatment approaches in clinical and counseling settings.

Business Correlational Studies

Correlational research is also widely utilized in the business and management fields to explore relationships between organizational variables and outcomes. By examining correlations between different factors within an organization, researchers can identify patterns and trends that may impact performance, productivity, and profitability.

For example, a business correlational study might investigate the relationship between employee satisfaction and customer loyalty in a retail setting. By surveying employees to assess their job satisfaction levels and analyzing customer feedback and purchase behavior, researchers can determine whether higher employee satisfaction is correlated with increased customer loyalty and retention.

Other examples of business correlational studies include examining the relationship between:

• Leadership styles and employee motivation
• Organizational culture and innovation
• Marketing strategies and brand perception

These studies provide valuable insights for organizations seeking to optimize their operations, improve employee engagement, and enhance customer satisfaction.

Marketing Correlational Studies

In marketing, correlational studies are instrumental in understanding consumer behavior, identifying market trends, and optimizing marketing strategies. By examining correlations between various marketing variables, researchers can uncover insights that drive effective advertising campaigns, product development, and brand management.

For example, a marketing correlational study might explore the relationship between social media engagement and brand loyalty among millennials. By collecting data on millennials' social media usage, brand interactions, and purchase behaviors, researchers can analyze whether higher levels of social media engagement correlate with increased brand loyalty and advocacy.

Another example of a marketing correlational study could focus on investigating the relationship between pricing strategies and customer satisfaction in the retail sector. By analyzing data on pricing fluctuations, customer feedback , and sales performance, researchers can assess whether pricing strategies such as discounts or promotions impact customer satisfaction and repeat purchase behavior.

Other potential areas of inquiry in marketing correlational studies include examining the relationship between:

• Product features and consumer preferences
• Advertising expenditures and brand awareness
• Online reviews and purchase intent

These studies provide valuable insights for marketers seeking to optimize their strategies, allocate resources effectively, and build strong relationships with consumers in an increasingly competitive marketplace. By leveraging correlational methods, marketers can make data-driven decisions that drive business growth and enhance customer satisfaction.

Correlational Research Ethical Considerations

Ethical considerations are paramount in all stages of the research process, including correlational studies. Researchers must adhere to ethical guidelines to ensure the rights, well-being, and privacy of participants are protected. Key ethical considerations to keep in mind include:

• Informed Consent:  Obtain informed consent from participants before collecting any data. Clearly explain the purpose of the study, the procedures involved, and any potential risks or benefits. Participants should have the right to withdraw from the study at any time without consequence.
• Confidentiality:  Safeguard the confidentiality of participants' data. Ensure that any personal or sensitive information collected during the study is kept confidential and is only accessible to authorized individuals. Use anonymization techniques when reporting findings to protect participants' privacy.
• Voluntary Participation:  Ensure that participation in the study is voluntary and not coerced. Participants should not feel pressured to take part in the study or feel that they will suffer negative consequences for declining to participate.
• Avoiding Harm:  Take measures to minimize any potential physical, psychological, or emotional harm to participants. This includes avoiding deceptive practices, providing appropriate debriefing procedures (if necessary), and offering access to support services if participants experience distress.
• Deception:  If deception is necessary for the study, it must be justified and minimized. Deception should be disclosed to participants as soon as possible after data collection, and any potential risks associated with the deception should be mitigated.
• Researcher Integrity:  Maintain integrity and honesty throughout the research process. Avoid falsifying data, manipulating results, or engaging in any other unethical practices that could compromise the integrity of the study.
• Respect for Diversity:  Respect participants' cultural, social, and individual differences. Ensure that research protocols are culturally sensitive and inclusive, and that participants from diverse backgrounds are represented and treated with respect.
• Institutional Review:  Obtain ethical approval from institutional review boards or ethics committees before commencing the study. Adhere to the guidelines and regulations set forth by the relevant governing bodies and professional organizations.

Adhering to these ethical considerations ensures that correlational research is conducted responsibly and ethically, promoting trust and integrity in the scientific community.

Correlational Research Best Practices and Tips

Conducting a successful correlational study requires careful planning, attention to detail, and adherence to best practices in research methodology. Here are some tips and best practices to help you conduct your correlational research effectively:

• Clearly Define Variables:  Clearly define the variables you are studying and operationalize them into measurable constructs. Ensure that your variables are accurately and consistently measured to avoid ambiguity and ensure reliability.
• Use Valid and Reliable Measures:  Select measurement instruments that are valid and reliable for assessing your variables of interest. Pilot test your measures to ensure clarity, comprehension, and appropriateness for your target population.
• Consider Potential Confounding Variables:  Identify and control for potential confounding variables that could influence the relationship between your variables of interest. Consider including control variables in your analysis to isolate the effects of interest.
• Ensure Adequate Sample Size:  Determine the appropriate sample size based on power analyses and considerations of statistical power. Larger sample sizes increase the reliability and generalizability of your findings.
• Random Sampling:  Whenever possible, use random sampling techniques to ensure that your sample is representative of the population you are studying. If random sampling is not feasible, carefully consider the characteristics of your sample and the extent to which findings can be generalized.
• Statistical Analysis :  Choose appropriate statistical techniques for analyzing your data, taking into account the nature of your variables and research questions. Consult with a statistician if necessary to ensure the validity and accuracy of your analyses.
• Transparent Reporting:  Transparently report your methods, procedures, and findings in accordance with best practices in research reporting. Clearly articulate your research questions, methods, results, and interpretations to facilitate reproducibility and transparency.
• Peer Review:  Seek feedback from colleagues, mentors, or peer reviewers throughout the research process. Peer review helps identify potential flaws or biases in your study design, analysis, and interpretation, improving your research's overall quality and credibility.

By following these best practices and tips, you can conduct your correlational research with rigor, integrity, and confidence, leading to valuable insights and contributions to your field.

Conclusion for Correlational Research

Correlational research serves as a powerful tool for uncovering connections between variables in the world around us. By examining the relationships between different factors, researchers can gain valuable insights into human behavior, health outcomes, market trends, and more. While correlational studies cannot establish causation on their own, they provide a crucial foundation for generating hypotheses, predicting outcomes, and informing decision-making in various fields. Understanding the principles and practices of correlational research empowers researchers to explore complex phenomena, advance scientific knowledge, and address real-world challenges. Moreover, embracing ethical considerations and best practices in correlational research ensures the integrity, validity, and reliability of study findings. By prioritizing informed consent, confidentiality, and participant well-being, researchers can conduct studies that uphold ethical standards and contribute meaningfully to the body of knowledge. Incorporating transparent reporting, peer review, and continuous learning further enhances the quality and credibility of correlational research. Ultimately, by leveraging correlational methods responsibly and ethically, researchers can unlock new insights, drive innovation, and make a positive impact on society.

How to Collect Data for Correlational Research in Minutes?

Discover the revolutionary power of Appinio , the real-time market research platform. With Appinio, conducting your own correlational research has never been easier or more exciting. Gain access to real-time consumer insights, empowering you to make data-driven decisions in minutes. Here's why Appinio stands out:

• From questions to insights in minutes:  Say goodbye to lengthy research processes. With Appinio, you can gather valuable insights swiftly, allowing you to act on them immediately.
• Intuitive platform for everyone:  No need for a PhD in research. Appinio's user-friendly interface makes it accessible to anyone, empowering you to conduct professional-grade research effortlessly.
• Extensive reach, global impact:  Define your target group from over 1200 characteristics and survey consumers in over 90 countries. With Appinio, the world is your research playground.

Get free access to the platform!

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

06.08.2024 | 32min read

Marketing Mix: The 4 Ps of Marketing & How to Use Them?

01.08.2024 | 27min read

Multistage Sampling: Definition, Guide, Examples

30.07.2024 | 37min read

VRIO Framework: Definition, Analysis, Examples

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Correlational Research

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson's  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

• Nicholas Cage and Pool Drownings  © Tyler Viegen is licensed under a  CC BY (Attribution)  license
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X  and  Y , are statistically related either because X  causes  Y, or because  Y  causes  X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Correlational Research Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

Correlation Studies in Psychology Research

Determining the relationship between two or more variables.

Verywell / Brianna Gilmartin

• Characteristics

Potential Pitfalls

Frequently asked questions.

A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables.

A correlation refers to a relationship between two variables. Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation.

There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation. Researchers can present the results using a numerical value called the correlation coefficient, a measure of the correlation strength. It can range from –1.00 (negative) to +1.00 (positive). A correlation coefficient of 0 indicates no correlation.

• Positive correlations : Both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation.
• Negative correlations : As the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation.
• No correlation : There is no relationship between the two variables. A correlation coefficient of 0 indicates no correlation.

Characteristics of a Correlational Study

Correlational studies are often used in psychology, as well as other fields like medicine. Correlational research is a preliminary way to gather information about a topic. The method is also useful if researchers are unable to perform an experiment.

Researchers use correlations to see if a relationship between two or more variables exists, but the variables themselves are not under the control of the researchers.

While correlational research can demonstrate a relationship between variables, it cannot prove that changing one variable will change another. In other words, correlational studies cannot prove cause-and-effect relationships.

When you encounter research that refers to a "link" or an "association" between two things, they are most likely talking about a correlational study.

Types of Correlational Research

There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.

Naturalistic Observation

The naturalistic observation method involves observing and recording variables of interest in a natural setting without interference or manipulation.  

Can inspire ideas for further research

Option if lab experiment not available

Variables are viewed in natural setting

Can be time-consuming and expensive

Extraneous variables can't be controlled

No scientific control of variables

Subjects might behave differently if aware of being observed

This method is well-suited to studies where researchers want to see how variables behave in their natural setting or state.   Inspiration can then be drawn from the observations to inform future avenues of research.

In some cases, it might be the only method available to researchers; for example, if lab experimentation would be precluded by access, resources, or ethics. It might be preferable to not being able to conduct research at all, but the method can be costly and usually takes a lot of time.  

Naturalistic observation presents several challenges for researchers. For one, it does not allow them to control or influence the variables in any way nor can they change any possible external variables.

However, this does not mean that researchers will get reliable data from watching the variables, or that the information they gather will be free from bias.

For example, study subjects might act differently if they know that they are being watched. The researchers might not be aware that the behavior that they are observing is not necessarily the subject's natural state (i.e., how they would act if they did not know they were being watched).

Researchers also need to be aware of their biases, which can affect the observation and interpretation of a subject's behavior.  

Surveys and questionnaires are some of the most common methods used for psychological research. The survey method involves having a  random sample  of participants complete a survey, test, or questionnaire related to the variables of interest.   Random sampling is vital to the generalizability of a survey's results.

Cheap, easy, and fast

Can collect large amounts of data in a short amount of time

Results can be affected by poor survey questions

Results can be affected by unrepresentative sample

Outcomes can be affected by participants

If researchers need to gather a large amount of data in a short period of time, a survey is likely to be the fastest, easiest, and cheapest option.  

It's also a flexible method because it lets researchers create data-gathering tools that will help ensure they get the information they need (survey responses) from all the sources they want to use (a random sample of participants taking the survey).

Survey data might be cost-efficient and easy to get, but it has its downsides. For one, the data is not always reliable—particularly if the survey questions are poorly written or the overall design or delivery is weak.   Data is also affected by specific faults, such as unrepresented or underrepresented samples .

The use of surveys relies on participants to provide useful data. Researchers need to be aware of the specific factors related to the people taking the survey that will affect its outcome.

For example, some people might struggle to understand the questions. A person might answer a particular way to try to please the researchers or to try to control how the researchers perceive them (such as trying to make themselves "look better").

Sometimes, respondents might not even realize that their answers are incorrect or misleading because of mistaken memories .

Archival Research

Many areas of psychological research benefit from analyzing studies that were conducted long ago by other researchers, as well as reviewing historical records and case studies.

For example, in an experiment known as  "The Irritable Heart ," researchers used digitalized records containing information on American Civil War veterans to learn more about post-traumatic stress disorder (PTSD).

Large amount of data

Can be less expensive

Researchers cannot change participant behavior

Can be unreliable

Information might be missing

No control over data collection methods

Using records, databases, and libraries that are publicly accessible or accessible through their institution can help researchers who might not have a lot of money to support their research efforts.

Free and low-cost resources are available to researchers at all levels through academic institutions, museums, and data repositories around the world.

Another potential benefit is that these sources often provide an enormous amount of data that was collected over a very long period of time, which can give researchers a way to view trends, relationships, and outcomes related to their research.

While the inability to change variables can be a disadvantage of some methods, it can be a benefit of archival research. That said, using historical records or information that was collected a long time ago also presents challenges. For one, important information might be missing or incomplete and some aspects of older studies might not be useful to researchers in a modern context.

A primary issue with archival research is reliability. When reviewing old research, little information might be available about who conducted the research, how a study was designed, who participated in the research, as well as how data was collected and interpreted.

Researchers can also be presented with ethical quandaries—for example, should modern researchers use data from studies that were conducted unethically or with questionable ethics?

You've probably heard the phrase, "correlation does not equal causation." This means that while correlational research can suggest that there is a relationship between two variables, it cannot prove that one variable will change another.

For example, researchers might perform a correlational study that suggests there is a relationship between academic success and a person's self-esteem. However, the study cannot show that academic success changes a person's self-esteem.

To determine why the relationship exists, researchers would need to consider and experiment with other variables, such as the subject's social relationships, cognitive abilities, personality, and socioeconomic status.

The difference between a correlational study and an experimental study involves the manipulation of variables. Researchers do not manipulate variables in a correlational study, but they do control and systematically vary the independent variables in an experimental study. Correlational studies allow researchers to detect the presence and strength of a relationship between variables, while experimental studies allow researchers to look for cause and effect relationships.

If the study involves the systematic manipulation of the levels of a variable, it is an experimental study. If researchers are measuring what is already present without actually changing the variables, then is a correlational study.

The variables in a correlational study are what the researcher measures. Once measured, researchers can then use statistical analysis to determine the existence, strength, and direction of the relationship. However, while correlational studies can say that variable X and variable Y have a relationship, it does not mean that X causes Y.

The goal of correlational research is often to look for relationships, describe these relationships, and then make predictions. Such research can also often serve as a jumping off point for future experimental research. 

Heath W. Psychology Research Methods . Cambridge University Press; 2018:134-156.

Schneider FW. Applied Social Psychology . 2nd ed. SAGE; 2012:50-53.

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research .  Nurse Researcher . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382

Carpenter S. Visualizing Psychology . 3rd ed. John Wiley & Sons; 2012:14-30.

Pizarro J, Silver RC, Prause J. Physical and mental health costs of traumatic war experiences among civil war veterans .  Arch Gen Psychiatry . 2006;63(2):193. doi:10.1001/archpsyc.63.2.193

Post SG. The echo of Nuremberg: Nazi data and ethics .  J Med Ethics . 1991;17(1):42-44. doi:10.1136/jme.17.1.42

Lau F. Chapter 12 Methods for Correlational Studies . In: Lau F, Kuziemsky C, eds. Handbook of eHealth Evaluation: An Evidence-based Approach . University of Victoria.

Akoglu H. User's guide to correlation coefficients .  Turk J Emerg Med . 2018;18(3):91-93. doi:10.1016/j.tjem.2018.08.001

Price PC. Research Methods in Psychology . California State University.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Non-Experimental Research

29 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson's  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

• Nicholas Cage and Pool Drownings  © Tyler Viegen is licensed under a  CC BY (Attribution)  license
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X  and  Y , are statistically related either because X  causes  Y, or because  Y  causes  X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• Thesis Statement Generator
• Online Summarizer
• Rewording Tool
• Topic Generator
• Essay Title Page Maker
• Conclusion Writer
• Academic Paraphraser
• Essay Writing Help
• Topic Ideas
• Writing Guides
• Useful Information

532 Correlational Research Topics in Psychology

What occurs to you while thinking of a research paper in psychology? We imagine in-depth interviews, surveys, and experiments. These research methods open up the mind of a particular person or a group of people. However, nowadays, psychology has shifted away from employing manual analysis techniques to establishing the fundamental patterns of human behavior. How so? Researchers use data analysis, statistics, and mathematical modeling. Thus, the correlational study is the most understandable and illustrative method.

This article features more than five hundred examples of correlational research topics in psychology for college students. You will learn the basics and primary purposes of this method.

• 🧠 Top 15 Psychology Topics
• 📄 Research in Psychology: the Basics
• 😞 Anxiety, Stress, Depression
• 🤤 Addiction & Eating Disorders
• ⚖️ Research Topics in Psychology
• 🚴 Sports & Health Psychology
• 💢 Violence & Sexual Abuse
• 🧑‍💼 Workplace & Gender Issues Topics
• 🧑‍🤝‍🧑 Correlational Research Topics on Gender
• 🏫 Education & Learning Topics
• 💒 Marriage, Relationships, Parenting

🖇 References

🧠 top 15 correlational research topics in psychology.

• Do our actions define our personality?
• Stress and motivation: Can they coexist?
• How does parents’ IQ level relate to their children’s IQ?
• Which behavioral patterns are the most frequent in children with ASD?
• Success in studies Vs. Career success.
• What role does self-awareness play in changing unhealthy behavior?
• How has the world pandemic changed the stress levels of the population?
• Results of early treatment of mental health.
• Does yoga contribute to stress resilience?
• Social media addiction in young people hurts their college grades.
• Archival research: How often does bullying cause suicidal thoughts?
• Do anti-discrimination education lower children’s abilities to categorize other people?
• Sexual abuse experience and depression probability.
• Are hate crimes more frequent in highly religious societies?
• A laugh can enhance cognitive skills.

📄 Correlational Research in Psychology: the Basics

A correlation study can end in three possible ways that researchers illustrate in a correlation coefficient:

• A Positive correlation happens when both variables simultaneously grow or fall. The coefficient is about +1.00.
• A negative correlation occurs when one variable grows and the other drops. The coefficient is about -1.00.
• A zero correlation happens when there is no relationship between variables. The coefficient is close to 0.

The visual representation of a correlation is called a scatter diagram or scatter chart.

There are three methods for you to perform correlational research in psychology.

We will help you find the most suitable correlational research topic in psychology. Depending on your purposes and methods, the same topics can be used as quantitative research topics.

😞 Correlational Topics on Anxiety, Stress, & Depression

• The rise of depression among college students.
• The impact of Covid-19 on anxiety and depression.
• Post-traumatic stress disorder after Covid-19.
• Recognizing depression in the early stage.
• Importance of stress management at college.
• Anxiety Disorder Studies and Therapy.
• Ways to overcome anxiety during a lockdown.
• The difference between anxiety and depression.
• Mindfulness as a tool to manage stress and anxiety.
• Anxiety effect on young adults and their academic performance.
• The Efficacy of Iron Supplementation to Reduce Vulnerability to Anxiety.
• Effects of post-traumatic stress on the body.
• Anxiety and its effects on teenagers’ self-esteem.
• The rise of social anxiety disorder after Covid-19.
• Depression in adolescents and its contribution to teenage suicides.
• Positive thinking as a treatment for depression.
• COVID-19 and Anxiety Levels Among Nursing Students.
• Prevention of anxiety and emotional burnout on campus.
• Reasons and effects of depression on young females.
• The effects of perfectionism on anxiety and stress.
• Comparison of stress rates among teenagers and adults.
• Social Anxiety Disorder in Teenagers.
• Hypnotherapy as a treatment for stress and anxiety.
• Financial stress and its impact on high school and college students.
• Ways to help someone with depression or anxiety.
• Anxiety and stress as the distractors of an athlete’s attention.
• Overweight and Mental Wellbeing Association.
• Stress as the reason for the lack of energy among teenagers.
• Ways to treat depression after sexual assault.
• Coping with stress and anxiety by yourself.
• The main causes of students’ emotional burnout.
• Musical Effects on Agitation in Dementia.
• The new sources of stress and anxiety in modern society.
• The positive effects of blogging about anxiety and depression.
• Influence of procrastination on academic performance and stress.
• The importance of mental health awareness on campus.
• Post-Traumatic Stress Disorder.
• Coping styles and mechanisms among teenagers.
• Substance use as a form of coping with stress and anxiety.
• Study-life balance as prevention for depression.
• How media creates social anxiety in younger generations?
• Depression as a growing global problem.
• Detrimental Effects of Stress.
• Types of anxiety medication and its effects on teenagers.
• The social effects of generalized anxiety on students.
• The factors that cause depression stigmatization in society.
• The stress of college students with financial debt.
• Depressive Disorder Treatment Discrepancies.
• Importance of self-care for stress management.

🤤 Correlational Research Topics on Addictions & Eating Disorders

• The impacts of social media on eating disorders.
• The relation between drug addiction and crimes among college students.
• Social media addiction among teenagers and its impact.
• The genetic risk factors in eating disorders.
• Drug Addiction and Treatment Program Evaluation.
• Hidden impacts of alcohol abuse among young adults.
• The prevention and treatment of anorexia in teenage girls.
• Stereotypes around eating disorders.
• The impact of patriarchal values on the rise of eating disorders.
• Public and Private Goods: Heroin Addiction Treatment.
• Food addiction among teenagers and possible treatment.
• The rise of marijuana consumption among young adults.
• The effects of substance abuse on mental health.
• Understanding the emotional connections teenagers have with food.
• Malnutrition Secondary to Eating Disorders.
• Health risks among teenagers dealing with eating disorders.
• Effects of video game addiction on a teenager’s mental health.
• Eating disorders from a perspective of developmental psychology.
• The influence of family and culture on teenagers with bulimia.
• Harm Reduction Addiction Treatment in Los Angeles.
• The effects of smoking cigarettes and vaping on mental health.
• Cell phone addiction as a global problem.
• Effects of parental alcoholism on teenagers.
• The analysis of anti-smoking policies in college.
• Positive Body Image and Eating Disorders.
• Effects of substance abuse in teenage depression.
• Early symptoms of binge eating disorder.
• Importance of awareness-raising classes on eating disorders among adolescents.
• Video Games: Benefits and Addiction.
• Self-injurious behavior among girls with eating disorders.
• Effects of bullying in the advancement of drug addiction.
• The connection between depression and eating disorders.
• The urge to eat in fast food restaurants among students.
• The War on Drugs: Legalization of Marijuana.
• Factors contributing to alcoholism among young adults.
• Effects of body shame on the rise of eating disorders.
• Orthorexia and its influence on young women’s self-esteem.
• The SBIRT Method for Alcohol Misuse Screening and Treatment.
• Eating disorders among female athletes.
• The main signals of substance abuse among teenagers.
• Effects of alcohol on students’ academic performance.
• Illicit Drug Use in Palm Beach County.
• Detrimental effects of pornography addiction on mental health.
• Debunking the stigma around eating disorders.
• The micro-trend of selfie addiction and its effects.
• Effects of Nicotine on Medication.
• Mobile games addiction among children and teenagers.
• Violence and addiction in video games.
• The role of the family in the treatment of eating disorders.
• The importance of halving metabolism when dealing with anorexia.

⚖️ Controversial Correlational Research Topics in Psychology

Adhd, bipolar disorder, schizophrenia.

• How can one tell the difference between a hypomanic episode and ADHD?
• What are the connections between ADHD and non-psychiatric disorders?
• Is there a comorbidity between ADHD and post-traumatic disorder?
• How do genetic factors influence the risk of ADHD?
• Treatment of Schizophrenia Spectrum Disorders.
• How do lifestyle changes affect the treatment and management of ADHD?
• Is there a relationship between cognitive behavioral therapy and ADHD response?
• In the case of ADHD, how may hyperactive and inattentive behaviors combine?
• Can autism specter disorder be intervened with ADHD?
• Bipolar Disorder: Pathology, Diagnosis, and Treatment.
• Is there a relationship between ADD-influenced impulsivity and other conduct disorders?
• What causes schizophrenia in children?
• What are the signs of schizophrenia in a child?
• How do opposing moods correlate in the case of bipolar disorder?
• Is there a relationship between gene pool and economic factors in cases of bipolar disorder?
• Children With Bipolar Disorder.
• What is the interdependence between manic episodes and outbursts of psychomotor activity?
• Is there a relationship between a patient’s mania and sleep deprivation?
• What are the connections between the patient’s social responsibilities and hypomania?
• How does bipolar disorder relate to other psychiatric conditions, such as anxiety?
• Humanistic Approach to Emotional Dysfunction.
• Dependence of a child’s development on a parent with bipolar disorder?
• How does early ADHD connect with other antisocial tendencies?
• What is the codependence between ADHD and substance abuse?
• Describe the connection between inattentiveness and reactive attachment disorder.
• Does ADHD influence the patient’s cognitive tempo?
• Treatment Plan For the Patient With Hyperactivity Disorder.
• Establish the connection between schizophrenia and social problems.
• How do environmental factors affect the risk of schizophrenia?
• How does a parent suffering from schizophrenia affect their children?
• How do such complications as poor nutrition may influence the schizophrenia diagnosis?
• Psychodiagnostics in Schizophrenia Case.
• What is the connection between age and schizophrenia?
• How do such disorders as OCD relate to schizophrenia?
• What is the connection between neurological soft signs and symptoms of schizophrenia?
• How does disorganized thinking affect social anxiety and withdrawal?
• Disease Models and Social Learning Therapy.
• Is there a correlation between hormonal cycles and the occurrence of schizophrenia?
• How does schizophrenic disorder affect IQ and neurocognition in general?
• Is there an interdependence between delusions and hallucinations in the case of schizophrenia?
• How can one compare schizophrenic symptoms among teens and adults?
• Usher Syndrome and Mental Illness Relationship.
• How is a schizophrenic disorder linked with suicidal thoughts?
• Is there a connection between social disorders and victimization practices?
• How do mind-altering substances relate to schizophrenic disorder?
• How do rapid cycling episodes relate to the bipolar disorder experience?
• Strategies for Students With ADHD.
• What is the relation between lithium intake and suicide rate reduction among those with bipolar disorder?
• How do deaths from natural causes connect with bipolar disorder?
• In which way can family-focused therapy influence the treatment of manic depression?

Racism, Discrimination, Hate Crimes

• How does racism affect mental health?
• Racism in the 20th and 21st century: the difference.
• How does racism work in world medicine?
• Racism in the USA and South Africa: fundamental contrasts.
• Racial Discrimination and Educational Gap.
• How does social experience affect racial bias?
• Combating racism and the oppression of whites: the correlation.
• How does the brain deal with racism?
• Does anti-racism lead to a split in society?
• Modern racism versus past racism: similarities and differences.
• The Myth of Multiculturalism in Canada.
• How do social networks influence the fight against racism?
• What significant similarities do racial and gender biases have?
• How does racism affect the quality of life?
• The difference between internalized racism and interpersonal racism.
• Racial Happiness and Anti-Racism.
• Men and Women: wage gap in the world.
• What is the contradiction between discrimination and intolerance?
• Women and men: inequality in the labor market.
• How does the digital economy affect gender inequality?
• Mental Illness in Black Community in South Africa.
• Young workers or age-related: who faces discrimination the most?
• Employment without discrimination: myth or reality?
• The contrast between direct and indirect discrimination.
• Comparison of age discrimination with gender discrimination.
• Healthcare Disparities for African Americans.
• “Black Lives Matter” and “MeToo:” Interrelation.
• The discrepancy between traditional male and female roles.
• Is the level of discrimination in the world growing or falling?
• People’s reactions to sexist and racist jokes.
• Discrimination Against Girls in Canada.
• What is the contradiction between same-sex and traditional marriages?
• Correlation between LGBT and Hate Crimes.
• How does racism affect Hate Crimes?
• Hate Crimes in the West and East: the ratio.
• The Black Lives Matter Movement: Aims and Outcomes.
• How has the COVID-19 pandemic affected Hate Crimes?
• What are the main factors affecting the increase in Hate Crimes?
• The influence of the media on prejudice against social minorities.
• Xenophobia and hate crimes: the connection.
• Racial Inequality, Poverty, and Gentrification in Durham, North Carolina.
• How do US laws affect the situation with hate crimes?
• Upbringing vs. social environment: a more significant influence on racism.
• On what grounds do Hate Crimes occur more often: religion or race?
• Do political or social changes have the most impact on Hate Crimes?
• Asking About Sexual Orientation of Patients in Healthcare.
• The impact of political intimidation on the Hate Crimes situation.
• Terrorism and Hate Crimes: similarities.
• The history of changing women’s inequality.

🚴 Sports & Health Psychology Correlational Topics

• A coach and a psychologist: Whose help is more effective?
• Can a coach replace a psychologist?
• Features of children-athletes of preschool and school age.
• The influence of sports on the manifestation of aggression.
• The Impact of Human Resources Management on Healthcare Quality.
• Why is the cooperation of a coach and a psychologist ineffective?
• What is the difference between fear and anxiety in sports activities?
• How to help an athlete cope with pre-start apathy?
• How to set up an athlete for the next performance after a defeat?
• Patient, Family, or Population Health Problem Solution.
• Is friendship possible in sports?
• Can sports help to cope with aggression?
• Common signs of a successful and unsuccessful athlete.
• Satisfaction with the process or result in sports: which is better?
• Why People Exercise.
• Correlation between loss and acquisition of personal resources in sports.
• Is it helpful to fear in sports or not?
• External and internal expression of fear: difference.
• Crises of competition and training process: interrelation.
• Exercising at Home vs. Exercising at the Gym.
• The ratio of life expectancy in the 21st and 20th centuries.
• Are cancer or heart disease the most common causes of death in the United States?
• Does psychological death affect biological death?
• The prevalence of hospices in the United States and Britain.
• Healthcare Types Accessible to Any Individual.
• The impact of palliative care on a patient’s perception of death.
• Is sports motivation or team building the best strategy for solving problems in sports?
• Does moderate daily exercise make a difference in the hygiene of old age?
• Is relaxation the central link of autogenic training?
• Advantages of Physical Exercise for Good Health.
• Physical fitness – an external manifestation of the level of physical activity?
• The relationship between mental and physiological stress in sports.
• Does sport have a significant influence on personality formation?
• Team and individual sports: interrelation and difference.
• Common Health Traditions of Cultural Heritage.
• Sports and physical education: what is the most significant influence on the psychological state of a person?
• Do professional athletes experience fears?
• An Individual’s Passion: The Ideas of Mental Health Care.
• At what age is it advisable for an athlete to start working with a psychologist?
• How does sport ensure the mental health of young athletes?
• How to set up a child for the training process?
• What are the features of the manifestation of fear in sports?
• Health Care Coverage in the USA.
• How to combine academic activities at school and training?
• How to form an interest in sports among children of 8-10 years old?
• What should a coach do to prevent emotional burnout?
• How to develop attention in athletes?
• How to form moral behavior in an athlete?

💢 Correlational Research Topics on Violence & Sexual Abuse

• Is violence an attempt to gain power over others?
• Prevalence of sexual abuse among women and girls.
• Sexual contact without consent and date rape: similarities.
• The ratio of rape among heterosexuals and bisexuals.
• Victimology: Definition of the Concept.
• Physical or mental violence: relationships.
• Do external factors provoke impulsive aggression?
• Are employees often silent or report harassment to management?
• The proper reaction to aggression: showing calmness or rebuffing?
• Nursing Practice and Violence Reporting.
• Micro and Macro causes of violence: correlation.
• Do victims of sexual criminals trust the legal system?
• Sexual assaults on whites and blacks: degree of punishment.
• Is childhood sexual abuse commonplace in the US?
• Domestic Violence and COVID-19 Relation.
• Correlation between PTSD and depression in sexual violence survivors.
• Sexual dysfunction and fertility problems: Connections.
• Are psychological or neurological processes most underlying aggression?
• Does sexual violence cause addiction?
• Gender-Based Violence Against Women and Girls.
• Does the lack of empathy affect aggression manifestation?
• Relationship between cases of elimination and limitation of violence.
• Correlation between physical punishment and sexual abuse of children.
• Violence against children by the States and the USA: scales.
• Violence in Mass Communication and Behavior.
• The root causes of violence against women and men.
• Is envy often the cause of bullying in collectives?
• Degree of the relationship between violence and force.
• Cases of violence before COVID-19 and after.
• Media Violence Effect and Desensitization of Children.
• Violence against women and gender inequality: relationships.
• Are the vast majority of abusers men?
• Can psychological violence turn into physical violence?
• Is psychological violence more common in the family or at work?
• Are abusers depressed people?
• Child Abuse in the United States.
• Are all people capable of violence, or only some?
• What is the probability that someone who committed violence will do it again?
• The boundaries between permissible self-defense and crime.
• Consequences of violence against women and girls.
• Sexual violence and sexual harassment: differences.
• Health Determinants Among Sexually Active High-Risk Adolescents.
• Do strangers commit most sexual assaults?
• Is sexual violence a crime of passion?
• Is the victim of the crime irreversibly damaged?
• The relationship between shared feelings and the consequences experienced by survivors.
• Sexual violence against men and women: the ratio of crimes.
• Sexual violence among primary school children and adolescents: connections.

🧑‍💼 Correlational Research Topics on Workplace Psychology

• Interpersonal relationships and dynamics of working culture: Impacts.
• The relation between industrial and organizational psychology.
• Content and process theories of motivation: the ratio.
• Is money the only motivating factor in work?
• Material or non-material motivation: which is better?
• The Effects of Workplace Conflict on Nurses’ Working Environment.
• Is Maslow’s theory relevant in the workplace?
• Are theories of motivation based on employee needs concepts?
• Are individual needs the primary motivator of leaders’ ideas?
• Distinctive characteristics of moral and material encouragement.
• When Work Is Punishment?
• Do the majority of managers have leadership qualities?
• Liberal and democratic leadership style: similarities.
• Does the democratic style determine subordinates’ professional growth?
• Advantages of combining leadership and management skills.
• Workplace Incivility in Healthcare Facilities.
• What is the relationship between power and leadership?
• The working environment and staff turnover: interrelations.
• Does absenteeism come from a decrease in labor efficiency?
• The dismissal threat and the loyalty: degree of influence.
• Promoting a Healthy Work Environment.
• Is low labor efficiency associated with criticism of results?
• Do timing techniques help to identify standard time sinks?
• The motive power and urgency of the needs: connections.
• The organization of adequate rest and concentration: no correlation.
• Professional Burnout of Medical Workers in Ghana.
• Principle of materialization and a helpful review of tasks: Relationships.
• Chronophages then and today: comparison and difference.
• Importance and urgency: comparison of task evaluation criteria.
• The success of professional activity depends on the psyche’s mood.
• Work-Life Balance and Workplace Stress Management.
• Intellectual biorhythm and changes in professional abilities: Interrelations.
• Does self-management require self-awareness?
• Self-management determines the ability to cope with stress.
• Balance of effort and results in modern realities.
• Gender-Based Discrimination in the Workplace.
• Does perfectionism limit a specialist?

🧑‍🤝‍🧑 Correlational Research Topics in Psychology of Gender

• The relationship between gender and technology.
• What is the impact of sex differences on cognitive functions?
• How does gender influence dreams and aspirations?
• What effect does gender identity have on socialization?
• Gender Roles in the Context of Religion.
• How do gender stereotypes affect people’s perception of their gender?
• The relationship between sex and gender.
• What is the impact of gender identity on children’s mental well-being?
• Sex differences and experiences of pain.
• Gender Discrimination in Nursing.
• Gender roles and the battlefield.
• Gender identity concerning the idea of woman.
• How do gender differences influence the style of parenting?
• Sex differences about substance use disorders.
• Gender Discrimination after the Reemergence of the Taliban in Afghanistan.
• How does gender analysis affect science?
• How do gender differences impact self-esteem?
• The relationship between gender identity and linguistic style.
• How does gender influence job satisfaction?
• Gender Equality at the Heart of Development.
• How do sex differences impact depression?
• Gender influences on adolescent development.
• The influence of adults’ gender stereotypes on children.
• How do sex differences in school relate to gender identity?
• The relationship between gender and emotion.
• Gender differences in terms of negotiation outcomes.
• The relationship between gender identity and coping strategies.
• Sex differences and attitudes towards love.
• Social Change and the Environment.
• The importance of gender in personality psychology.
• What is the effect of sex differences on leadership qualities?
• Sex differences in mortality rates.
• Gender Stereotypes of Superheroes.
• How do gender stereotypes impact school performance?
• The impact of sex differences on influence tactics.
• How does sex impact the immune response?

🏫 Education, Learning, Memory Correlational Research Topics

• How might memory limitations hamper learning opportunities?
• What is the correlation between low education and memory decline?
• Working memory’s role in childhood education.
• The relationship between brain activity and education.
• Comparing Human Memory to the Working of a Computer.
• The effects of collaboration on learning outcomes.
• The effects of education on immediate memory.
• The improvement of memory functions through education programs.
• How does the teacher-student relationship affect student engagement?
• Amnesic: Symptoms and Treatment.
• Cognitively normal people and their memory functioning.
• Individual differences and their role in learning.
• How are rates of memory decline connected to education?
• The effects of problem-based learning on medical education.
• The Significance of Ethics and Ethical Education in Daily Life.
• Children with visual impairments and their ways of learning.
• The relationship between students’ temperament and learning behaviors.
• How does the modern-age education system affect memory?
• Problem behaviors and the role of students’ gender differences.
• Transformational Leadership in Nursing Education.
• The speed of information processing and its effects on memory.
• The relationship between language and semantic memory.
• The practices of memory in history education.
• Episodic memory in contemporary educational psychology.
• Patient Education and Healthcare Professionals’ Role.
• How does retrieval contribute to learning?
• The correlation between active learning and enhanced memory.
• People with learning disabilities and memory difficulties.
• The impact of emotion on learning.
• Health Education and Promotion in Community.
• Short-term memory’s role in language learning.
• The relationship between personality characteristics and learning outcomes.
• How does drug abuse impact memory?
• The function of gesture concerning learning.
• Customized Education Effect on Readmissions of Patients with Congestive Heart Failure.
• How can music improve memory?
• The role of relationships in students’ academic success.
• The implications of learning under stress for educational settings.
• The pandemic’s impact on academic motivation.
• Mathematical Skills in Early Childhood Education.
• Digital storytelling and its effects on visual memory.
• Working memory in the context of science education.
• Working memory and learning difficulties.
• The effects of bilingualism on memory.
• Online Social Networks in Education.
• Sensory learning styles and their impact on learning.
• How did the COVID-19 crisis affect doctoral students?
• What effect do teachers’ beliefs and values have on their teaching?
• The relationship between problem-solving and motivation to learn.
• Technology in Education: Mixed-Methods Design.

💒 Marriage & Relationships Correlational Topics

• The impact of previous relationships on marriage.
• How do romantic relationships affect health?
• The connection between sexual satisfaction and marital satisfaction.
• The correlation between individual strength and relationship satisfaction.
• Same-Sex Marriage Research Paper.
• Same-sex marriage and people’s understanding of same-sex relationships.
• The relationship between gender role attitudes and marriage expectations.
• How does marriage change people’s relationships with their parents?
• Relationship education and a couple’s communication skills.
• Same-Sex Marriages Justification and Human Rights.
• The relationship between marriage and drug use.
• The impact of rape in marriage on mental health.
• Emotional intelligence and marital satisfaction.
• Gender role attitudes and division of household labor in marriage.
• Millennials Say Marriage Ideal but Parenthood the Priority.
• How does a transition from cohabitation to marriage affect a relationship?
• The impact of religion on marriage.
• A spouse’s gender and their assessment of marital satisfaction.
• The correlation between wedding spending and marriage duration.
• Gay Marriage: Disputes and the Ethical Dilemma.
• The effects of sexual timing on relationships
• How does a marriage influence ego development?
• The lack of argumentative skills and marriage quality.
• The connection between marriage and individualism.
• Marriage and Family in America.
• What is the effect of economic factors on relationship quality?
• How is marriage strength affected by relationship enrichment programs?
• The relationship between child marriage and domestic violence.
• Neuroticism and marital satisfaction.
• Relationships: Importance and Impacts.
• Household income and relationship satisfaction.
• The relationship between a wife’s employment and marriage quality.
• What is the correlation between relationship quality and sleep quality?
• The impact fathers have on their daughters’ romantic relationships.
• How does forgiveness in marriage affect relationship quality?
• The Family Discipline Issues.
• The number of marriageable men and nuptiality.
• The relationship between a healthy marriage and health outcomes.
• How does marriage change people’s views on relationships?
• The relationship between work stress and marriage quality.
• The effect of personality on marriage.
• Work-Family Conflict and Its Impacts on Parties.
• People’s childhood experiences and their marriage quality.
• How does a marriage relationship change during a couple’s transition to parenthood?
• Adult children of divorce and their view on marriage.
• What is the effect of children on a marriage relationship?
• The Golden Era of the American Family.
• Stigma experiences and relationship quality in same-sex couples.
• The relationship between losing parents and marrying early.

Correlational Research Topics on Parenting Psychology

• Relations between child development stages theories and styles of parenting?
• What is the impact of media on children’s and adolescents’ behavior?
• How are children’s mental capacities affected by the roles of school teachers?
• Is there a relation between baby sign language and further linguistic cognition?
• Single Mothers, Poverty, and Mental Health Issues.
• How to reduce the risk of a child developing schizophrenia?
• Can bad parenting be a cause of mental illnesses in a child?
• How does puberty affect a teenager’s social behavior?
• What is the relation between spanking a child and children’s violent behavior?
• What is the relation between the gender of a single parent and parenting style?
• How does single male parenting affect a male child?
• How does female single parenting affect a male child?
• How does female single parenting affect a female child?
• Human Development in Childhood.
• Dynamics between adopted children and gay parents?
• What are the disciplinary strategies of authoritarian parenting?
• What is the relation between parenting style and the socialization of children?
• What is the relation between authoritarian and authoritative parenting styles?
• Childhood Obesity as an Urgent Problem of Epidemiology.
• How does authoritarian parenting affect the emergence of obsessive-compulsive syndrome in children in adulthood?
• In which way does authoritarian parenting affect children’s self-esteem?
• What is the connection between children’s obedience and level of happiness?
• The connection between culture and parenting style: Transfer of values.
• Malnutrition in Children Under Five Years of Age.
• What two parental dimensions form the basis for the parenting styles?
• What are parent/child separation effects on children’s emotional stability?
• How can a lack of care in a family affect a child’s psyche?
• How may the phenomenon of learned helplessness manifest itself in a child’s development?
• Benefits of Sex Education for Teenagers.
• How does the malicious parent syndrome influence a child’s morale and cognition?
• How does the self-centered behavior of a parent reflect in a child’s development?
• Is there a correlation between a parent’s unresolved trauma and emotional abuse in parenting?
• How does gaslighting relate to children’s self-esteem?
• Teen Pregnancy and Self Awareness in Mississippi.
• How does emotional development relate to cognitive skills?
• Are social and language development mutually connected?
• What are the signals of a child being emotionally manipulated?
• Can a parent’s behavior lead a child to be emotionally manipulative?
• Teenage Pregnancy Research Paper.
• How does an uninvolved style of parenting affect children’s development?
• Can parental abuse cause a child to have a social disorder?
• Name the connections between an infant’s and a toddler’s stages of development.
• How does child development relate to increasing autonomy?
• Why Should Children Have a Pet During Childhood?
• How does the behavioral model of development relate to Freudian theories?
• Can cognitive advancement lead to asynchronous development?
• How are mood swings connected to emotional development?
• Can cognitive development influence egotistic tendencies in a child?
• Children’s Psychological Qualitative Research Methodology.
• How does the attachment theory relate to a child’s social development?
• Quantitative Psychology Designs Research Methods to Test Complex Issues – American Psychological Association
• What Is a Correlational Study? – Verywell Mind
• Correlation Definitions, Examples & Interpretation – SymplyPsyhology
• Stress relief is within reach – American Psychological Association
• Anxiety Disorders – National Institut of Mental Health
• A Parent’s Role – Psychology Today
• What Is Educational Psychology? – Verywell Mind
• APS Backgrounder Series: Psychological Science and COVID-19: Pandemic Effects on Marriage and Relationships – Association for Psychological Science
• Psychology in the Workplace – Boss Foundation
• I/O Psychology Provides Workplace Solutions – American Psychological Association

Top 150+ Correlational Research Topics For Students [2024]

Correlational research looks at how two or more things relate without saying one causes the other. It tries to find patterns and connections between different things to see how changes in one might be connected to changes in another.

In education, correlational studies are super important because they help us understand how different factors affect how well students learn. Whether looking at teaching methods or considering students’ backgrounds, correlational research helps teachers determine how to help students do better in school.

Our blog is here to give students interesting correlational research topics. We want to make it easy for students to find ideas and get excited about doing research. 

We aim to get you thinking and curious about how things are connected so you can learn more about them.

What is Correlation? An Introduction

Table of Contents

Correlation is defined as how two variables change simultaneously. It helps us comprehend their relationship. 

When two variables are correlated, changes in one tend to be associated with changes in the other, but it doesn’t necessarily mean that one causes the other. 

Correlation can be positive, meaning both variables move in the same direction, or negative, where they move in opposite directions. 

Understanding correlation is crucial in various fields like science, economics, and social sciences, as it allows us to identify patterns, make predictions, and better comprehend the complexities of the world around us.

Also Read: “ Top 151+ Quantitative Research Topics for ABM Students “.

Benefits of Correlational Research Topics For Students

Correlational research topics offer numerous benefits for students, allowing them to explore relationships between variables and understand the complexity of real-world phenomena. Here are several benefits of correlational research topics for students:

Enhances critical thinking skills

Engaging in correlational research encourages students to analyze data, draw conclusions, and evaluate the relationships between variables, fostering critical thinking abilities.

Provides real-world application

Correlational research topics often relate to everyday phenomena, allowing students to apply theoretical concepts to practical situations promoting a deeper understanding of the subject matter.

Fosters research skills

Conducting correlational studies equips students with valuable research skills, including data collection, analysis, and interpretation, essential for academic and professional success.

Stimulates curiosity and creativity

Exploring correlational research topics ignites curiosity and creativity, inspiring students to explore new ideas, generate hypotheses, and develop innovative solutions to complex problems.

Prepares for future academic pursuits

Engaging in correlational research prepares students for future academic endeavors by honing their research abilities and preparing them for more advanced research projects at higher levels of education.

List of Interesting Correlational Research Topics For Students

Here’s a list of interesting correlational research topics for students across various disciplines:

• The correlation between teacher enthusiasm and student engagement.
• The relationship between parental involvement and student academic performance.
• Correlating study habits with GPA in high school students.
• The impact of class size on student achievement.
• Relationship between technology use and learning outcomes.
• Correlation between sleep quality and academic success in college students.
• The correlation between extracurricular activity and academic achievement.
• Correlation between self-esteem and academic achievement.
• The influence of school climate on student behavior and achievement.
• Relationship between student-teacher rapport and academic success.

Health and Wellness

• Correlation between exercise frequency and mental health.
• Relationship between diet and stress levels in college students.
• The impact of social support on overall health.
• Correlating screen time with sleep quality in adolescents.
• The relationship between mindfulness practices and emotional well-being.
• Correlation between access to green spaces and physical activity levels.
• The influence of peer pressure on health-related behaviors.
• Relationship between music preference and stress reduction.
• The correlation between pet ownership and mental health.
• The relationship between outdoor recreation and overall wellness.

Social Sciences

• Correlation between socioeconomic status and academic achievement.
• The link between social media usage and self-esteem.
• The impact of family structure on social behavior.
• Correlation between political ideology and charitable giving.
• Relationship between cultural background and communication styles.
• The influence of peer group on academic motivation.
• Correlation between media consumption and attitudes towards diversity.
• Relationship between personality traits and career success.
• The impact of community involvement on civic engagement.
• Correlation between volunteering and life satisfaction.

Technology and Society

• The relationship between smartphone use and attention span.
• Correlation between video game usage and problem-solving skills.
• The influence of social media on interpersonal relationships.
• Relationship between Internet usage and academic performance.
• Correlation between online shopping habits and financial literacy.
• The impact of digital literacy on job opportunities.
• Relationship between virtual reality exposure and empathy levels.
• Correlation between social networking and political engagement.
• The relationship between technology use and environmental awareness.
• Correlation between online activism and real-world action.

Economics and Finance

• The relationship between household income and savings behavior.
• Correlation between education level and earning potential.
• The impact of inflation on consumer spending habits.
• Relationship between stock market performance and consumer confidence.
• Correlation between financial literacy and debt management.
• The influence of advertising on consumer purchasing decisions.
• Relationship between economic growth and unemployment rates.
• Correlation between housing prices and neighborhood demographics.
• The relationship between government spending and economic growth.
• Correlation between education funding and student outcomes.

Environmental Studies

• The relationship between air pollution and respiratory health.
• Correlation between waste management practices and environmental sustainability.
• The impact of deforestation on biodiversity.
• Relationship between climate change awareness and pro-environmental behaviors.
• Correlation between water quality and public health.
• The influence of renewable energy adoption on greenhouse gas emissions.
• Relationship between urbanization and wildlife habitat loss.
• Correlation between environmental regulations and industry practices.
• The relationship between sustainable agriculture and food security.
• Correlation between green infrastructure and urban heat island effect.
• The link between childhood trauma and adult mental health.
• Correlation between personality type and career choice.
• The effects of early attachment types on romantic relationships.
• Relationship between parental discipline strategies and child behavior.
• Correlation between introversion/extroversion and social networking.
• The effect of peer pressure on risk-taking behavior.
• The link between body image and social media use.
• Correlation between anxiety levels and academic performance.
• The relationship between self-esteem and relationship satisfaction.
• Correlation between happiness levels and gratitude practices.

Criminal Justice

• The association between childhood trauma and adult mental health.
• Correlation between access to education and recidivism rates.
• The impact of community policing on crime prevention.
• Relationship between substance abuse and criminal behavior.
• Correlation between gun control laws and violent crime rates.
• The influence of media portrayal on perceptions of crime.
• Relationship between juvenile delinquency and family dynamics.
• Correlation between sentencing disparities and race.
• The relationship between policing tactics and public trust.
• Correlation between restorative justice programs and rehabilitation rates.

Business and Management

• The relationship between employee satisfaction and productivity.
• Correlation between leadership style and team performance.
• The impact of workplace diversity on organizational success.
• The link between staff training programs and work happiness.
• Correlation between customer satisfaction and repeat business.
• The impact of company culture on employee turnover.
• Relationship between ethical business practices and consumer trust.
• Correlation between innovation and market competitiveness.
• The relationship between employee engagement and company profitability.
• Correlation between marketing strategies and brand loyalty.

Media and Communication

• The link between media consumption and political polarization.
• Correlation between advertising exposure and consumer behavior.
• The influence of media depiction on body image.
• Relationship between news consumption and knowledge of current events.
• Correlation between social media usage and interpersonal communication skills.
• The influence of celebrity endorsements on brand perception.
• Relationship between media violence exposure and aggression levels.
• Correlation between news bias and public opinion.
• The link between media literacy and critical thinking abilities.
• Correlation between reality television consumption and social attitudes.

Culture and Society

• The relationship between cultural diversity and creativity.
• Correlation between cultural heritage preservation and community identity.
• The impact of globalization on cultural values.
• Relationship between language diversity and social cohesion.
• Correlation between cultural norms and attitudes towards gender roles.
• Communication styles are influenced by cultural background.
• Relationship between cultural assimilation and mental health.
• Correlation between cultural festivals and community bonding.
• The relationship between cultural stereotypes and prejudice.
• Correlation between cultural adaptation and immigrant integration.

Sports and Recreation

• The relationship between sports participation and academic achievement.
• Correlation between exercise frequency and stress reduction.
• The impact of sports team success on school spirit.
• Relationship between youth sports involvement and leadership skills.
• Correlation between sports fandom and social connections.
• The influence of sports participation on self-esteem.
• Relationship between sportsmanship and moral development.
• Correlation between coaching style and athlete motivation.
• The relationship between sports injuries and long-term health outcomes.
• Correlation between sports specialization and athletic performance.

Science and Technology

• The relationship between science education and technological innovation.
• Correlation between technology use and environmental impact.
• The impact of science literacy on public policy attitudes.
• Relationship between STEM education and career opportunities.
• Correlation between scientific research funding and breakthrough discoveries.
• The influence of technology on scientific research methodologies.
• Relationship between science communication and public understanding.
• Correlation between technological advancements and quality of life.
• The relationship between science engagement and environmental conservation efforts.
• Correlation between technology adoption and societal changes.

Language and Linguistics

• The relationship between bilingualism and cognitive development.
• Correlation between language proficiency and academic success.
• The impact of language diversity on social integration.
• Relationship between language acquisition and brain development.
• Correlation between language use and cultural preservation.
• The influence of language barriers on access to healthcare.
• Relationship between language learning strategies and proficiency levels.
• Correlation between language policies and educational outcomes.
• The relationship between language evolution and societal change.
• Correlation between language dialects and regional identities.

Travel and Tourism

• The relationship between travel experiences and cultural awareness.
• Correlation between tourism development and economic growth.
• The impact of travel restrictions on tourism industries.
• Relationship between destination marketing and tourist arrivals.
• Correlation between travel preferences and personality traits.
• The influence of travel experiences on personal growth.
• Relationship between travel safety perceptions and tourist behavior.
• Correlation between travel motivations and destination choices.
• The relationship between travel blogging and destination popularity.
• Correlation between travel trends and environmental sustainability.
• The relationship between public transportation accessibility and urban development .

These topics offer students various possibilities for conducting correlational research across various domains, allowing them to explore meaningful relationships between different variables and contribute to existing knowledge.

Tips for Conducting Correlational Research

Conducting correlational research requires careful planning, attention to detail, and adherence to established research methodologies . Here are some tips to help students conduct correlational research effectively:

1. Clearly define variables

Identify the variables you want to study and ensure they are measurable and relevant to your research question.

2. Choose appropriate measures

Select reliable and valid measures for each variable to capture the data accurately.

3. Collect sufficient data

Ensure your sample size is large enough to detect meaningful correlations and consider diverse populations if applicable.

4. Use appropriate statistical analysis

Employ statistical techniques like the Pearson correlation coefficient to analyze the relationship between variables.

5. Consider potential confounding variables

Be aware of other factors that may influence the correlation and control for them if possible.

6. Interpret results cautiously

Remember that correlation does not imply causation; consider alternative explanations for observed relationships.

7. Communicate findings effectively

Present your results clearly and accurately, including any limitations or caveats in your interpretations.

Correlational research topics offer invaluable insights into the intricate relationships between variables across diverse fields. 

Researchers can uncover patterns, make predictions, and deepen our understanding of complex phenomena by exploring correlations. While correlational studies do not establish causation, they provide a foundational framework for further investigation and practical applications.

Through meticulous analysis and interpretation, correlational research contributes to advancements in education, health, social sciences, and beyond. 

As we continue to explore the interconnectedness of variables, correlational research remains a powerful tool for unraveling the mysteries of the world around us and driving progress in various fields.

What is the difference between correlational research and experimental research?

Correlational research examines the relationship between variables without manipulating them, while experimental research involves manipulating variables to determine cause-and-effect relationships. Experimental research allows for stronger causal inferences compared to correlational research.

What are some strengths and weaknesses of correlational research? 

Strengths include being relatively inexpensive and efficient and avoiding manipulation, which might be unethical. Weaknesses include not establishing causality and being susceptible to confounding variables.

Can correlational research establish causation between variables?

No, correlational research cannot establish causation between variables. While it can identify relationships and associations, it does not manipulate variables to determine cause-and-effect, making it unable to establish causal relationships definitively.

What are some common pitfalls to avoid when conducting correlational research?

Common pitfalls in correlational research include mistaking correlation for causation, failing to control for confounding variables, relying on small or biased samples, and neglecting to consider the directionality or third-variable explanations for observed correlations.

Related Posts

8 easiest programming language to learn for beginners.

There are so many programming languages you can learn. But if you’re looking to start with something easier. We bring to you a list of…

10 Online Tutoring Help Benefits

Do you need a computer science assignment help? Get the best quality assignment help from computer science tutors at affordable prices. They always presented to help…

Correlational Research: Methods and Examples

Correlational research is a type of research design commonly used in the social and behavioral sciences. It measures the relationship…

Correlational research is a type of research design commonly used in the social and behavioral sciences. It measures the relationship between two or more variables.

Researchers using correlational research design typically look at associations or correlations in data without establishing that one event causes another. To statistically analyze correlational data, researchers must control variables that may affect the relationships found in the data.

Let’s take a closer look at the correlational method .

What Is Correlational Research?

How is correlational research conducted, examples of correlational research, what to watch out for in correlational research design.

Different research techniques have different uses. Here are some features of correlational study :

• Correlational research is often used in observational studies. This means researchers gather information about an event and attempt to correlate it with other variables (also called independent or dependent variables) that they cannot control.
• Researchers use the correlational method because, unlike experimental design, correlational research doesn’t control for individual differences and other factors.
• This also means the results of the correlational method may not be as reliable as those of experimental studies. Analyzing the data can be challenging. Researchers must use statistical tests to determine whether observed relationships are statistically significant.
• Correlational research doesn’t always provide evidence that one factor causes another. They’re correlational, not causal. It can, however, provide information about relationships between variables.

There are specific situations where a correlational study can be a useful tool. Now that we know what is correlational research, let’s look at how it’s done.

In correlational research , the most important part of the design process is to identify the variables. Here’s how:

• Researchers get ready for data collection. They might create a nomogram where they can plot all the variables. A nomogram is a grid with rows and columns. The rows represent variables, while the columns represent observations.
• Researchers collect their data. Once collected, they use a second nomogram to help them place their observations. This is called plotting in the correlational method .
• The data is sorted and researchers look for patterns. Then they enter the data into the nomogram according to those patterns.
• Researchers choose additional variables that will help them identify the relationship between the dependent and independent variables.
• Researchers can collect data from different sources to compare their findings.

Such considerations must be incorporated in all types of correlational research design .

Scientists might want to see if people working in the public sector are less likely to take their car for repairs than those who work in the private sector. If they identify this variable, they’ll need to use an appropriate nomogram to determine which variables represent it. They‘d first classify variables into two categories: public employees and private employees. Next, they’d plot data on an appropriate nomogram that shows how many observations each category represents. That’s one of the examples of correlational research .

Here are some further considerations for effective correlational research design:

• Proper sampling is essential to the validity of any study. It’s important that each observation represent the entire sample. Researchers can use random sampling or stratified sampling in the correlational method.
• Random sampling involves choosing subjects at random. If there are eight cases in an experiment and 12 people to choose from, for example, a coin flip can decide which two people each observation will be based on. This helps ensure that each observation is represented equally in a correlational research.
• Stratified sampling allows researchers to choose subjects based on specific characteristics, such as gender or race in correlational research. This helps ensure that each case represents the population.
• It’s also important that the sample represents the population. Race, gender, age, social class and other factors all affect results. Researchers can correct for these biases by using appropriate sampling techniques.
• In all types of correlational research design , the sample should be large enough that there are no extreme outliers or isolated points in the data.

Like any research project, a correlational study requires careful planning and management. These are skills that Harappa’s Thinking Critically course provides. It’s ideal for professionals at any level of their careers. This self-paced course equips the next generation of leaders with frameworks and concepts to make well-thought-out decisions for the improvement of the organization. Get the Harappa advantage today!

Explore Harappa Diaries to learn more about topics such as Examples Of Research Objectives , Conditioning Theory , The Straw Man Fallacy and How To Improve Critical Thinking to upgrade your knowledge and skills.

• Basics of Research Process
• Methodology

Correlational Study: Design, Methods and Examples

• Speech Topics
• Basics of Essay Writing
• Essay Topics
• Other Essays
• Main Academic Essays
• Research Paper Topics
• Basics of Research Paper Writing
• Miscellaneous
• Chicago/ Turabian
• Data & Statistics
• Admission Writing Tips
• Admission Advice
• Other Guides
• Student Life
• Studying Tips
• Understanding Plagiarism
• Academic Writing Tips
• Basics of Dissertation & Thesis Writing

• Essay Guides
• Research Paper Guides
• Formatting Guides
• Admission Guides
• Dissertation & Thesis Guides

Table of contents

Use our free Readability checker

Correlational research is a type of research design used to examine the relationship between two or more variables. In correlational research, researchers measure the extent to which two or more variables are related, without manipulating or controlling any of the variables.

Whether you are a beginner or an experienced researcher, chances are you’ve heard something about correlational research. It’s time that you learn more about this type of study more in-depth, since you will be using it a lot.

• What is correlation?
• When to use it?
• How is it different from experimental studies?
• What data collection method will work?

Grab your pen and get ready to jot down some notes as our paper writing service is going to cover all questions you may have about this type of study. Let’s get down to business! 

What Is Correlational Research: Definition

A correlational research is a preliminary type of study used to explore the connection between two variables. In this type of research, you won’t interfere with the variables. Instead of manipulating or adjusting them, researchers focus more on observation.  Correlational study is a perfect option if you want to figure out if there is any link between variables. You will conduct it in 2 cases:

• When you want to test a theory about non-causal connection. For example, you may want to know whether drinking hot water boosts the immune system. In this case, you expect that vitamins, healthy lifestyle and regular exercise are those factors that have a real positive impact. However, this doesn’t mean that drinking hot water isn’t associated with the immune system. So measuring this relationship will be really useful.
• When you want to investigate a causal link. You want to study whether using aerosol products leads to ozone depletion. You don’t have enough expenses for conducting complex research. Besides, you can’t control how often people use aerosols. In this case, you will opt for a correlational study.

Correlational Study: Purpose

Correlational research is most useful for purposes of observation and prediction. Researcher's goal is to observe and measure variables to determine if any relationship exists. In case there is some association, researchers assess how strong it is. As an initial type of research, this method allows you to test and write the hypotheses. Correlational study doesn’t require much time and is rather cheap.

Correlational Research Design

Correlational research designs are often used in psychology, epidemiology , medicine and nursing. They show the strength of correlation that exists between the variables within a population. For this reason, these studies are also known as ecological studies.  Correlational research design methods are characterized by such traits:

• Non-experimental method. No manipulation or exposure to extra conditions takes place. Researchers only examine how variables act in their natural environment without any interference.
• Fluctuating patterns. Association is never the same and can change due to various factors.
• Quantitative research. These studies require quantitative research methods . Researchers mostly run a statistical analysis and work with numbers to get results.
• Association-oriented study. Correlational study is aimed at finding an association between 2 or more phenomena or events. This has nothing to do with causal relationships between dependent and independent variables .

Correlational Research Questions

Correlational research questions usually focus on how one variable related to another one. If there is some connection, you will observe how strong it is. Let’s look at several examples.

Correlational Research Types

Depending on the direction and strength of association, there are 3 types of correlational research:

• Positive correlation If one variable increases, the other one will grow accordingly. If there is any reduction, both variables will decrease.

• Negative correlation All changes happen in the reverse direction. If one variable increases, the other one should decrease and vice versa.

• Zero correlation No association between 2 factors or events can be found.

Correlational Research: Data Collection Methods

There are 3 main methods applied to collect data in correlational research:

• Surveys and polls
• Naturalistic observation
• Secondary or archival data.

It’s essential that you select the right study method. Otherwise, it won’t be possible to achieve accurate results and answer the research question correctly. Let’s have a closer look at each of these methods to make sure that you make the right choice.

Surveys in Correlational Study

Survey is an easy way to collect data about a population in a correlational study. Depending on the nature of the question, you can choose different survey variations. Questionnaires, polls and interviews are the three most popular formats used in a survey research study. To conduct an effective study, you should first identify the population and choose whether you want to run a survey online, via email or in person.

Naturalistic Observation: Correlational Research

Naturalistic observation is another data collection approach in correlational research methodology. This method allows us to observe behavioral patterns in a natural setting. Scientists often document, describe or categorize data to get a clear picture about a group of people. During naturalistic observations, you may work with both qualitative and quantitative research information. Nevertheless, to measure the strength of association, you should analyze numeric data. Members of a population shouldn’t know that they are being studied. Thus, you should blend in a target group as naturally as possible. Otherwise, participants may behave in a different way which may cause a statistical error. 

Correlational Study: Archival Data

Sometimes, you may access ready-made data that suits your study. Archival data is a quick correlational research method that allows to obtain necessary details from the similar studies that have already been conducted. You won’t deal with data collection techniques , since most of numbers will be served on a silver platter. All you will be left to do is analyze them and draw a conclusion. Unfortunately, not all records are accurate, so you should rely only on credible sources.

Pros and Cons of Correlational Research

Choosing what study to run can be difficult. But in this article, we are going to take an in-depth look at advantages and disadvantages of correlational research. This should help you decide whether this type of study is the best fit for you. Without any ado, let’s dive deep right in.

Advantages of Correlational Research

Obviously, one of the many advantages of correlational research is that it can be conducted when an experiment can’t be the case. Sometimes, it may be unethical to run an experimental study or you may have limited resources. This is exactly when ecological study can come in handy.  This type of study also has several benefits that have an irreplaceable value:

• Works well as a preliminary study
• Allows examining complex connection between multiple variables
• Helps you study natural behavior
• Can be generalized to other settings.

If you decide to run an archival study or conduct a survey, you will be able to save much time and expenses.

Disadvantages of Correlational Research

There are several limitations of correlational research you should keep in mind while deciding on the main methodology. Here are the advantages one should consider:

• No causal relationships can be identified
• No chance to manipulate extraneous variables
• Biased results caused by unnatural behavior
• Naturalistic studies require quite a lot of time.

As you can see, these types of studies aren’t end-all, be-all. They may indicate a direction for further research. Still, correlational studies don’t show a cause-and-effect relationship which is probably the biggest disadvantage. 

Difference Between Correlational and Experimental Research

Now that you’ve come this far, let’s discuss correlational vs experimental research design . Both studies involve quantitative data. But the main difference lies in the aim of research. Correlational studies are used to identify an association which is measured with a coefficient, while an experiment is aimed at determining a causal relationship.  Due to a different purpose, the studies also have different approaches to control over variables. In the first case, scientists can’t control or otherwise manipulate the variables in question. Meanwhile, experiments allow you to control variables without limit. There is a  causation vs correlation  blog on our website. Find out their differences as it will be useful for your research.

Example of Correlational Research

Above, we have offered several correlational research examples. Let’s have a closer look at how things work using a more detailed example.

Example You want to determine if there is any connection between the time employees work in one company and their performance. An experiment will be rather time-consuming. For this reason, you can offer a questionnaire to collect data and assess an association. After running a survey, you will be able to confirm or disprove your hypothesis.

Correlational Study: Final Thoughts

That’s pretty much everything you should know about correlational study. The key takeaway is that this type of study is used to measure the connection between 2 or more variables. It’s a good choice if you have no chance to run an experiment. However, in this case you won’t be able to control for extraneous variables . So you should consider your options carefully before conducting your own research. 

We’ve got your back! Entrust your assignment to our skilled paper writers and they will complete a custom research paper with top quality in mind!

Frequently Asked Questions About Correlational Study

1. what is a correlation.

Correlation is a connection that shows to which extent two or more variables are associated. It doesn’t show a causal link and only helps to identify a direction (positive, negative or zero) or the strength of association.

2. How many variables are in a correlation?

There can be many different variables in a correlation which makes this type of study very useful for exploring complex relationships. However, most scientists use this research to measure the association between only 2 variables.

3. What is a correlation coefficient?

Correlation coefficient (ρ) is a statistical measure that indicates the extent to which two variables are related. Association can be strong, moderate or weak. There are different types of p coefficients: positive, negative and zero.

4. What is a correlational study?

Correlational study is a type of statistical research that involves examining two variables in order to determine association between them. It’s a non-experimental type of study, meaning that researchers can’t change independent variables or control extraneous variables.

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

You may also like

200+ Experimental Quantitative Research Topics For STEM Students In 2023

STEM stands for Science, Technology, Engineering, and Math, but these are not the only subjects we learn in school. STEM is like a treasure chest of skills that help students become great problem solvers, ready to tackle the real world’s challenges.

In this blog, we are here to explore the world of Research Topics for STEM Students. We will break down what STEM really means and why it is so important for students. In addition, we will give you the lowdown on how to pick a fascinating research topic. We will explain a list of 200+ Experimental Quantitative Research Topics For STEM Students.

And when it comes to writing a research title, we will guide you step by step. So, stay with us as we unlock the exciting world of STEM research – it is not just about grades; it is about growing smarter, more confident, and happier along the way.

What Is STEM?

Table of Contents

STEM is Science, Technology, Engineering, and Mathematics. It is a way of talking about things like learning, jobs, and activities related to these four important subjects. Science is about understanding the world around us, technology is about using tools and machines to solve problems, engineering is about designing and building things, and mathematics is about numbers and solving problems with them. STEM helps us explore, discover, and create cool stuff that makes our world better and more exciting.

Why STEM Research Is Important?

STEM research is important because it helps us learn new things about the world and solve problems. When scientists, engineers, and mathematicians study these subjects, they can discover cures for diseases, create new technology that makes life easier, and build things that help us live better. It is like a big puzzle where we put together pieces of knowledge to make our world safer, healthier, and more fun.

• STEM research leads to new discoveries and solutions.
• It helps find cures for diseases.
• STEM technology makes life easier.
• Engineers build things that improve our lives.
• Mathematics helps us understand and solve complex problems.

How to Choose a Topic for STEM Research Paper

Here are some steps to choose a topic for STEM Research Paper:

Step 1: Identify Your Interests

Think about what you like and what excites you in science, technology, engineering, or math. It could be something you learned in school, saw in the news, or experienced in your daily life. Choosing a topic you’re passionate about makes the research process more enjoyable.

Step 2: Research Existing Topics

Look up different STEM research areas online, in books, or at your library. See what scientists and experts are studying. This can give you ideas and help you understand what’s already known in your chosen field.

Step 3: Consider Real-World Problems

Think about the problems you see around you. Are there issues in your community or the world that STEM can help solve? Choosing a topic that addresses a real-world problem can make your research impactful.

Step 4: Talk to Teachers and Mentors

Discuss your interests with your teachers, professors, or mentors. They can offer guidance and suggest topics that align with your skills and goals. They may also provide resources and support for your research.

Step 5: Narrow Down Your Topic

Once you have some ideas, narrow them down to a specific research question or project. Make sure it’s not too broad or too narrow. You want a topic that you can explore in depth within the scope of your research paper.

Here we will discuss 200+ Experimental Quantitative Research Topics For STEM Students: 

Qualitative Research Topics for STEM Students:

Qualitative research focuses on exploring and understanding phenomena through non-numerical data and subjective experiences. Here are 10 qualitative research topics for STEM students:

• Exploring the experiences of female STEM students in overcoming gender bias in academia.
• Understanding the perceptions of teachers regarding the integration of technology in STEM education.
• Investigating the motivations and challenges of STEM educators in underprivileged schools.
• Exploring the attitudes and beliefs of parents towards STEM education for their children.
• Analyzing the impact of collaborative learning on student engagement in STEM subjects.
• Investigating the experiences of STEM professionals in bridging the gap between academia and industry.
• Understanding the cultural factors influencing STEM career choices among minority students.
• Exploring the role of mentorship in the career development of STEM graduates.
• Analyzing the perceptions of students towards the ethics of emerging STEM technologies like AI and CRISPR.
• Investigating the emotional well-being and stress levels of STEM students during their academic journey.

Easy Experimental Research Topics for STEM Students:

These experimental research topics are relatively straightforward and suitable for STEM students who are new to research:

•  Measuring the effect of different light wavelengths on plant growth.
•  Investigating the relationship between exercise and heart rate in various age groups.
•  Testing the effectiveness of different insulating materials in conserving heat.
•  Examining the impact of pH levels on the rate of chemical reactions.
•  Studying the behavior of magnets in different temperature conditions.
•  Investigating the effect of different concentrations of a substance on bacterial growth.
•  Testing the efficiency of various sunscreen brands in blocking UV radiation.
•  Measuring the impact of music genres on concentration and productivity.
•  Examining the correlation between the angle of a ramp and the speed of a rolling object.
•  Investigating the relationship between the number of blades on a wind turbine and energy output.

Research Topics for STEM Students in the Philippines:

These research topics are tailored for STEM students in the Philippines:

•  Assessing the impact of climate change on the biodiversity of coral reefs in the Philippines.
•  Studying the potential of indigenous plants in the Philippines for medicinal purposes.
•  Investigating the feasibility of harnessing renewable energy sources like solar and wind in rural Filipino communities.
•  Analyzing the water quality and pollution levels in major rivers and lakes in the Philippines.
•  Exploring sustainable agricultural practices for small-scale farmers in the Philippines.
•  Assessing the prevalence and impact of dengue fever outbreaks in urban areas of the Philippines.
•  Investigating the challenges and opportunities of STEM education in remote Filipino islands.
•  Studying the impact of typhoons and natural disasters on infrastructure resilience in the Philippines.
•  Analyzing the genetic diversity of endemic species in the Philippine rainforests.
•  Assessing the effectiveness of disaster preparedness programs in Philippine communities.

Read More 

• Frontend Project Ideas
• Business Intelligence Projects For Beginners

Good Research Topics for STEM Students:

These research topics are considered good because they offer interesting avenues for investigation and learning:

•  Developing a low-cost and efficient water purification system for rural communities.
•  Investigating the potential use of CRISPR-Cas9 for gene therapy in genetic disorders.
•  Studying the applications of blockchain technology in securing medical records.
•  Analyzing the impact of 3D printing on customized prosthetics for amputees.
•  Exploring the use of artificial intelligence in predicting and preventing forest fires.
•  Investigating the effects of microplastic pollution on aquatic ecosystems.
•  Analyzing the use of drones in monitoring and managing agricultural crops.
•  Studying the potential of quantum computing in solving complex optimization problems.
•  Investigating the development of biodegradable materials for sustainable packaging.
•  Exploring the ethical implications of gene editing in humans.

Unique Research Topics for STEM Students:

Unique research topics can provide STEM students with the opportunity to explore unconventional and innovative ideas. Here are 10 unique research topics for STEM students:

•  Investigating the use of bioluminescent organisms for sustainable lighting solutions.
•  Studying the potential of using spider silk proteins for advanced materials in engineering.
•  Exploring the application of quantum entanglement for secure communication in the field of cryptography.
•  Analyzing the feasibility of harnessing geothermal energy from underwater volcanoes.
•  Investigating the use of CRISPR-Cas12 for rapid and cost-effective disease diagnostics.
•  Studying the interaction between artificial intelligence and human creativity in art and music generation.
•  Exploring the development of edible packaging materials to reduce plastic waste.
•  Investigating the impact of microgravity on cellular behavior and tissue regeneration in space.
•  Analyzing the potential of using sound waves to detect and combat invasive species in aquatic ecosystems.
•  Studying the use of biotechnology in reviving extinct species, such as the woolly mammoth.

Experimental Research Topics for STEM Students in the Philippines

Research topics for STEM students in the Philippines can address specific regional challenges and opportunities. Here are 10 experimental research topics for STEM students in the Philippines:

• Assessing the effectiveness of locally sourced materials for disaster-resilient housing construction in typhoon-prone areas.
• Investigating the utilization of indigenous plants for natural remedies in Filipino traditional medicine.
• Studying the impact of volcanic soil on crop growth and agriculture in volcanic regions of the Philippines.
• Analyzing the water quality and purification methods in remote island communities.
• Exploring the feasibility of using bamboo as a sustainable construction material in the Philippines.
• Investigating the potential of using solar stills for freshwater production in water-scarce regions.
• Studying the effects of climate change on the migration patterns of bird species in the Philippines.
• Analyzing the growth and sustainability of coral reefs in marine protected areas.
• Investigating the utilization of coconut waste for biofuel production.
• Studying the biodiversity and conservation efforts in the Tubbataha Reefs Natural Park.

Capstone Research Topics for STEM Students in the Philippines:

Capstone research projects are often more comprehensive and can address real-world issues. Here are 10 capstone research topics for STEM students in the Philippines:

• Designing a low-cost and sustainable sanitation system for informal settlements in urban Manila.
• Developing a mobile app for monitoring and reporting natural disasters in the Philippines.
• Assessing the impact of climate change on the availability and quality of drinking water in Philippine cities.
• Designing an efficient traffic management system to address congestion in major Filipino cities.
• Analyzing the health implications of air pollution in densely populated urban areas of the Philippines.
• Developing a renewable energy microgrid for off-grid communities in the archipelago.
• Assessing the feasibility of using unmanned aerial vehicles (drones) for agricultural monitoring in rural Philippines.
• Designing a low-cost and sustainable aquaponics system for urban agriculture.
• Investigating the potential of vertical farming to address food security in densely populated urban areas.
• Developing a disaster-resilient housing prototype suitable for typhoon-prone regions.

Experimental Quantitative Research Topics for STEM Students:

Experimental quantitative research involves the collection and analysis of numerical data to conclude. Here are 10 Experimental Quantitative Research Topics For STEM Students interested in experimental quantitative research:

• Examining the impact of different fertilizers on crop yield in agriculture.
• Investigating the relationship between exercise and heart rate among different age groups.
• Analyzing the effect of varying light intensities on photosynthesis in plants.
• Studying the efficiency of various insulation materials in reducing building heat loss.
• Investigating the relationship between pH levels and the rate of corrosion in metals.
• Analyzing the impact of different concentrations of pollutants on aquatic ecosystems.
• Examining the effectiveness of different antibiotics on bacterial growth.
• Trying to figure out how temperature affects how thick liquids are.
• Finding out if there is a link between the amount of pollution in the air and lung illnesses in cities.
• Analyzing the efficiency of solar panels in converting sunlight into electricity under varying conditions.

Descriptive Research Topics for STEM Students

Descriptive research aims to provide a detailed account or description of a phenomenon. Here are 10 topics for STEM students interested in descriptive research:

• Describing the physical characteristics and behavior of a newly discovered species of marine life.
• Documenting the geological features and formations of a particular region.
• Creating a detailed inventory of plant species in a specific ecosystem.
• Describing the properties and behavior of a new synthetic polymer.
• Documenting the daily weather patterns and climate trends in a particular area.
• Providing a comprehensive analysis of the energy consumption patterns in a city.
• Describing the structural components and functions of a newly developed medical device.
• Documenting the characteristics and usage of traditional construction materials in a region.
• Providing a detailed account of the microbiome in a specific environmental niche.
• Describing the life cycle and behavior of a rare insect species.

Research Topics for STEM Students in the Pandemic:

The COVID-19 pandemic has raised many research opportunities for STEM students. Here are 10 research topics related to pandemics:

• Analyzing the effectiveness of various personal protective equipment (PPE) in preventing the spread of respiratory viruses.
• Studying the impact of lockdown measures on air quality and pollution levels in urban areas.
• Investigating the psychological effects of quarantine and social isolation on mental health.
• Analyzing the genomic variation of the SARS-CoV-2 virus and its implications for vaccine development.
• Studying the efficacy of different disinfection methods on various surfaces.
• Investigating the role of contact tracing apps in tracking & controlling the spread of infectious diseases.
• Analyzing the economic impact of the pandemic on different industries and sectors.
• Studying the effectiveness of remote learning in STEM education during lockdowns.
• Investigating the social disparities in healthcare access during a pandemic.
• Analyzing the ethical considerations surrounding vaccine distribution and prioritization.

Research Topics for STEM Students Middle School

Research topics for middle school STEM students should be engaging and suitable for their age group. Here are 10 research topics:

• Investigating the growth patterns of different types of mold on various food items.
• Studying the negative effects of music on plant growth and development.
• Analyzing the relationship between the shape of a paper airplane and its flight distance.
• Investigating the properties of different materials in making effective insulators for hot and cold beverages.
• Studying the effect of salt on the buoyancy of different objects in water.
• Analyzing the behavior of magnets when exposed to different temperatures.
• Investigating the factors that affect the rate of ice melting in different environments.
• Studying the impact of color on the absorption of heat by various surfaces.
• Analyzing the growth of crystals in different types of solutions.
• Investigating the effectiveness of different natural repellents against common pests like mosquitoes.

Technology Research Topics for STEM Students

Technology is at the forefront of STEM fields. Here are 10 research topics for STEM students interested in technology:

• Developing and optimizing algorithms for autonomous drone navigation in complex environments.
• Exploring the use of blockchain technology for enhancing the security and transparency of supply chains.
• Investigating the applications of virtual reality (VR) and augmented reality (AR) in medical training and surgery simulations.
• Studying the potential of 3D printing for creating personalized prosthetics and orthopedic implants.
• Analyzing the ethical and privacy implications of facial recognition technology in public spaces.
• Investigating the development of quantum computing algorithms for solving complex optimization problems.
• Explaining the use of machine learning and AI in predicting and mitigating the impact of natural disasters.
• Studying the advancement of brain-computer interfaces for assisting individuals with
• disabilities.
• Analyzing the role of wearable technology in monitoring and improving personal health and wellness.
• Investigating the use of robotics in disaster response and search and rescue operations.

Scientific Research Topics for STEM Students

Scientific research encompasses a wide range of topics. Here are 10 research topics for STEM students focusing on scientific exploration:

• Investigating the behavior of subatomic particles in high-energy particle accelerators.
• Studying the ecological impact of invasive species on native ecosystems.
• Analyzing the genetics of antibiotic resistance in bacteria and its implications for healthcare.
• Exploring the physics of gravitational waves and their detection through advanced interferometry.
• Investigating the neurobiology of memory formation and retention in the human brain.
• Studying the biodiversity and adaptation of extremophiles in harsh environments.
• Analyzing the chemistry of deep-sea hydrothermal vents and their potential for life beyond Earth.
• Exploring the properties of superconductors and their applications in technology.
• Investigating the mechanisms of stem cell differentiation for regenerative medicine.
• Studying the dynamics of climate change and its impact on global ecosystems.

Interesting Research Topics for STEM Students:

Engaging and intriguing research topics can foster a passion for STEM. Here are 10 interesting research topics for STEM students:

• Exploring the science behind the formation of auroras and their cultural significance.
• Investigating the mysteries of dark matter and dark energy in the universe.
• Studying the psychology of decision-making in high-pressure situations, such as sports or
• emergencies.
• Analyzing the impact of social media on interpersonal relationships and mental health.
• Exploring the potential for using genetic modification to create disease-resistant crops.
• Investigating the cognitive processes involved in solving complex puzzles and riddles.
• Studying the history and evolution of cryptography and encryption methods.
• Analyzing the physics of time travel and its theoretical possibilities.
• Exploring the role of Artificial Intelligence in creating art and music.
• Investigating the science of happiness and well-being, including factors contributing to life satisfaction.

Practical Research Topics for STEM Students

Practical research often leads to real-world solutions. Here are 10 practical research topics for STEM students:

• Developing an affordable and sustainable water purification system for rural communities.
• Designing a low-cost, energy-efficient home heating and cooling system.
• Investigating strategies for reducing food waste in the supply chain and households.
• Studying the effectiveness of eco-friendly pest control methods in agriculture.
• Analyzing the impact of renewable energy integration on the stability of power grids.
• Developing a smartphone app for early detection of common medical conditions.
• Investigating the feasibility of vertical farming for urban food production.
• Designing a system for recycling and upcycling electronic waste.
• Studying the environmental benefits of green roofs and their potential for urban heat island mitigation.
• Analyzing the efficiency of alternative transportation methods in reducing carbon emissions.

Experimental Research Topics for STEM Students About Plants

Plants offer a rich field for experimental research. Here are 10 experimental research topics about plants for STEM students:

• Investigating the effect of different light wavelengths on plant growth and photosynthesis.
• Studying the impact of various fertilizers and nutrient solutions on crop yield.
• Analyzing the response of plants to different types and concentrations of plant hormones.
• Investigating the role of mycorrhizal in enhancing nutrient uptake in plants.
• Studying the effects of drought stress and water scarcity on plant physiology and adaptation mechanisms.
• Analyzing the influence of soil pH on plant nutrient availability and growth.
• Investigating the chemical signaling and defense mechanisms of plants against herbivores.
• Studying the impact of environmental pollutants on plant health and genetic diversity.
• Analyzing the role of plant secondary metabolites in pharmaceutical and agricultural applications.
• Investigating the interactions between plants and beneficial microorganisms in the rhizosphere.

Qualitative Research Topics for STEM Students in the Philippines

Qualitative research in the Philippines can address local issues and cultural contexts. Here are 10 qualitative research topics for STEM students in the Philippines:

• Exploring indigenous knowledge and practices in sustainable agriculture in Filipino communities.
• Studying the perceptions and experiences of Filipino fishermen in coping with climate change impacts.
• Analyzing the cultural significance and traditional uses of medicinal plants in indigenous Filipino communities.
• Investigating the barriers and facilitators of STEM education access in remote Philippine islands.
• Exploring the role of traditional Filipino architecture in natural disaster resilience.
• Studying the impact of indigenous farming methods on soil conservation and fertility.
• Analyzing the cultural and environmental significance of mangroves in coastal Filipino regions.
• Investigating the knowledge and practices of Filipino healers in treating common ailments.
• Exploring the cultural heritage and conservation efforts of the Ifugao rice terraces.
• Studying the perceptions and practices of Filipino communities in preserving marine biodiversity.

Science Research Topics for STEM Students

Science offers a diverse range of research avenues. Here are 10 science research topics for STEM students:

• Investigating the potential of gene editing techniques like CRISPR-Cas9 in curing genetic diseases.
• Studying the ecological impacts of species reintroduction programs on local ecosystems.
• Analyzing the effects of microplastic pollution on aquatic food webs and ecosystems.
• Investigating the link between air pollution and respiratory health in urban populations.
• Studying the role of epigenetics in the inheritance of acquired traits in organisms.
• Analyzing the physiology and adaptations of extremophiles in extreme environments on Earth.
• Investigating the genetics of longevity and factors influencing human lifespan.
• Studying the behavioral ecology and communication strategies of social insects.
• Analyzing the effects of deforestation on global climate patterns and biodiversity loss.
• Investigating the potential of synthetic biology in creating bioengineered organisms for beneficial applications.

Correlational Research Topics for STEM Students

Correlational research focuses on relationships between variables. Here are 10 correlational research topics for STEM students:

• Analyzing the correlation between dietary habits and the incidence of chronic diseases.
• Studying the relationship between exercise frequency and mental health outcomes.
• Investigating the correlation between socioeconomic status and access to quality healthcare.
• Analyzing the link between social media usage and self-esteem in adolescents.
• Studying the correlation between academic performance and sleep duration among students.
• Investigating the relationship between environmental factors and the prevalence of allergies.
• Analyzing the correlation between technology use and attention span in children.
• Studying how environmental factors are related to the frequency of allergies.
• Investigating the link between parental involvement in education and student achievement.
• Analyzing the correlation between temperature fluctuations and wildlife migration patterns.

Quantitative Research Topics for STEM Students in the Philippines

Quantitative research in the Philippines can address specific regional issues. Here are 10 quantitative research topics for STEM students in the Philippines

• Analyzing the impact of typhoons on coastal erosion rates in the Philippines.
• Studying the quantitative effects of land use change on watershed hydrology in Filipino regions.
• Investigating the quantitative relationship between deforestation and habitat loss for endangered species.
• Analyzing the quantitative patterns of marine biodiversity in Philippine coral reef ecosystems.
• Studying the quantitative assessment of water quality in major Philippine rivers and lakes.
• Investigating the quantitative analysis of renewable energy potential in specific Philippine provinces.
• Analyzing the quantitative impacts of agricultural practices on soil health and fertility.
• Studying the quantitative effectiveness of mangrove restoration in coastal protection in the Philippines.
• Investigating the quantitative evaluation of indigenous agricultural practices for sustainability.
• Analyzing the quantitative patterns of air pollution and its health impacts in urban Filipino areas.

Things That Must Keep In Mind While Writing Quantitative Research Title 

Here are a few things that must be kept in mind while writing a quantitative research:

1. Be Clear and Precise

Make sure your research title is clear and says exactly what your study is about. People should easily understand the topic and goals of your research by reading the title.

2. Use Important Words

Include words that are crucial to your research, like the main subjects, who you’re studying, and how you’re doing your research. This helps others find your work and understand what it’s about.

3. Avoid Confusing Words

Stay away from words that might confuse people. Your title should be easy to grasp, even if someone isn’t an expert in your field.

4. Show Your Research Approach

Tell readers what kind of research you did, like experiments or surveys. This gives them a hint about how you conducted your study.

5. Match Your Title with Your Research Questions

Make sure your title matches the questions you’re trying to answer in your research. It should give a sneak peek into what your study is all about and keep you on the right track as you work on it.

STEM students, addressing what STEM is and why research matters in this field. It offered an extensive list of research topics , including experimental, qualitative, and regional options, catering to various academic levels and interests. Whether you’re a middle school student or pursuing advanced studies, these topics offer a wealth of ideas. The key takeaway is to choose a topic that resonates with your passion and aligns with your goals, ensuring a successful journey in STEM research. Choose the best Experimental Quantitative Research Topics For Stem Students today!

Related Posts

Step by Step Guide on The Best Way to Finance Car

The Best Way on How to Get Fund For Business to Grow it Efficiently

Get the Reddit app

A place for researchers to interact. Ask questions, tell stories, share tips, and anything in between!

120+ Great Correlational Research Topics For Students [2024]

By continuing, you agree to our User Agreement and acknowledge that you understand the Privacy Policy .

Enter the 6-digit code from your authenticator app

You’ve set up two-factor authentication for this account.

Enter a 6-digit backup code

Create your username and password.

Reddit is anonymous, so your username is what you’ll go by here. Choose wisely—because once you get a name, you can’t change it.

Reset your password

Enter your email address or username and we’ll send you a link to reset your password

Check your inbox

An email with a link to reset your password was sent to the email address associated with your account

Choose a Reddit account to continue

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Dental hygiene challenges in children with autism: correlation with parental stress: a scoping review.

1. Introduction

2. material and methods, 2.1. inclusion criteria, 2.2. exclusion criteria, 2.3. search period, 2.4. search formulae, 2.5. step-by-step procedure.

• Identification of Records in Each Database (PubMed, Medline, ScienceDirect, and Scopus) via Search Formulae
• Exclusion of Matching or Duplicate Records between Databases
• Record Selection by Title and Abstract, with Inclusion Criteria
• Review and Evaluation of Complete Articles, Including Principal Findings
• Final Selection of Articles for Analysis

2.6. Procedure for Analyzing the Selected Articles

2.6.1. pico standard, 2.6.2. abstract in each field, 2.6.3. qualitative analysis, 2.6.4. contrast between articles, 2.6.5. discussion of thematic connections, 4. discussion, 4.1. summary of key findings and interpretation, 4.2. scoping and limitations, 4.3. future studies, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Bernath, B.; Kanji, Z. Exploring barriers to oral health care experienced by individuals living with autism spectrum disorder. Can. J. Dent. Hyg. 2021 , 55 , 160–166. [ Google Scholar ] [ PubMed ]
• Como, D.H.; Stein Duker, L.I.; Polido, J.C.; Cermak, S.A. Oral Health and Autism Spectrum Disorders: A Unique Collaboration between Dentistry and Occupational Therapy. Int. J. Environ. Res. Public Health 2021 , 18 , 135. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Farias, A.C.; Barbosa, T.; Duarte, M. Parental Perception of the Oral Health-Related Quality of Life of Children and Adolescents with Autism Spectrum Disorder (ASD). Int. J. Environ. Res. Public Health 2023 , 20 , 1151. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fernández, M.P.; Espinoza, A.E. Salud mental e intervenciones para padres de niños con trastorno del espectro autista: Una revisión narrativa y la relevancia de esta temática en Chile. Rev. Psicol. 2019 , 37 , 643–682. [ Google Scholar ] [ CrossRef ]
• World Health Organization. Autism: Key Facts ; World Health Organization: New York, NY, USA, 2023; Available online: https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disorders (accessed on 30 May 2024).
• El Khatib, A.; El Tekeya, M.; El Tantawi, M.; Omar, T. Oral health status and behaviours of children with Autism Spectrum Disorder: A case-control study. Int. J. Paediatr. Dent. 2013 , 24 , 314–323. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ferrer-Coba, S.; Martínez-Hernández, N.L.; Recio-Díaz, T.; Ferrer-Coba, O. Atención estomatológica integral a niños con trastorno del espectro autista. Rev. Cienc. Méd. Pinar Río 2022 , 26 , e5160. [ Google Scholar ]
• Medina-Oropeza, D.; Rueda-Ventura, M.; Ramírez-Mendoza, J.; Hernández Abreu, K. Cuidados bucodentales que tienen los padres con el estado de salud dental de los niños con Trastorno del Espectro del Autismo en el CRIAT. Rev. Tamé 2018 , 7 , 769–772. [ Google Scholar ]
• Alqahtani, A.S.; Gufran, K.; Alsakr, A.; Alnufaiy, B.; Al Ghwainem, A.; Bin Khames, Y.M.; Althani, R.A.; Almuthaybiri, S.M. Oral Healthcare Practices and Awareness among the Parents of Autism Spectrum Disorder Children: A Multi-Center Study. Children 2023 , 10 , 978. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• AlHumaid, J. Dental experiences related to oral care of children with autism spectrum disorders in Saudi Arabia: A literature review. Saudi Dent. J. 2022 , 34 , 1–10. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pimienta, N.; González, Y.; Rodríguez, L. Autismo infantil, manejo en la Especialidad de Odontología. Acta Méd. Centro 2017 , 11 , 70–80. [ Google Scholar ]
• Erwin, J.; Paisi, M.; Neill, S.; Burns, L.; Vassallo, L.; Nelder, A.; Facenfield, J.; Devalia, U.; Vassallo, T.; Witton, R. Factors influencing oral health behaviours, access and delivery of dental care for autistic children and adolescents: A mixed-methods systematic review. Health Expect. 2022 , 25 , 1269–1318. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stein, L.I.; Lane, C.J.; Williams, M.E.; Dawson, M.E.; Polido, J.C.; Cermak, S.A. Physiological and Behavioral Stress and Anxiety in Children with Autism Spectrum Disorders during Routine Oral Care. BioMed Res. Int. 2014 , 2014 , 694876. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mohamed, H.; Abdalla, E. Behavioral Guidance Approaches to Provide Dental Care for Patients with Autism Spectrum Disorder: A Review of the Literature. Acta Sci. Dent. Sci. 2022 , 6 , 71–85. [ Google Scholar ] [ CrossRef ]
• Nunes Da Silva, S.; Gimenez, T.; Souza, R.; Mello-Moura, A.; Raggio, D.; Morimoto, S.; Lara, J.S.; Soares, G.C.; Tedesco, T.K. Oral health status of children and young adults with autism spectrum disorders: Systematic review and meta-analysis. Int. J. Paediatr. Dent. 2016 , 27 , 388–398. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Park, Y.; Guzick, A.G.; Schneider, S.C.; Fuselier, M.; Wood, J.J.; Kerns, C.M.; Kendall, P.C.; Storch, E.A. Dental Anxiety in Children with Autism Spectrum Disorder: Understanding Frequency and Associated Variables. Front. Psychiatry 2022 , 13 , 838557. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tirado-Amador, L.; Madera, M.; Leal-Acosta, C. Salud bucal en sujetos con trastorno del espectro autista: Consideraciones para la atención odontológica. CES Odontol. 2021 , 34 , 139–158. [ Google Scholar ] [ CrossRef ]
• Al-Oran, H.M.; Khuan, L. Predictors of parenting stress in parents of children diagnosed with autism spectrum disorder: A scoping review. Egypt. J. Neurol. Psychiatry Neurosurg. 2021 , 57 , 103. [ Google Scholar ] [ CrossRef ]
• Baker-Ericzén, M.J.; Brookman-Frazee, L.; Stahmer, A. Stress Levels and Adaptability in Parents of Toddlers with and without Autism Spectrum Disorders. Res. Pract. Pers. Sev. Disabil. 2005 , 30 , 194–204. [ Google Scholar ] [ CrossRef ]
• Di Renzo, M.; Guerriero, V.; Petrillo, M.; Bianchi di Castelbianco, F. What is Parental Stress Connected to in Families of Children with Autism Spectrum Disorder? Implications for Parents’ Interventions. J. Fam. Issues 2022 , 43 , 2456–2479. [ Google Scholar ] [ CrossRef ]
• Galpin, J.; Barratt, P.; Ashcroft, E.; Greathead, S.; Kenny, L.; Pellicano, E. ‘The dots just don’t join up’: Understanding the support needs of families of children on the autism spectrum. Autism 2018 , 22 , 571–584. [ Google Scholar ] [ CrossRef ]
• Li, F.; Tang, Y.; Li, F.; Fang, S.; Liu, X.; Tao, M.; Wu, D.; Jiang, L. Psychological distress in parents of children with autism spectrum disorder: A cross-sectional study based on 683 mother-father dyads. J. Pediatr. Nurs. 2022 , 65 , e49–e55. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Porter, N.; Loveland, K. An Integrative Review of Parenting Stress in Mothers of Children with Autism in Japan. Int. J. Disabil. Dev. Ed. 2018 , 66 , 249–272. [ Google Scholar ] [ CrossRef ]
• Pozo, P.; Sarriá, E. A global model of stress in parents of individuals with autism spectrum disorders (ASD). Ann. Psychol. 2014 , 30 , 180–191. [ Google Scholar ] [ CrossRef ]
• Curley, K.; Colman, R.; Rushforth, A.; Kotera, Y. Stress Reduction Interventions for Parents of Children with Autism Spectrum Disorder: A Focused Literature Review. Youth 2023 , 3 , 246–260. [ Google Scholar ] [ CrossRef ]
• Ni’matuzahroh; Suen, M.W.; Ningrum, V.; Widayat; Yuniardi, M.S.; Hasanati, N.; Wang, J.H. The Association between Parenting Stress, Positive Reappraisal Coping, and Quality of Life in Parents with Autism Spectrum Disorder (ASD) Children: A Systematic Review. Healthcare 2022 , 10 , 52. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ng, C.S.M.; Fang, Y.; Wang, Z.; Zhang, M. Potential Factors of Parenting Stress in Chinese Parents of Children with Autism Spectrum Disorder: A Systematic Review. Focus Autism Other Dev. Disabl. 2021 , 36 , 237–248. [ Google Scholar ] [ CrossRef ]
• Jolly, K.; Huber, T. Prevalence of Anxiety and Stress in Mothers of Children with Autism Spectrum Disorder (ASD): A Review of Literature. Res. J. Ed. 2020 , 6 , 121–127. [ Google Scholar ] [ CrossRef ]
• Dolev, S.; Oppenheim, D.; Koren-Karie, N.; Yirmiya, N. Emotional Availability in Mother-Child Interaction: The Case of Children with Autism Spectrum Disorders. Parenting 2009 , 9 , 183–197. [ Google Scholar ] [ CrossRef ]
• May, C.; Fletcher, R.; Dempsey, I.; Newman, L. Modeling Relations among Coparenting Quality, Autism-Specific Parenting Self-Efficacy, and Parenting Stress in Mothers and Fathers of Children with ASD. Parenting 2015 , 15 , 119–133. [ Google Scholar ] [ CrossRef ]
• Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.; Colquhoun, H.; Kastner, M.; Levac, D.; Ng, C.; Pearson Sharpe, J.; Wilson, K.; et al. A scoping review on the conduct and reporting of scoping reviews. BMC Med. Res. Methodol. 2016 , 16 , 15. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• AlHumaid, J.; Gaffar, B.; AlYousef, Y.; Alshuraim, F.; Alhareky, M.; El Tantawi, M. Oral Health of Children with Autism: The Influence of Parental Attitudes and Willingness in Providing Care. Sci. World J. 2020 , 2020 , 8329426. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alibekova, R.; Chan, C.K.; Crape, B.; Kadyrzhanuly, K.; Gusmanov, A.; An, S.; Bulekbayeva, S.; Akhmetzhanova, Z.; Ainabekova, A.; Yerubayev, Z.; et al. Stress, anxiety and depression in parents of children with autism spectrum disorders in Kazakhstan: Prevalence and associated factors. Glob. Ment. Health 2022 , 9 , 472–482. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Foody, C.; James, J.; Leader, G. Parenting stress, salivary biomarkers, and ambulatory blood pressure in mothers of children with Autism Spectrum Disorders. Res. Autism Spectr. Disord. 2014 , 8 , 99–110. [ Google Scholar ] [ CrossRef ]
• Parry, J.A.; Newton, T.; Linehan, C.; Ryan, C. Dental Visits for Autistic Children: A Qualitative Focus Group Study of Parental Perceptions. JDR Clin. Trans. Res. 2023 , 8 , 36–47. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Permatasari, I.; Saskianti, T.; Moeharyono, M. Correlation Of Mother’s Behavior In Autistic Children With DMF-T Value and Dental Care Need. Int. J. Pharm. Res. 2020 , 12 , 1570–1574. [ Google Scholar ] [ CrossRef ]
• Prakash, J.; Das, I.; Bindal, R.; Shivu, M.E.; Sidhu, S.; Kak, V.; Kumar, A. Parental perception of oral health-related quality of life in children with autism. An observational study. J. Family Med. Prim. Care 2021 , 10 , 3845–3850. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Verma, A.; Priyank, H.; Viswanath, B.; Bhagat, J.K.; Purbay, S.; Mahalakshmi, V.; Shivakumar, S. Assessment of Parental Perceptions of Socio-Psychological Factors, Unmet Dental Needs, and Barriers to Utilise Oral Health Care in Autistic Children. Cureus 2022 , 14 , e27950. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alvesson, M.; Sandberg, J. Constructing Research Questions: Doing Interesting Research ; SAGE Publications: London, UK, 2013. [ Google Scholar ]
• Omer, R. Oral Health Practices and Challenges Facing Parents of Autistic Children in the Western Cape. Master’s Thesis, University of the Western Cape, Cape Town, South Africa, 2022. [ Google Scholar ]
• Zieliński, G.; Ginszt, M.; Zawadka, M.; Rutkowska, K.; Podstawka, Z.; Szkutnik, J.; Majcher, P.; Gawda, P. The Relationship between Stress and Masticatory Muscle Activity in Female Students. J. Clin. Med. 2021 , 10 , 3459. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mercante, J.P.P.; Oliveira, A.B.; Peres, M.F.P.; Wang, Y.P.; Brunoni, A.R.; Lotufo, P.A.; Benseñor, I.M.; Goulart, A.C. Association of mental health symptoms with the migraine-tension-type headache spectrum in the Brazilian longitudinal study of adult health. J. Psychosom. Res. 2024 , 179 , 111624. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Enea, V.; Rusu, D.M. Raising a Child with Autism Spectrum Disorder: A Systematic Review of the Literature Investigating Parenting Stress. J. Ment. Health Res. Intellect. Disabil. 2020 , 13 , 283–321. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Alegría, P.L.; Landim, S.F.; Branco, B.H.M.; Carmine, F.; Birditt, K.; Sandoval, C.; González, M.M. Dental Hygiene Challenges in Children with Autism: Correlation with Parental Stress: A Scoping Review. J. Clin. Med. 2024 , 13 , 4675. https://doi.org/10.3390/jcm13164675

Alegría PL, Landim SF, Branco BHM, Carmine F, Birditt K, Sandoval C, González MM. Dental Hygiene Challenges in Children with Autism: Correlation with Parental Stress: A Scoping Review. Journal of Clinical Medicine . 2024; 13(16):4675. https://doi.org/10.3390/jcm13164675

Alegría, Pablo López, Síbila Floriano Landim, Braulio Henrique Magnani Branco, Florencia Carmine, Katherine Birditt, Cristian Sandoval, and Manuel Martín González. 2024. "Dental Hygiene Challenges in Children with Autism: Correlation with Parental Stress: A Scoping Review" Journal of Clinical Medicine 13, no. 16: 4675. https://doi.org/10.3390/jcm13164675

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• 06 August 2024

Our local research project put us on the global stage — here’s how you can do it, too

• Seyoon Lee 0 ,
• Hanjae Lee 1 ,
• Juhyun Kim 2 &
• Jong-Il Kim 3

Seyoon Lee is a PhD candidate at the Genomic Medicine Institute, Medical Research Center, Seoul National University, and in the Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea.

You can also search for this author in PubMed   Google Scholar

Hanjae Lee is a PhD candidate at the Genomic Medicine Institute, Department of Translational Medicine, and in the Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea.

Juhyun Kim is a PhD candidate at the Genomic Medicine Institute and in the Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea.

Jong-Il Kim is director of the Genomic Medicine Institute, chair of the Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea, and head principal investigator of the SCAID project.

You have full access to this article via your institution.

People travel from across South Korea to receive medical treatment in Seoul. Credit: Will & Deni McIntyre/Getty

Later this year, South Korea is expected to sign up to the European Union’s research-funding programme, Horizon Europe . It’s a good time to reflect on the nature of large collaborative projects — and, in particular, when cross-border collaboration is most beneficial and when a deeper dive into local issues can be more rewarding.

Large international collaborations have unquestionably produced great breakthroughs. Sequencing the human genome , for example, took 13 years of work by 20 institutions in 6 countries 1 . But large consortia such as these are almost always established in the same few countries: the United States, the United Kingdom and others in Europe. For scientists working elsewhere, setting up a large international project can seem unachievable, given the billion-dollar price tags and the networks of contacts required.

And, sometimes, it is not the best solution. Global projects spearheaded in a few countries can have biases — for example, people of Asian descent are often under-represented in international genetic studies initiated in the West. National laws on acquiring data can differ, meaning that researchers need to conduct experiments differently in different regions, introducing biases. And the logistical complexity of coordinating a project across multiple countries in different time zones and with different work cultures can be problematic when rapid data collection and analysis are crucial 2 .

There is an alternative — set up a large local consortium in one nation.

Cancer research needs a better map

We’ve done just that in Seoul. Our single-cell atlas of immune diseases (SCAID) consortium is a multi-institutional effort led by one of us (J.-I.K.), alongside 23 others. Running since April 2022, the project now involves 120 South Korean clinicians, immunologists, geneticists and bioinformaticians (including S.L., H.L. and J.K., who work in J.-I.K’s group).

We aim to map gene expression in millions of individual cells from people who have immune-related diseases , including (but not limited to) rheumatoid arthritis, inflammatory bowel disease, interstitial lung disease and alopecia areata. Systemic immune diseases are thought to affect at least 1 in 20 people 3 . They are often incurable and cause debilitating symptoms, from chronic skin rashes to skeletomuscular changes. They can be fatal if they are not managed appropriately. We hope that our research will reveal similarities between 16 diverse diseases that manifest across the body, and help to uncover ways to use treatments more effectively.

Our experiences have shown us that a regional consortium can be an efficient way to ask crucial research questions. Here, we share two broad lessons that we hope will help others to build effective regional consortia.

Find a niche

To compete in international circles, local consortia need to focus on addressing research questions that they are in a unique position to answer. This might be because of the particular mix of expertise of local researchers. It might be the regulatory environment in a country. Or it might be specific to the geography of the place where the research is done.

In our case, we were inspired to set up SCAID by an international consortium called the Human Cell Atlas (HCA) . Since 2016, it has been trying to map every single cell type in the human body using state-of-the art genomic technology. The next logical step is to create similar atlases for diseased cells. But this involves bringing in specialized clinicians for each disease and obtaining proper consent from a large number of people.

This can be hard to achieve in a global consortium, in which each country has distinct legislative frameworks, ethics committees and medical systems 4 . For instance, the International HapMap Project — a genome-sequencing project launched in 2002 with researchers from six countries — needed to spend months in community consultation in Nigeria before it was able to obtain ethics approvals 5 . It also faced concerns raised by community advisory groups in Japan and China around depositing biological samples in overseas repositories. Overcoming these obstacles took 18 months 6 .

For these reasons, most single-cell studies of disease data sets have focused on single diseases in single tissues, for simplicity. By contrast, restricting our study to a single country with one legislative framework has made it easier for us to gain ethics and individual approval, allowing us to study multiple diseases across multiple tissues.

Seoul National University Hospital is one of 56 general hospitals in the South Korean capital. Credit: Anthony Wallace/AFP via Getty

Seoul also has other benefits for such a project. First, it’s easy to enlist a diverse range of participants in the city. South Korea has a universal medical-insurance system that is mandatory for all residents 7 . This avoids biases that can arise when participants are part of a private health-insurance system. And people from across the country and all socio-economic classes travel to Seoul for treatment — the city’s cluster of 56 general hospitals can be reached from anywhere in South Korea in half a day.

The concentration of hospitals also makes it easy to transfer samples quickly from donors to our central laboratory for analysis — it is no more than two hours’ drive from any hospital. Such proximity is a great advantage in single-cell genomics, because RNA — which is analysed to ascertain gene expression — degrades within hours once a sample is collected. A US National Institutes of Health large-scale genetics project called the Genotype–Tissue Expression project, for instance, found variability in the quality of RNA in its samples, depending on the time between collection and processing. This variability could skew interpretations of gene-expression data, and the researchers had to develop ways to account for it in their analyses 8 .

Having a centralized hub prevents the problem of batch effects — undesired differences between samples — that can arise if samples are processed or analysed differently by different centres 9 , 10 . Handling batch effects is a big task for international consortia. The HCA, for instance, has a dedicated team of researchers to check for and minimize such effects 11 .

Exploiting this niche is already proving fruitful for us. So far, we’ve collected more than 500 samples from 334 donors. We have analysed more than two million cells — equivalent to the second-largest data set collected in the HCA project so far. Our early analysis hints at common features between diseases: although symptoms arise in different organs, we are identifying distinct immune profiles that group the diseases into a few major categories.

Unblock research bottlenecks with non-profit start-ups

Still, being small and nimble comes with challenges. Local consortia need to be aware that they might lack some expertise , and they need to be prepared to seek help. Our consortium faced obstacles in obtaining ethics approvals, because each hospital review board had different requirements and concerns. Getting approval from each board was arduous, and required persistence when asking for opinions of the boards themselves, along with those of the Korea National Institute for Bioethics Policy and Korean Bioinformation Center. Nonetheless, it was easier than grappling with multiple international rules around ethics and data collection.

To make this process smoother for others, it would help for institutions in a country to standardize their ethical-review processes and data-sharing agreements, ensuring that both comply with national regulations. Furthermore, institutions should establish collaborative networks to share best practices and discuss common challenges. These steps could ease the administrative burden on local consortia considerably, and accelerate their progress.

Not all countries will have the strong technical skills of the South Korean workforce, nor the established biobanking repositories for genetic and clinical data, which are essential in projects such as ours. For scientists in countries without this infrastructure, international consortia can be a valuable source of guidance. For instance, the HCA’s Equity Working Group specifically aims to engage diverse geographical and ethnic groups in its projects 12 . By participating in such initiatives, countries can gain access to expertise, resources and best practices, helping them to overcome technical challenges and build their capabilities.

Build in local benefits

Regional projects should reflect the needs of the local community, both for ethical reasons and to attract funding. Funders are more likely to invest in big projects that can benefit citizens. Researchers must make those benefits clear.

This might mean championing a field to governments and other funders. In South Korea, most research funding comes from the government — scientists propose broad topics that need funding, and the government selects those that align with its own goals and puts out funding calls, for which all researchers can apply. So genomicists, immunologists and bioinformaticians — not all of whom are members of the SCAID consortium — requested that the South Korean government fund a large-scale disease single-cell atlas. These scientists spelled out how the data could ultimately help researchers and clinicians to improve understanding of the disease predispositions that are unique to South Koreans. This will hopefully speed up the development of precision medicines tailored to the country’s own population.

South Korean scientists’ outcry over planned R&D budget cuts

In countries that do not have official channels for petitioning the government, raising the profile of a field might involve using networks of contacts to meet with funders, or publishing papers that outline a field’s potential. Persistence is key — scientists must keep voicing their needs and perspectives.

Researchers must also give careful thought to how their project will benefit local science. SCAID was designed to maximize the long-term benefits for the South Korean researchers and clinicians involved.

To develop researchers’ careers, we hold regular seminars and workshops focused on learning skills and network building. Cross-disciplinary collaborations are one focus. For example, bioinformaticians are working with clinicians on a strategy pinpointing the specialized data that should be collected for each disease — such as acquiring information on immune receptors for specific disorders. Bioinformaticians are also exploiting the expertise of clinicians to help interpret their analyses. This includes the identification of abnormal cell states, which can be hard to distinguish from artefacts in the data without a deep knowledge of disease. These networks of contacts will be useful for many projects long after SCAID is completed.

Once established, local consortia need not exist in isolation. They can complement existing global projects by adding diverse data, and can act as stepping stones for future global consortia. For instance, many scientists have approached us, intrigued by the scale and potential of our work, and enquired about possible collaborations.

We are keen for other regional groups to generate international databases from separate efforts led by those who understand their own local needs and niches best. We encourage them to start by seeking funding for a consortium to address the needs of their fellow citizens, and to eventually pool their knowledge.

Whatever the field, if a consortium is run well, it can cultivate a dynamic cluster of competent researchers, laying the groundwork for international recognition and collaboration.

Nature 632 , 256-258 (2024)

doi: https://doi.org/10.1038/d41586-024-02539-7

International Human Genome Sequencing Consortium. Nature 409 , 860–921 (2001).

Article   PubMed   Google Scholar  

Ando, Y., Kwon, A. T.-J. & Shin, J. W. Exp. Mol. Med. 52 , 1409–1418 (2020).

El-Gabalawy, H., Guenther, L. C. & Bernstein, C. N. J. Rheumatol. Suppl. 85 , 2–10 (2010).

Chen, H. & Pang, T. Bull. World Health Organ. 93 , 113–117 (2015).

The International HapMap Consortium. Nature Rev. Genet. 5 , 467–475 (2004).

Rotimi, C. et al. Community Genet. 10 , 186–198 (2007).

PubMed   Google Scholar  

Kwon, S. Health. Policy. Plan. 24 , 63–71 (2009).

Ferreira, P. G. et al. Nature Commun. 9 , 490 (2018).

Lähnemann, D. et al. Genome. Biol. 21 , 31 (2020).

van den Brink, S. C. et al. Nature Methods 14 , 935–936 (2017).

Rozenblatt-Rosen, O. et al. Nature Biotechnol. 39 , 149–153 (2021).

Majumder, P. P., Mhlanga, M. M. & Shalek, A. K. Nature Med. 26 , 1509–1511 (2020).

Download references

Reprints and permissions

Competing Interests

The authors declare no competing interests.

Related Articles

• Scientific community
• Research management

Why I’ve removed journal titles from the papers on my CV

Career Column 09 AUG 24

The Taliban ‘took my life’ — scientists who fled takeover speak out

News 09 AUG 24

Why we quit: how ‘toxic management’ and pandemic pressures fuelled disillusionment in higher education

Career News 08 AUG 24

Hybrid conferences should be the norm — optimize them so everyone benefits

Editorial 06 AUG 24

How a space physicist is shaking up China’s research funding

News Q&A 02 AUG 24

Sexual harassment in science: biologists in India speak out

News Feature 29 JUL 24

From Vikings to Beethoven: what your DNA says about your ancient relatives

News Feature 07 AUG 24

Prognostic genome and transcriptome signatures in colorectal cancers

Article 07 AUG 24

Teosinte Pollen Drive guides maize diversification and domestication by RNAi

Professor/Associate Professor/Assistant Professor/Senior Lecturer/Lecturer

The School of Science and Engineering (SSE) at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) sincerely invites applications for mul...

Shenzhen, China

The Chinese University of Hong Kong, Shenzhen (CUHK Shenzhen)

Postdoctoral Fellowships at West China Hospital/West China School of Medicine of Sichuan University

Open to PhD students, PhD, Post-Doc and residents.

Chengdu, Sichuan, China

West China School of Medicine/West China Hospital

Welcome Global Talents to West China Hospital/West China School of Medicine of Sichuan University

Top Talents; Leading Talents; Excellent Overseas Young Talents on National level; Overseas Young Talents

Open Faculty Position in Mathematical and Information Security

We are now seeking outstanding candidates in all areas of mathematics and information security.

Dongguan, Guangdong, China

GREAT BAY INSTITUTE FOR ADVANCED STUDY： Institute of Mathematical and Information Security

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Biodiversity
• Cities & society
• Land & water
• All research news
• All research topics
• Learning experiences
• Programs & partnerships
• All school news
• All school news topics
• In the media
• For journalists

New climate and sustainability research efforts will focus on eight ‘Solution Areas’

The Stanford Doerr School of Sustainability will establish new research initiatives under topics including climate, water, energy, food, nature, and cities.

The Stanford Doerr School of Sustainability has selected eight interconnected Solution Areas to focus its research efforts over the next decade. This new research plan amplifies the school’s ability to translate Stanford research into large-scale solutions and inform key decision makers in policy and business.

Selected based on extensive faculty input and assessment of where Stanford can make the most meaningful impact, the eight areas are: climate; water; energy; food; risk, resilience, and adaptation; nature; cities; and platforms and tools for monitoring and decision making. 

“Solution Areas identify and leverage the critical junctions between the most pressing global sustainability challenges and the areas where Stanford has the talent and expertise to find solutions,” said Dean Arun Majumdar. “This collaborative all-campus approach expands and strengthens our commitment to using all the power we have – the knowledge, the education, the talent, the innovation, the resources, the influence – to build a thriving planet for future generations.” 

‘Integrative Projects’ and ‘Flagship Destinations’

In each Solution Area, the school plans to build two types of research initiatives. One type, called Integrative Projects, will be managed by the school’s institutes, including the Stanford Woods Institute for the Environment , the Precourt Institute for Energy , and a planned Sustainable Societies Institute. 

Integrative Projects will be organized around decade-long research themes and dedicated to creating solutions through interdisciplinary collaboration, engagement with partners beyond Stanford, identifying significant knowledge gaps, and understanding systems.

According to Chris Field , the Perry L. McCarty Director of the Stanford Woods Institute for the Environment and a professor in the Stanford Doerr School of Sustainability and the School of Humanities and Sciences , the new commitment to these areas “will provide both resources and coordination that expand Stanford faculty’s capacity to deliver sustainability solutions at scale.” 

A second type of research initiative, called Flagship Destinations, is managed by Stanford’s Sustainability Accelerator . Flagship Destinations are targets for the pace and scale of work to address challenges facing Earth, climate, and society. For example, the school’s first Flagship Destination, announced in 2023 , calls for enabling the removal of billions of tons of planet-warming gases annually from Earth’s atmosphere by the middle of this century. By working backward from sustainability targets in consultation with faculty and external experts, this initiative seeks to rapidly translate Stanford research into policy and technology solutions. Additional Flagship Destinations will be announced later this week.

Whereas Integrative Projects are designed to produce knowledge and evidence that can eventually lead to solutions, Flagship Destination projects are intended to help verify and demonstrate that well-studied solutions can succeed at large scale so they can be launched out of Stanford and implemented for the benefit of humanity and our planet. Scalable solutions nurtured and launched through these projects could take the form of policy frameworks, open-source platforms, nonprofit organizations, new for-profit companies, and ongoing collaborations all committed to addressing pressing sustainability challenges.

“By working together in these Solution Areas across disciplines and with collaborators beyond the university, we maximize our ability to have positive impacts on the timeframe and scale needed for the planet and humanity,” said Scott Fendorf , senior associate dean for integrative initiatives and the Terry Huffington Professor in the Stanford Doerr School of Sustainability. 

Workshops will be held with faculty and external experts to develop research strategies for each Solution Area on a rolling basis. Strategy workshops, opportunities to provide input on future Integrative Projects, and requests for proposals (open to all Stanford faculty) will be announced in the coming months.

Related message from leadership: Read a letter to faculty about the new Solution Areas from Dean Majumdar with Precourt Institute for Energy director William Chueh; Stanford Woods Institute for the Environment director Chris Field; Accelerator faculty director Yi Cui and executive director Charlotte Pera; and Integrative Initiatives associate dean Jenna Davis and senior associate dean Scott Fendorf.

Media Contacts

Josie garthwaite, explore more.

Stanford’s Sustainability Accelerator adds new targets

The Sustainability Accelerator in the Stanford Doerr School of Sustainability will support work in new areas including energy, climate adaptation, industry, and more.

• School planning

Solution Areas and research funding

A message from school leadership announcing solutions-oriented and scale-focused research funding opportunities to address pressing sustainability challenges.

Forecasting climate’s impact on a debilitating disease

In Brazil, climate and other human-made environmental changes threaten efforts to fight schistosomiasis, a widespread and debilitating parasitic disease. Stanford and Brazilian researchers have now developed models that can predict how disease risk will shift in response to environmental changes.

• Health and wellbeing

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Why Dropping the E in DEI Is a Mistake

• Enrica N. Ruggs
• Oscar Holmes IV

The Society for Human Resource Management’s decision to remove “equity” from its DEI framework sets a dangerous precedent that flies in the face of decades of research.

The Society for Human Resource Management (SHRM) has decided to remove “equity” from its inclusion, equity, and diversity (IE&D) framework, now promoting “inclusion and diversity” (I&D) instead. This decision sets a dangerous precedent that flies in the face of decades of research about DEI in the workplace. It undermines efforts to create equitable workplaces and ignores the vital role of equity in fostering fairness and addressing systemic barriers faced by marginalized groups. Instead of scaling back their focus on equity, companies should: 1) Commit to achievable equity goals; 2) Implement and track evidence-based DEI policies and practices; and 3) Establish accountability and transparency.

Recently, the Society for Human Resource Management (SHRM), a leading voice of HR professionals, announced that it was abandoning the acronym “IE&D” — inclusion, equity, and diversity — in favor of “I&D.”

• Enrica N. Ruggs , PhD is an associate professor of management in the C. T. Bauer College of Business at the University of Houston. She is a workplace diversity scholar who conducts research on reducing discrimination and bias in organizations and improving workplace experiences for individuals with marginalized identities.
• Oscar Holmes IV , PhD, SHRM-SCP is an associate professor of management at Rutgers University-Camden and the creator and host of the podcast Diversity Matters . In his research he examines how leaders can maximize productivity and well-being by fostering more inclusive workplaces.

Partner Center

• Vehicle Technologies Office
• About the Vehicle Technologies Office
• Technology Areas
• Technology Integration
• Reports and Publications

Office: Vehicle Technologies Office FOA number: DE-FOA-0003383 Link to apply:  Apply on EERE Exchange FOA Amount: $42,950,000

The U.S. Department of Energy (DOE) announced $43 million in funding for projects that will advance research, development, demonstration, and deployment (RDD&D) in several areas critical to the future of advanced batteries. The funding will drive innovations in low-cost electric vehicle (EV) battery electrode, cell, or pack manufacturing; improve battery safety and reduce cascading failures; and strengthen the domestic supply chain of inexpensive and abundant battery materials. This funding is aligned with strategies detailed in the U.S. National Blueprint for Transportation Decarbonization , which is a landmark interagency framework of strategies and actions to remove all emissions from the transportation sector by 2050, by advancing battery technologies that can power safe and efficient zero-emission EVs. 

DOE’s Vehicle Technologies Office (VTO) will supply the funding and build on the Office of Energy Efficiency and Renewable Energy’s leadership in RDD&D of new technologies leading to efficient, clean, and affordable mobility options. Topic areas in VTO’s Fiscal Year 2024 Batteries funding opportunity include:

• Improving 12V lead-acid battery service life and performance requirements to meet critical safety features while reducing cost. 
• Developing cell, module, pack, vehicle, or structural strategies that reduce cascading effects of thermal issues that could lead to EV fires. 
• Conducting research and development that would reduce the manufacturing cost at the electrode, cell, or pack level by at least 30% compared to the state of the art, and increasing throughput by at least 50% compared to the state of the art.
• Researching, fabricating, and testing silicon-based lithium battery cells that meet EV battery performance requirements.
• Developing high energy density cathodes containing metal chalcogenide, oxide, or halide materials that surpass the energy density of state-of-the art nickel cathodes. 

As part of this approach, VTO encourages the participation of underserved communities and underrepresented groups. Applicants are highly encouraged to include individuals from groups historically underrepresented in STEM on their project teams.

Learn more about this funding opportunity and other funding opportunities within DOE’s Office of Energy Efficiency and Renewable Energy .

Topic Areas

This FOA has five topic areas:

Topic 1 Improved 12 Volt Lead Acid Batteries for Safety-Critical Electric Vehicle Applications,  focused on improving the service life and performance requirements to meet critical safety features while reducing cost ($10 million).

Topic 2 Develop Vehicle or Structural Level Strategies to Reduce the Likelihood of the Cascading Effects of Electric Vehicle Fires, focused on university-led teams conducting research at the cell, pack, and vehicle level ($3.9 million).

Topic 3 Battery Electrode, Cell, and Pack Manufacturing Cost Reduction, focused on developing improved manufacturing technologies for EV battery electrodes, cells, and packs ($12.5 million). 

Topic 4 Silicon-Based Anodes for Lithium-Ion Batteries, focused on researching, fabricating, and testing lithium battery cells implementing silicon electrodes with a commercially available cathode technology to achieve cell and cost performance targets (more than 350 Wh/kg of usable energy with a cell cost target of less than $70/kWh) ($12.5 million). 

Topic 5 High Energy Density Conversion Cathodes,  focused on developing high energy density battery cells containing metal chalcogenide, oxide, or halide cathodes by solving key challenges for the cathode, electrolyte, electrode integrity, or safety ($4.05 million). 

Additional Information

• Download the full funding opportunity  on the EERE Exchange website.
• For FOA-specific support, contact  [email protected] .
• Sign up for the Office of Energy Efficiency and Renewable Energy (EERE) email list  to get notified of new EERE funding opportunities. Also sign up for the VTO GO! newsletter to stay current with the latest VTO news.

Transforming the understanding and treatment of mental illnesses.

Información en español

Celebrating 75 Years! Learn More >>

• Science News
• Meetings and Events
• Social Media
• Press Resources
• Email Updates
• Innovation Speaker Series

Office for Disparities Research and Workforce Diversity’s Disability, Equity, and Mental Health Research Webinar Series: Transforming Mental Health Disability Research Through Lived Experience Leadership and Co-Production

Date and time.

Additional upcoming webinars

• Framework for Understanding Structural Ableism in Health Care: September 9, 2024, 2:00–3:30 p.m. ET

This webinar will introduce a range of approaches to meaningfully integrate individuals with lived experiences of psychiatric disabilities into mental health research.

Nev Jones, Ph.D.   , will discuss proven methods of integration and co-creation, including co-production, integration, and co-leadership, and highlight ways in which the National Institute of Mental Health (NIMH) research portfolio would benefit from greater emphasis on and high standards for meaningful inclusion. 

About the speakers

Dr. Jones is an assistant professor in the School of Social Work at the University of Pittsburgh, and an interdisciplinary mental health services researcher focused on serious mental illness (SMI). Dr. Jones' work is grounded in her personal and family experiences of schizophrenia and associated experiences with public sector mental health services. She has been a steadfast champion of participatory methods and the integration and co-leadership of individuals and families directly impacted by psychiatric disability in research, policy, and implementation. She currently leads studies focused on early psychosis, community-based SMI services, civil commitment, and poverty and unemployment.

About the Office for Disparities Research and Workforce Diversity’s Disability, Equity, and Mental Health Research Webinar Series

The  Office for Disparities Research and Workforce Diversity is hosting a Disability, Equity, and Mental Health Research Webinar Series, which focuses on exploring the health disparities experienced by people with disability. These include disparities in mental health outcomes, which can be further compounded for those with intersecting identities. This webinar series brings together researchers, advocates, and people with lived experience to discuss the intersection of disability, mental health, and race and ethnicity. There will be a special emphasis on how disability intersects with other health disparities populations to contribute to mental health outcomes and ways to increase equity, accessibility, and inclusion and reduce disparities. Additionally, the series explores the notion of mental health conditions as a disability and considers the viewpoints of those with lived experience.

Registration

This webinar is free, but  registration is required   .

Sponsored by

National Institute of Mental Health, Office for Disparities Research and Workforce Diversity

For questions, please contact  Beshaun Davis, Ph.D. , Program Director,  Mental Health of Minoritized Populations Research , Office for Disparities Research and Workforce Diversity

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Americans’ views of offensive speech aren’t necessarily clear-cut

About six-in-ten U.S. adults (62%) say that “people being too easily offended by things others say” is a major problem in the country today.

In a separate question, 47% say that “people saying things that are very offensive to others” is a major problem, according to a Pew Research Center survey conducted in April.

Pew Research Center conducted this analysis to understand Americans’ views on whether offensive speech – and people being too easily offended by what others say – are major problems for the country. For this analysis, we surveyed 8,709 U.S. adults from April 8 to 14, 2024.

Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology . Here are the questions used for this analysis , along with responses, and its methodology .

There are substantial differences in these views between Republicans and Democrats.

• Eight-in-ten Republicans and Republican-leaning independents say people being too easily offended by what others say is a major problem. By comparison, 45% of Democrats and Democratic leaners say the same.
• In contrast, Democrats are more likely than Republicans to say that people saying things that are very offensive is a major problem in the country today. A 59% majority of Democrats say this, compared with 34% of Republicans.

Looking at Americans’ views on these two questions together, about a third (32%) say that people being too easily offended by things others say and people saying very offensive things to others are both major problems.

About as many Americans (30%) say people taking offense too easily is a major problem, but very offensive speech is not. A much smaller share (15%) say that people saying very offensive things is a major problem, but people too easily taking offense isn’t. And another 23% say that neither is a major problem in the country.

Sizable shares within both parties say both issues are major problems – 30% of Republicans and 32% of Democrats say this.

However, half of Republicans, compared with just 12% of Democrats, say people being too easily offended is a major problem, but people saying very offensive things isn’t. Slightly more than half of conservative Republicans (53%) hold this combination of views, along with 44% of moderate and liberal Republicans.

By contrast, about a quarter of Democrats (26%) – and a third of liberal Democrats – say people saying very offensive things is a major problem, but people being too easily offended is not. Just 4% of Republicans hold this combination of views.

Another 29% of Democrats, but just 15% of Republicans, say neither of these is a major problem.

There are also significant demographic differences in attitudes about offensive speech.

Race and ethnicity

While at least half of Americans across racial and ethnic groups say being too easily offended is a major problem in the country, White adults are particularly likely to say this. Nearly two-thirds of White adults (65%) say this is a major problem, as do 59% of Hispanic, 59% of Asian and 50% of Black adults.

No more than about one-in-ten in any of these groups say people getting offended too easily is not a problem in the country today.

Conversely, Black (63%), Asian (58%) and Hispanic (55%) adults are more likely than White adults (42%) to say that people saying very offensive things to others is a major problem.

Men (62%) and women (63%) are about equally likely to say people being too easily offended is a major problem.

But women (54%) are far more likely than men (40%) to say offensive speech is a major problem.

Within political parties, there are some differences by gender, race and ethnicity on these questions.

On whether people being too easily offended is a major problem:

• Hispanic Republicans (71%) are less likely than White Republicans (83%) to say this is a major problem. (The sample size for Black and Asian Republicans is too small to evaluate these groups individually.)
• There are no gaps between men and women in either party.

On whether offensive speech is a major problem:

• Democratic and Republican women are more likely than men in their parties to say offensive speech is a major problem. Among Democrats, 63% of women and 54% of men say this. And in the GOP, 43% of women and 27% of men say the same.
• While roughly two-thirds of Black (67%), Hispanic (65%) and Asian Democrats (64%) say this is a major issue, a narrower majority of White Democrats (54%) share that view.

Note: This is an update of a post originally published Dec. 14, 2021. Here are the questions used for this analysis , along with responses, and its methodology .

• Discrimination & Prejudice
• Free Speech & Press
• Political Ideals & Systems

J. Baxter Oliphant is a senior researcher focusing on politics at Pew Research Center .

Many adults in East and Southeast Asia support free speech, are open to societal change

Americans’ views of technology companies, most americans say a free press is highly important to society, ­most americans favor restrictions on false information, violent content online, freedom of speech and lgbt rights: americans’ views of issues in supreme court case, most popular.

901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center