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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Research Study Types

There are many different types of research studies, and each has distinct strengths and weaknesses. In general, randomized trials and cohort studies provide the best information when looking at the link between a certain factor (like diet) and a health outcome (like heart disease).

Laboratory and Animal Studies

These are studies done in laboratories on cells, tissue, or animals.

• Strengths: Laboratories provide strictly controlled conditions and are often the genesis of scientific ideas that go on to have a broad impact on human health. They can help understand the mechanisms of disease.
• Weaknesses: Laboratory and animal studies are only a starting point. Animals or cells are not a substitute for humans.

Cross-Sectional Surveys

These studies examine the incidence of a certain outcome (disease or other health characteristic) in a specific group of people at one point in time. Surveys are often sent to participants to gather data about the outcome of interest.

• Strengths: Inexpensive and easy to perform.
• Weaknesses: Can only establish an association in that one specific time period.

Case-Control Studies

These studies look at the characteristics of one group of people who already have a certain health outcome (the cases) and compare them with a similar group of people who do not have the outcome (the controls). An example may be looking at a group of people with heart disease and another group without heart disease who are similar in age, sex, and economic status, and comparing their intakes of fruits and vegetables to see if this exposure could be associated with heart disease risk.

• Strengths: Case-control studies can be done quickly and relatively cheaply.
• Weaknesses: Not ideal for studying diet because they gather information from the past, which can be difficult for most people to recall accurately. Furthermore, people with illnesses often recall past behaviors differently from those without illness. This opens such studies to potential inaccuracy and bias in the information they gather.

Cohort Studies

These are observational studies that follow large groups of people over a long period of time, years or even decades, to find associations of an exposure(s) with disease outcomes. Researchers regularly gather information from the people in the study on several variables (like meat intake, physical activity level, and weight). Once a specified amount of time has elapsed, the characteristics of people in the group are compared to test specific hypotheses (such as a link between high versus low intake of carotenoid-rich foods and glaucoma, or high versus low meat intake and prostate cancer).

• Strengths: Participants are not required to change their diets or lifestyle as may be with randomized controlled studies. Study sizes may be larger than other study types. They generally provide more reliable information than case-control studies because they don’t rely on information from the past. Cohort studies gather information from participants at the beginning and throughout the study, long before they may develop the disease being studied. As a group, many of these types of studies have provided valuable information about the link between lifestyle factors and disease.
• Weaknesses: A longer duration of following participants make these studies time-consuming and expensive. Results cannot suggest cause-and-effect, only associations. Evaluation of dietary intake is self-reported.

Two of the largest and longest-running cohort studies of diet are the Harvard-based Nurses’ Health Study and the Health Professionals Follow-up Study.

If you follow nutrition news, chances are you have come across findings from a cohort called the Nurses’ Health Study . The Nurses’ Health Study (NHS) began in 1976, spearheaded by researchers from the Channing Laboratory at the Brigham and Women’s Hospital, Harvard Medical School, and the Harvard T.H. Chan School of Public Health, with funding from the National Institutes of Health. It gathered registered nurses ages 30-55 years from across the U.S. to respond to a series of questionnaires. Nurses were specifically chosen because of their ability to complete the health-related, often very technical, questionnaires thoroughly and accurately. They showed motivation to participate in the long-term study that required ongoing questionnaires every two years. Furthermore, the group provided blood, urine, and other samples over the course of the study.

The NHS is a prospective cohort study, meaning a group of people who are followed forward in time to examine lifestyle habits or other characteristics to see if they develop a disease, death, or some other indicated outcome. In comparison, a retrospective cohort study would specify a disease or outcome and look back in time at the group to see if there were common factors leading to the disease or outcome. A benefit of prospective studies over retrospective studies is greater accuracy in reporting details, such as food intake, that is not distorted by the diagnosis of illness.

To date, there are three NHS cohorts: NHS original cohort, NHS II, and NHS 3. Below are some features unique to each cohort.

NHS – Original Cohort

• Started in 1976 by Frank Speizer, M.D.
• Participants: 121,700 married women, ages 30 to 55 in 1976.
• Outcomes studied: Impact of contraceptive methods and smoking on breast cancer; later this was expanded to observe other lifestyle factors and behaviors in relation to 30 diseases.
• A food frequency questionnaire was added in 1980 to collect information on dietary intake, and continues to be collected every four years.
• Started in 1989 by Walter Willett, M.D., M.P.H., Dr.P.H., and colleagues.
• Participants: 116,430 single and married women, ages 25 to 42 in 1989.
• Outcomes studied: Impact on women’s health of oral contraceptives initiated during adolescence, diet and physical activity in adolescence, and lifestyle risk factors in a younger population than the NHS Original Cohort. The wide range of diseases examined in the original NHS is now also being studied in NHSII.
• The first food frequency questionnaire was collected in 1991, and is collected every four years.
• Started in 2010 by Jorge Chavarro, M.D., Sc.M., Sc.D, Walter Willett, M.D., M.P.H., Dr.P.H., Janet Rich-Edwards, Sc.D., M.P.H, and Stacey Missmer, Sc.D.
• Participants: Expanded to include not just registered nurses but licensed practical nurses (LPN) and licensed vocational nurses (LVN), ages 19 to 46. Enrollment is currently open.
• Inclusion of more diverse population of nurses, including male nurses and nurses from Canada.
• Outcomes studied: Dietary patterns, lifestyle, environment, and nursing occupational exposures that may impact men’s and women’s health; the impact of new hormone preparations and fertility/pregnancy on women’s health; relationship of diet in adolescence on breast cancer risk.

From these three cohorts, extensive research has been published regarding the association of diet, smoking, physical activity levels, overweight and obesity, oral contraceptive use, hormone therapy, endogenous hormones, dietary factors, sleep, genetics, and other behaviors and characteristics with various diseases. In 2016, in celebration of the 40 th  Anniversary of NHS, the  American Journal of Public Health’s  September issue  was dedicated to featuring the many contributions of the Nurses’ Health Studies to public health.

Growing Up Today Study (GUTS)

In 1996, recruitment began for a new cross-generational cohort called  GUTS (Growing Up Today Study) —children of nurses from the NHS II. GUTS is composed of 27,802 girls and boys who were between the ages of 9 and 17 at the time of enrollment. As the entire cohort has entered adulthood, they complete annual questionnaires including information on dietary intake, weight changes, exercise level, substance and alcohol use, body image, and environmental factors. Researchers are looking at conditions more common in young adults such as asthma, skin cancer, eating disorders, and sports injuries.

Randomized Trials

Like cohort studies, these studies follow a group of people over time. However, with randomized trials, the researchers intervene with a specific behavior change or treatment (such as following a specific diet or taking a supplement) to see how it affects a health outcome. They are called “randomized trials” because people in the study are randomly assigned to either receive or not receive the intervention. This randomization helps researchers determine the true effect the intervention has on the health outcome. Those who do not receive the intervention or labelled the “control group,” which means these participants do not change their behavior, or if the study is examining the effects of a vitamin supplement, the control group participants receive a placebo supplement that contains no active ingredients.

• Strengths: Considered the “gold standard” and best for determining the effectiveness of an intervention (e.g., dietary pattern, supplement) on an endpoint such as cancer or heart disease. Conducted in a highly controlled setting with limited variables that could affect the outcome. They determine cause-and-effect relationships.
• Weaknesses: High cost, potentially low long-term compliance with prescribed diets, and possible ethical issues. Due to expense, the study size may be small.

Meta-Analyses and Systematic Reviews

A meta-analysis collects data from several previous studies on one topic to analyze and combine the results using statistical methods to provide a summary conclusion. Meta-analyses are usually conducted using randomized controlled trials and cohort studies that have higher quality of evidence than other designs. A systematic review also examines past literature related to a specific topic and design, analyzing the quality of studies and results but may not pool the data. Sometimes a systematic review is followed by conducting a meta-analysis if the quality of the studies is good and the data can be combined.

• Strengths: Inexpensive and provides a general comprehensive summary of existing research on a topic. This can create an explanation or assumption to be used for further investigation.
• Weaknesses: Prone to selection bias, as the authors can choose or exclude certain studies, which can change the resulting outcome. Combining data that includes lower-quality studies can also skew the results.

A primer on systematic review and meta-analysis in diabetes research

Terms of use.

The contents of this website are for educational purposes and are not intended to offer personal medical advice. You should seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read on this website. The Nutrition Source does not recommend or endorse any products.

Types of Study Design

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Introduction

Study designs are frameworks used in medical research to gather data and explore a specific research question .

Choosing an appropriate study design is one of many essential considerations before conducting research to minimise bias and yield valid results .

This guide provides a summary of study designs commonly used in medical research, their characteristics, advantages and disadvantages.

Case-report and case-series

A case report is a detailed description of a patient’s medical history, diagnosis, treatment, and outcome. A case report typically documents unusual or rare cases or reports  new or unexpected clinical findings .

A case series is a similar study that involves a group of patients sharing a similar disease or condition. A case series involves a comprehensive review of medical records for each patient to identify common features or disease patterns. Case series help better understand a disease’s presentation, diagnosis, and treatment.

While a case report focuses on a single patient, a case series involves a group of patients to provide a broader perspective on a specific disease. Both case reports and case series are important tools for understanding rare or unusual diseases .

Advantages of case series and case reports include:

• Able to describe rare or poorly understood conditions or diseases
• Helpful in generating hypotheses and identifying patterns or trends in patient populations
• Can be conducted relatively quickly and at a lower cost compared to other research designs

Disadvantages

Disadvantages of case series and case reports include:

• Prone to selection bias , meaning that the patients included in the series may not be representative of the general population
• Lack a control group, which makes it difficult to conclude  the effectiveness of different treatments or interventions
• They are descriptive and cannot establish causality or control for confounding factors

Cross-sectional study

A cross-sectional study aims to measure the prevalence or frequency of a disease in a population at a specific point in time. In other words, it provides a “ snapshot ” of the population at a single moment in time.

Cross-sectional studies are unique from other study designs in that they collect data on the exposure and the outcome of interest from a sample of individuals in the population. This type of data is used to investigate the distribution of health-related conditions and behaviours in different populations, which is especially useful for guiding the development of public health interventions .

Example of a cross-sectional study

A cross-sectional study might investigate the prevalence of hypertension (the outcome) in a sample of adults in a particular region. The researchers would measure blood pressure levels in each participant and gather information on other factors that could influence blood pressure, such as age, sex, weight, and lifestyle habits (exposure).

Advantages of cross-sectional studies include:

• Relatively quick and inexpensive to conduct compared to other study designs, such as cohort or case-control studies
• They can provide a snapshot of the prevalence and distribution of a particular health condition in a population
• They can help to identify patterns and associations between exposure and outcome variables, which can be used to generate hypotheses for further research

Disadvantages of cross-sectional studies include:

• They cannot establish causality , as they do not follow participants over time and cannot determine the temporal sequence between exposure and outcome
• Prone to selection bias , as the sample may not represent the entire population being studied
• They cannot account for confounding variables , which may affect the relationship between the exposure and outcome of interest

Case-control study

A case-control study compares people who have developed a disease of interest ( cases ) with people who have not developed the disease ( controls ) to identify potential risk factors associated with the disease.

Once cases and controls have been identified, researchers then collect information about related risk factors , such as age, sex, lifestyle factors, or environmental exposures, from individuals. By comparing the prevalence of risk factors between the cases and the controls, researchers can determine the association between the risk factors and the disease.

Example of a case-control study

A case-control study design might involve comparing a group of individuals with lung cancer (cases) to a group of individuals without lung cancer (controls) to assess the association between smoking (risk factor) and the development of lung cancer.

Advantages of case-control studies include:

• Useful for studying rare diseases , as they allow researchers to selectively recruit cases with the disease of interest
• Useful for investigating potential risk factors for a disease, as the researchers can collect data on many different factors from both cases and controls
• Can be helpful in situations where it is not ethical or practical to manipulate exposure levels or randomise study participants

Disadvantages of case-control studies include:

• Prone to selection bias , as the controls may not be representative of the general population or may have different underlying risk factors than the cases
• Cannot establish causality , as they can only identify associations between factors and disease
• May be limited by the availability of suitable controls , as finding appropriate controls who have similar characteristics to the cases can be challenging

Cohort study

A cohort study follows a group of individuals (a cohort) over time to investigate the relationship between an exposure or risk factor and a particular outcome or health condition. Cohort studies can be further classified into prospective or retrospective cohort studies.

Prospective cohort study

A prospective cohort study is a study in which the researchers select a group of individuals who do not have a particular disease or outcome of interest at the start of the study.

They then follow this cohort over time to track the number of patients who develop the outcome . Before the start of the study, information on exposure(s) of interest may also be collected.

Example of a prospective cohort study

A prospective cohort study might follow a group of individuals who have never smoked and measure their exposure to tobacco smoke over time to investigate the relationship between smoking and lung cancer .

Retrospective cohort study

In contrast, a retrospective cohort study is a study in which the researchers select a group of individuals who have already been exposed to something (e.g. smoking) and look back in time (for example, through patient charts) to see if they developed the outcome (e.g. lung cancer ).

The key difference in retrospective cohort studies is that data on exposure and outcome are collected after the outcome has occurred.

Example of a retrospective cohort study

A retrospective cohort study might look at the medical records of smokers and see if they developed a particular adverse event such as lung cancer.

Advantages of cohort studies include:

• Generally considered to be the most appropriate study design for investigating the temporal relationship between exposure and outcome
• Can provide estimates of incidence and relative risk , which are useful for quantifying the strength of the association between exposure and outcome
• Can be used to investigate multiple outcomes or endpoints associated with a particular exposure, which can help to identify unexpected effects or outcomes

Disadvantages of cohort studies include:

• Can be expensive and time-consuming to conduct, particularly for long-term follow-up
• May suffer from selection bias , as the sample may not be representative of the entire population being studied
• May suffer from attrition bias , as participants may drop out or be lost to follow-up over time

Meta-analysis

A meta-analysis is a type of study that involves extracting outcome data from all relevant studies in the literature and combining the results of multiple studies to produce an overall estimate of the effect size of an intervention or exposure.

Meta-analysis is often conducted alongside a systematic review and can be considered a study of studies . By doing this, researchers provide a more comprehensive and reliable estimate of the overall effect size and their confidence interval (a measure of precision).

Meta-analyses can be conducted for a wide range of research questions , including evaluating the effectiveness of medical interventions, identifying risk factors for disease, or assessing the accuracy of diagnostic tests. They are particularly useful when the results of individual studies are inconsistent or when the sample sizes of individual studies are small, as a meta-analysis can provide a more precise estimate of the true effect size.

When conducting a meta-analysis, researchers must carefully assess the risk of bias in each study to enhance the validity of the meta-analysis. Many aspects of research studies are prone to bias , such as the methodology and the reporting of results. Where studies exhibit a high risk of bias, authors may opt to exclude the study from the analysis or perform a subgroup or sensitivity analysis.

Advantages of a meta-analysis include:

• Combine the results of multiple studies, resulting in a larger sample size and increased statistical power, to provide a more comprehensive and precise estimate of the effect size of an intervention or outcome
• Can help to identify sources of heterogeneity or variability in the results of individual studies by exploring the influence of different study characteristics or subgroups
• Can help to resolve conflicting results or controversies in the literature by providing a more robust estimate of the effect size

Disadvantages of a meta-analysis include:

• Susceptible to publication bias , where studies with statistically significant or positive results are more likely to be published than studies with nonsignificant or negative results. This bias can lead to an overestimation of the treatment effect in a meta-analysis
• May not be appropriate if the studies included are too heterogeneous , as this can make it difficult to draw meaningful conclusions from the pooled results
• Depend on the quality and completeness of the data available from the individual studies and may be limited by the lack of data on certain outcomes or subgroups

Ecological study

An ecological study assesses the relationship between outcome and exposure at a population level or among groups of people rather than studying individuals directly.

The main goal of an ecological study is to observe and analyse patterns or trends at the population level and to identify potential associations or correlations between environmental factors or exposures and health outcomes.

Ecological studies focus on collecting data on population health outcomes , such as disease or mortality rates, and environmental factors or exposures, such as air pollution, temperature, or socioeconomic status.

Example of an ecological study

An ecological study might be used when comparing smoking rates and lung cancer incidence across different countries.

Advantages of an ecological study include:

• Provide insights into how social, economic, and environmental factors may impact health outcomes in real-world settings , which can inform public health policies and interventions
• Cost-effective and efficient, often using existing data or readily available data, such as data from national or regional databases

Disadvantages of an ecological study include:

• Ecological fallacy occurs when conclusions about individual-level associations are drawn from population-level differences
• Ecological studies rely on population-level (i.e. aggregate) rather than individual-level data; they cannot establish causal relationships between exposures and outcomes, as the studies do not account for differences or confounders at the individual level

Randomised controlled trial

A randomised controlled trial (RCT) is an important study design commonly used in medical research to determine the effectiveness of a treatment or intervention . It is considered the gold standard in research design because it allows researchers to draw cause-and-effect conclusions about the effects of an intervention.

In an RCT, participants are randomly assigned to two or more groups. One group receives the intervention being tested, such as a new drug or a specific medical procedure. In contrast, the other group is a control group and receives either no intervention or a placebo .

Randomisation ensures that each participant has an equal chance of being assigned to either group, thereby minimising selection bias . To reduce bias, an RCT often uses a technique called blinding , in which study participants, researchers, or analysts are kept unaware of participant assignment during the study. The participants are then followed over time, and outcome measures are collected and compared to determine if there is any statistical difference between the intervention and control groups.

Example of a randomised controlled trial

An RCT might be employed to evaluate the effectiveness of a new smoking cessation program in helping individuals quit smoking compared to the existing standard of care.

Advantages of an RCT include:

• Considered the most reliable study design for establishing causal relationships between interventions and outcomes and determining the effectiveness of interventions
• Randomisation of participants to intervention and control groups ensures that the groups are similar at the outset, reducing the risk of selection bias and enhancing internal validity
• Using a control group allows researchers to compare with the group that received the intervention while controlling for confounding factors

Disadvantages of an RCT include:

• Can raise ethical concerns ; for example, it may be considered unethical to withhold an intervention from a control group, especially if the intervention is known to be effective
• Can be expensive and time-consuming to conduct, requiring resources for participant recruitment, randomisation, data collection, and analysis
• Often have strict inclusion and exclusion criteria , which may limit the generalisability of the findings to broader populations
• May not always be feasible or practical for certain research questions, especially in rare diseases or when studying long-term outcomes

Dr Chris Jefferies

• Yuliya L, Qazi MA (eds.). Toronto Notes 2022. Toronto: Toronto Notes for Medical Students Inc; 2022.
• Le T, Bhushan V, Qui C, Chalise A, Kaparaliotis P, Coleman C, Kallianos K. First Aid for the USMLE Step 1 2023. New York: McGraw-Hill Education; 2023.
• Rothman KJ, Greenland S, Lash T. Modern Epidemiology. 3 rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.

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Evidence-Based Medicine: Types of Studies

• What is Evidence-Based Practice?
• Question Types and Corresponding Resources
• Types of Studies
• Practice Guidelines
• Step 3: Appraise This link opens in a new window
• Steps 4-5: Apply & Assess

Experimental vs. Observational Studies

An observational study is a study in which the investigator cannot control the assignment of treatment to subjects because the participants or conditions are not directly assigned by the researcher.

• Examines predetermined treatments, interventions, policies, and their effects
• Four main types: case series , case-control studies , cross-sectional studies , and cohort studies

In an experimental study , the investigators directly manipulate or assign participants to different interventions or environments

Experimental studies that involve humans are called clinical trials . They fall into two categories: those with controls, and those without controls.

• Controlled trials - studies in which the experimental drug or procedure is compared with another drug or procedure
• Uncontrolled trials - studies in which the investigators' experience with the experimental drug or procedure is described, but the treatment is not compared with another treatment

Definitions taken from: Dawson B, Trapp R.G. (2004). Chapter 2. Study Designs in Medical Research. In Dawson B, Trapp R.G. (Eds), Basic & Clinical Biostatistics, 4e . Retrieved September 15, 2014 from  https://accessmedicine.mhmedical.com/book.aspx?bookid=2724

Levels of Evidence Pyramid

Levels of Evidence Pyramid created by Andy Puro, September 2014

Additional Study Design Resources

Study Design 101 : Himmelfarb's tutorial on study types and how to find them

Study Designs  (Centre for Evidence Based Medicine, University of Oxford)

Learn about Clinical Studies  (ClinicalTrials.gov, National Institutes of Health)

Study Designs Guide  (Deakin University)

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Finding Types of Research

• Evidence-Based Practice

On This Guide

About this guide, understand evidence-based practice, understand research study types.

• Quantitative and Qualitative
• Systematic Reviews and Meta-Analysis
• Randomized Controlled Trials
• Observational Studies
• Literature Reviews
• Finding Research Tools This link opens in a new window

Throughout your schooling, you may need to find different types of evidence and research to support your topic of interest. This guide provides a high-level overview of research types, study designs, and types of data you may encounter when searching for information on your topic.

What is Evidence-Based Practice?

One of the requirements for your coursework is to find articles that support evidence-based practice. But what exactly is evidence-based practice? Evidence-based practice is a method that uses relevant and current evidence to plan, implement, and evaluate patient care. This definition is included in the video below, which explains all the steps of evidence-based practice in greater detail.

• Video - Evidence-based practice: What it is and what it is not. Medcom (Producer), & Cobb, D. (Director). (2017). Evidence-based practice: What it is and what it is not [Streaming Video]. United States of America: Producer. Retrieved from Alexander Street Press Nursing Education Collection

Primary/Original Research/Empirical Study Designs

Primary research, or what is also known as original or empirical research, is where researchers conduct the study and report on their findings. These type of studies include:

• Case-Control Studies
• Cohort Studies
• Cross-Sectional Studies

Studies that Synthesize Other Studies

Sometimes, a research study will look at the results of many studies, look for trends and draw conclusions. These types of studies include:

• Meta Analyses
• Systematic Reviews

Tip!  How do you determine the research article’s level of evidence or study design? First, look at the methodology section of the article. It should provide clues about what type of study design they are using and all the details of how they conducted the study.

The books below will help you understand the literature you find during your searches, including study design and evidence-based practice concepts.

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Ultimate Guide to the 7 Types of Research: Definitions, Examples, Advantages and Limitations!

About the article : different types of research.

Research is a fundamental aspect of any field of study, providing a systematic approach to gather and analyze information. It plays a crucial role in expanding knowledge, solving problems, and making informed decisions. Understanding the different types of research is essential for researchers to choose the most appropriate method for their study.

In this article, we will explore the various types of research methods commonly used in academic and professional settings. Each type of research has its own unique characteristics, strengths, and limitations. By gaining a comprehensive understanding of these research types, researchers can effectively design and conduct their studies to achieve their objectives.

Exploratory Research

Exploratory research is a type of research that is used to investigate a problem that is not clearly defined and gain a better understanding of the existing problem. It is often conducted when a researcher has just begun an investigation and wishes to understand the topic generally.

There are two main methods of conducting exploratory research: primary research and secondary research. Primary research involves collecting new data directly from the source, while secondary research involves analyzing existing data that has already been collected by others.

Under these two broad types, various methods can be employed to gather information. These methods include surveys, interviews , focus groups, observations, and case studies. Each method has its own advantages and disadvantages, and the choice of method depends on the nature of the research question and the available resources.

Exploratory research is valuable because it helps researchers gain insights and generate hypotheses for further investigation. It allows them to explore new areas of study and discover potential relationships between variables. However, it is important to note that exploratory research does not provide definitive answers or conclusive results. Instead, it lays the foundation for more in-depth research and helps researchers refine their research questions and methodologies.

Descriptive Research

Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. It involves observing and measuring without manipulating variables, allowing researchers to identify characteristics, trends, and correlations. The main goal of descriptive research is to provide a detailed description of the population or phenomenon being studied. This type of research focuses on answering questions such as how, what, when, and where.

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey. Observational research involves observing and recording behavior in its natural setting. Case study research involves in-depth analysis of a single individual, group, or situation. Survey research involves collecting data from a sample of individuals through questionnaires or interviews.

Descriptive research is particularly useful when researchers want to describe specific behaviors, characteristics, or trends as they occur in the environment. It provides a foundation for further research and can help generate hypotheses for future studies.

However, one limitation of descriptive research is that it does not establish causal relationships between variables. It can only provide a snapshot of the current state of the population or phenomenon being studied. Despite this limitation, descriptive research plays a crucial role in understanding and describing various aspects of the world around us.

Experimental Research

Experimental research is a quantitative research method with a scientific approach. It is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This type of research design is popular in scientific experiments, social sciences, medical science, etc. Experimental research involves manipulating one or more variables to observe the effect on another variable. It aims to establish cause-and-effect relationships between variables. The researcher carefully controls and manipulates the independent variable(s) while measuring the dependent variable(s).

There are two broad categories of experimental research designs: true experimental designs and quasi-experimental designs. True experimental designs involve random assignment of participants to different groups and manipulation of the independent variable. Quasi-experimental designs lack random assignment but still involve manipulation of the independent variable.

One advantage of experimental research is its ability to establish causal relationships. By manipulating variables and controlling extraneous factors, researchers can determine whether changes in the independent variable(s) cause changes in the dependent variable(s). This allows for a more confident understanding of cause and effect.

Another advantage of experimental research is its versatility. It can be used in various fields and disciplines, allowing researchers to investigate a wide range of phenomena. Whether it’s testing the effectiveness of a new drug, studying the impact of different teaching methods, or exploring the relationship between variables, experimental research provides a powerful tool for scientific inquiry.

However, experimental research also has some limitations. One limitation is the potential for artificiality. In a controlled laboratory setting, variables may be manipulated in a way that does not fully reflect real-world conditions. This can limit the generalizability of the findings to real-life situations. Additionally, experimental research may face ethical considerations. Manipulating variables and potentially exposing participants to certain conditions can raise ethical concerns. Researchers must ensure that the benefits of the study outweigh any potential risks or harm to participants.

Correlational Research

Correlational research is a type of non-experimental research that focuses on observing and measuring the relationship between two or more variables. Unlike experimental research, the researcher does not control or manipulate the variables in correlational research. The main purpose of correlational research is to determine if there is a statistical relationship between the variables being studied. It involves comparing two variables and data sources, assessing the relationship between them, and identifying any trends or patterns.

There are several types of correlational studies that can be conducted. One type is positive correlation, which occurs when an increase in one variable is associated with an increase in another variable. For example, there may be a positive correlation between income and education level, meaning that as income increases, education level also tends to increase.

On the other hand, negative correlation refers to a relationship where an increase in one variable is associated with a decrease in another variable. An example of negative correlation could be the relationship between hours spent studying and test scores. As the number of hours spent studying increases, test scores tend to decrease. Lastly, zero correlation indicates that there is no relationship between the variables being studied. This means that changes in one variable do not affect the other variable. For instance, there may be zero correlation between shoe size and intelligence.

Correlational research is commonly used in various fields, including psychology, sociology, and marketing. It provides valuable insights into the relationships between variables and helps researchers understand the patterns and trends in data. However, correlational research has its limitations. Since it does not involve manipulation of variables, it cannot establish causation. It can only identify associations between variables. Additionally, correlational research relies on the accuracy and reliability of the data collected, which can be influenced by various factors.

Causal-Comparative Research

Causal-comparative research is a methodology used to identify cause-effect relationships between independent and dependent variables. It is a type of research method where the researcher tries to find out if there is a causal effect relationship between two or more groups or variables.

The main objective of causal-comparative research is to determine the cause or reason for pre-existing differences in groups of individuals. This research design involves comparing groups that have already been formed based on a specific characteristic or condition. The researcher then analyzes the differences between these groups to identify any causal relationships.

There are two types of causal-comparative research designs: retrospective and prospective. Retrospective causal-comparative research looks at past events or conditions to determine the cause-effect relationship. On the other hand, prospective causal-comparative research looks at current or future events or conditions to identify the causal relationship.

One example of causal-comparative research is a study comparing the critical thinking skills of students who were taught using the inquiry method versus those who were taught using the lecture method. The researcher would compare the two groups of students and analyze the differences in their critical thinking abilities to determine if the teaching method had a causal effect on their skills.

Causal-comparative research has its advantages and disadvantages. One advantage is that it allows researchers to study cause-effect relationships in situations where it is not possible or ethical to manipulate variables. It also provides valuable insights into the factors that contribute to differences between groups.

However, a limitation of causal-comparative research is that it cannot establish a cause-effect relationship with certainty, as there may be other variables or factors that influence the observed differences between groups.

Qualitative Research

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data, qualitative research deals with data types such as text, audio, images, and video, focusing on the variety of human experiences and perspectives.

There are different types of qualitative research methods that researchers can use depending on their study requirements. Some common qualitative research methods include in-depth interviews, focus groups, ethnographic research, content analysis, and case study. In-depth interviews involve conducting one-on-one interviews with participants to gather detailed information about their experiences, opinions, and perspectives. This method allows researchers to delve deep into the thoughts and feelings of individuals and gain a comprehensive understanding of their experiences.

Focus groups involve bringing together a small group of participants to discuss a specific topic or issue. The group dynamic allows for the exploration of different perspectives and the generation of rich and diverse insights. Focus groups are particularly useful for understanding social interactions and group dynamics. Ethnographic research involves immersing the researcher in the natural environment of the participants to observe and understand their behaviors, beliefs, and cultural practices. This method allows for a holistic understanding of the social and cultural context in which individuals operate.

Content analysis involves systematically analyzing textual, audio, or visual data to identify patterns, themes, and meanings. This method is often used to analyze documents, media content, or online discussions to gain insights into societal trends, attitudes, or representations. Case study research involves in-depth investigation of a specific individual, group, or organization. Researchers collect and analyze multiple sources of data to gain a comprehensive understanding of the case under study. Case studies are particularly useful for exploring complex phenomena or unique situations.

Qualitative research provides several advantages. It allows researchers to explore complex and nuanced phenomena in depth, providing rich and detailed insights. It also allows for flexibility and adaptability in the research process, as researchers can adjust their approach based on emerging findings. Additionally, qualitative research is often used to generate hypotheses or theories that can be further tested using quantitative research methods.

However, qualitative research also has some limitations. The findings are often context-specific and may not be generalizable to a larger population. The subjective nature of qualitative data collection and analysis can introduce bias and interpretation challenges. Qualitative research also requires significant time and resources, as data collection and analysis can be time-consuming and labor-intensive.

Quantitative Research

Quantitative research is a type of research that involves collecting and analyzing numerical data to describe characteristics, find correlations, or test hypotheses. It is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, and reliance on prior studies.

There are two main types of quantitative research: primary and secondary. Primary quantitative research involves collecting data directly from the source, such as through surveys or experiments. Secondary quantitative research, on the other hand, involves analyzing existing data that has been collected by someone else.

Quantitative research methods can be used to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, and more. It provides a systematic and objective approach to studying phenomena and allows for statistical analysis to draw conclusions.

There are several types of quantitative research designs that can be used, depending on the research objectives . These include descriptive research, correlational research, causal-comparative research, and experimental research as per explained above.

In conclusion, understanding the different types of research is essential for conducting effective and meaningful studies. Each type of research has its own strengths and limitations, and researchers must carefully consider which approach is the most appropriate for their specific research question and objectives. It is important to recognize that research is an iterative process, and different types of research may be used at different stages of a study.

In summary, the various types of research offer different perspectives and methodologies for investigating and understanding the world around us. By utilizing a combination of these approaches, researchers can gain a comprehensive understanding of complex phenomena and make meaningful contributions to their fields of study.

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Home > Blog > Tips for Online Students > A Comprehensive Guide to Different Types of Research

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A Comprehensive Guide to Different Types of Research

Updated: June 19, 2024

Published: June 15, 2024

When embarking on a research project, selecting the right methodology can be the difference between success and failure. With various methods available, each suited to different types of research, it’s essential you make an informed choice. This blog post will provide tips on how to choose a research methodology that best fits your research goals .

We’ll start with definitions: Research is the systematic process of exploring, investigating, and discovering new information or validating existing knowledge. It involves defining questions, collecting data, analyzing results, and drawing conclusions.

Meanwhile, a research methodology is a structured plan that outlines how your research is to be conducted. A complete methodology should detail the strategies, processes, and techniques you plan to use for your data collection and analysis.

Research Methods

The first step of a research methodology is to identify a focused research topic, which is the question you seek to answer. By setting clear boundaries on the scope of your research, you can concentrate on specific aspects of a problem without being overwhelmed by information. This will produce more accurate findings. 

Along with clarifying your research topic, your methodology should also address your research methods. Let’s look at the four main types of research: descriptive, correlational, experimental, and diagnostic.

Descriptive Research

Descriptive research is an approach designed to describe the characteristics of a population systematically and accurately. This method focuses on answering “what” questions by providing detailed observations about the subject. Descriptive research employs surveys, observational studies , and case studies to gather qualitative or quantitative data. 

A real-world example of descriptive research is a survey investigating consumer behavior toward a competitor’s product. By analyzing the survey results, the company can gather detailed insights into how consumers perceive a competitor’s product, which can inform their marketing strategies and product development.

Correlational Research

Correlational research examines the statistical relationship between two or more variables to determine whether a relationship exists. Correlational research is particularly useful when ethical or practical constraints prevent experimental manipulation. It is often employed in fields such as psychology, education, and health sciences to provide insights into complex real-world interactions, helping to develop theories and inform further experimental research.

An example of correlational research is the study of the relationship between smoking and lung cancer. Researchers observe and collect data on individuals’ smoking habits and the incidence of lung cancer to determine if there is a correlation between the two variables. This type of research helps identify patterns and relationships, indicating whether increased smoking is associated with higher rates of lung cancer.

Experimental Research

Experimental research is a scientific approach where researchers manipulate one or more independent variables to observe their effect on a dependent variable. This method is designed to establish cause-and-effect relationships. Fields like psychology , medicine, and social sciences frequently employ experimental research to test hypotheses and theories under controlled conditions. 

A real-world example of experimental research is Pavlov’s Dog experiment. In this experiment, Ivan Pavlov demonstrated classical conditioning by ringing a bell each time he fed his dogs. After repeating this process multiple times, the dogs began to salivate just by hearing the bell, even when no food was presented. This experiment helped to illustrate how certain stimuli can elicit specific responses through associative learning.

Diagnostic Research

Diagnostic research tries to accurately diagnose a problem by identifying its underlying causes. This type of research is crucial for understanding complex situations where a precise diagnosis is necessary for formulating effective solutions. It involves methods such as case studies and data analysis and often integrates both qualitative and quantitative data to provide a comprehensive view of the issue at hand. 

An example of diagnostic research is studying the causes of a specific illness outbreak. During an outbreak of a respiratory virus, researchers might conduct diagnostic research to determine the factors contributing to the spread of the virus. This could involve analyzing patient data, testing environmental samples, and evaluating potential sources of infection. The goal is to identify the root causes and contributing factors to develop effective containment and prevention strategies.

Using an established research method is imperative, no matter if you are researching for marketing , technology , healthcare , engineering, or social science. A methodology lends legitimacy to your research by ensuring your data is both consistent and credible. A well-defined methodology also enhances the reliability and validity of the research findings, which is crucial for drawing accurate and meaningful conclusions. 

Additionally, methodologies help researchers stay focused and on track, limiting the scope of the study to relevant questions and objectives. This not only improves the quality of the research but also ensures that the study can be replicated and verified by other researchers, further solidifying its scientific value.

How to Choose a Research Methodology

Choosing the best research methodology for your project involves several key steps to ensure that your approach aligns with your research goals and questions. Here’s a simplified guide to help you make the best choice.

Understand Your Goals

Clearly define the objectives of your research. What do you aim to discover, prove, or understand? Understanding your goals helps in selecting a methodology that aligns with your research purpose.

Consider the Nature of Your Data

Determine whether your research will involve numerical data, textual data, or both. Quantitative methods are best for numerical data, while qualitative methods are suitable for textual or thematic data.

Understand the Purpose of Each Methodology

Becoming familiar with the four types of research – descriptive, correlational, experimental, and diagnostic – will enable you to select the most appropriate method for your research. Many times, you will want to use a combination of methods to gather meaningful data. 

Evaluate Resources and Constraints

Consider the resources available to you, including time, budget, and access to data. Some methodologies may require more resources or longer timeframes to implement effectively.

Review Similar Studies

Look at previous research in your field to see which methodologies were successful. This can provide insights and help you choose a proven approach.

By following these steps, you can select a research methodology that best fits your project’s requirements and ensures robust, credible results.

Completing Your Research Project

Upon completing your research, the next critical step is to analyze and interpret the data you’ve collected. This involves summarizing the key findings, identifying patterns, and determining how these results address your initial research questions. By thoroughly examining the data, you can draw meaningful conclusions that contribute to the body of knowledge in your field. 

It’s essential that you present these findings clearly and concisely, using charts, graphs, and tables to enhance comprehension. Furthermore, discuss the implications of your results, any limitations encountered during the study, and how your findings align with or challenge existing theories.

Your research project should conclude with a strong statement that encapsulates the essence of your research and its broader impact. This final section should leave readers with a clear understanding of the value of your work and inspire continued exploration and discussion in the field.

Now that you know how to perform quality research , it’s time to get started! Applying the right research methodologies can make a significant difference in the accuracy and reliability of your findings. Remember, the key to successful research is not just in collecting data, but in analyzing it thoughtfully and systematically to draw meaningful conclusions. So, dive in, explore, and contribute to the ever-growing body of knowledge with confidence. Happy researching!

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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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.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

11 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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EHD Researchers will Conduct Impact Study of First Tee Youth Programming

National nonprofit organization First Tee turns to researchers at Youth-Nex to evaluate the program’s model and youth development outcomes during out-of-school time.

Leslie Booren

August 14, 2024

(Photo contributed by First Tee.)

It is estimated that 10.2 million young people participate in out-of-school time (OST) activities including sports programs. Researchers at Youth-Nex will be partnering with First Tee, the national youth development organization, to evaluate their youth programming.

First Tee began in 1997 as a partnership among the LPGA, the Masters Tournament, the PGA of America, the PGA TOUR, and the US Golf Association (USGA) to make golf affordable for and accessible to all kids. The First Tee model introduces the game of golf and includes a life skills curriculum that supports the development of character strengths built through the game of golf, including positive self-identity, using good judgment and collaborating with others.

“We are excited to engage in a collaborative co-design process with First Tee,” said Ashlee Sjogren, a research assistant professor and principal investigator of the project. “We hope to better understand how the First Tee model relates to participant outcomes by examining metrics like program attendance, dosage, engagement, and frequency.”

The proposed study is an outcome evaluation that utilizes mixed methods research to investigate the model and associated outcomes for youth, like relationship quality and academic achievement.

Nancy Deutsch, director of Youth-Nex and the associate dean for faculty affairs at the School of Education and Human Development, is a co-principal investigator of this study and was part of the first impact study of the First Tee programming in 2005, also conducted at the University of Virginia.

“There is deep expertise in youth development, mentorship, and OST programming here at Youth-Nex,” Sjogren said. “This will be extremely valuable to both support and validate the work First Tee is doing across the United States with young people.”

This project is expected to continue into the Spring of 2026.

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Epidemiology and Clinical Research Design, Part 1: Study Types

Veena manja.

* Department of Internal Medicine, University at Buffalo, Buffalo, NY

† Department of Clinical Epidemiology and Biostatistics, McMasters University, Hamilton, Ontario, Canada

Satyan Lakshminrusimha

‡ Department of Pediatrics, University at Buffalo, Buffalo, NY

Selecting the best available preventive and therapeutic measures to avoid disability and death is an important goal for all health care practitioners. To achieve this goal, we need to perform studies that determine the value of these measures. In this article, we discuss the possible study designs that can be used for evaluating new approaches to prevention and treatment. The gold standard study design is a randomized, controlled, double-blind trial. In many instances, a randomized controlled trial may not be ethically or practically feasible. Other study types, such as case series, case-control studies, cohort studies, cross-sectional studies, crossover designs, and open-label studies, may be required to hypothesize and evaluate the link between an exposure or predictor variable and an outcome variable. Various study types pertaining to neonatal-perinatal medicine are reviewed in this article.

After completing this article the readers should be able to:

• Describe various study designs and their strengths and limitations.

Introduction

This article provides a brief overview of principles of epidemiology and clinical research design and covers all the topics required by the American Board of Pediatrics content outline pertaining to study types (and uses the same alphabetical numbering in the content outline) and systematic reviews. The reader is referred to other review books listed in the Suggested Reading section for a complete understanding of study types and epidemiology. ( 1 )( 2 )( 3 )( 4 )( 5 )

1) Study Types

Distinguishing among preclinical and phase I, II, III, and IV clinical trials.

IND = Investigational New Drug Application, FDA = Food and Drug Administration, NDA = New Drug Application, PI = principal investigator.

• Preclinical: The first step in development of a new drug is bench research using tissue cultures or animal models. Information on mechanism of action, efficacy, toxicity, pharmacokinetics, and pharmacodynamics is obtained from these studies.
• The drug sponsor (eg, a pharmaceutical company) applies an Investigational New Drug Application to the Food and Drug Administration (FDA). After approval is obtained from the FDA, the sponsor and principal investigator plan human trials.
• Phase I trial: This phase emphasizes safety. It typically involves 20 to 80 healthy volunteers. Occasionally, drugs that cause adverse effects may be evaluated in patients with end-stage disease (eg, anticancer drugs). Information on the drug’s most frequent adverse effects, drug metabolism, and excretion are obtained from phase I studies.
• Phase II trial: The goal of phase II trials is to obtain preliminary data on whether the drug works in patients who have a certain disease or condition. It typically involves hundreds of patients. In controlled trials, patients receiving the drug are compared with patients receiving a different treatment (usually a placebo or a different drug). Safety continues to be evaluated, and short-term adverse effects are studied.
• Phase III trial: Phase III trials typically involve hundreds or thousands of patients and gather more information on safety and efficacy.
• New Drug Application review: If the phase III trial is successful, the sponsor applies for an NewDrug Application to the FDA. This process includes a review of the proposed professional labeling and inspection of the manufacturing facility. If the review is favorable, the FDA may approve the drug for marketing.
• Phase IV or postmarketing surveillance: This surveillance is performed by the sponsor (typically the manufacturer), who submits periodic safety updates to the FDA. The MedWatch voluntary system enables physicians and consumers to report adverse events. If important risks are uncovered, risks are added to prescribing information, and drug use may be limited, or in rare instances the drug may be withdrawn from the market.
• Retrospective study : A retrospective study uses existing data that have been recorded for nonresearch (such as a clinical database) purposes. The baseline measurements and follow-up, including the exposure and the outcome, have all occurred in the past. The investigator starts at the time of exposure and selects a cohort of patients with the exposure and a comparable cohort without the exposure. Available medical records are used to follow up these patients to evaluate the probability of outcome in the cohort with exposure compared with those without. Patients in the 2 groups are matched based on baseline characteristics so that the risk of outcome is the same in the exposed and control groups except for the exposure of interest.
• Answers can be obtained rapidly (speed).
• Relatively inexpensive (cost).
• The investigator has no control over data collected. The existing data may be incomplete, inaccurate, or measured in ways that are not ideal for answering a research question. If the clinician has erroneously diagnosed a case of focal, spontaneous intestinal perforation as NEC in the NICU database, the investigator conducting a retrospective analysis of this database may assume the patient had NEC.
• It is difficult to ensure that the exposed and control groups have the same risk of outcome (other than the exposure). In addition to known baseline characteristics for which the exposed and control groups are matched, there may be unknown prognostic factors that are unevenly distributed in the 2 groups contributing to the outcome.
• Bias may be due to many factors. For example, selection bias or detection bias may lead to a spurious association between the predictor variable and outcome in the study sample that does not exist in the population.
• Confounding identifies real associations in the population, but these associations are not causal in the direction of interest.
• Loss to follow-up may influence results.
• Case series : Case reports and series are helpful in recognizing and describing new disease processes or rare manifestations and identify emerging health conditions. ( 7 ) A case series of the first 1000 patients with AIDS reported that 727 were homosexual or bisexual males and 236 were injection drug users. ( 8 ) It did not require a formal control group to conclude that these groups were at higher risk. ( 3 )
• Help describe rare manifestations and new diseases.
• Identify emerging health conditions.
• Better suited to describing the characteristics of the disease than identify causality.
• Purely descriptive and considered to be the weakest form of evidence.
• Misleading and may suggest a plausible causal relationship where none exists in real population.
• Cross-sectional study : The investigator makes all of his/her measurements at a single point in time or within a short period. ( 3 ) For example, the association between systemic blood pressure and postmenstrual age can be evaluated in a cross-sectional study in the NICU.
• Prevalence estimation is the proportion of patients who have a disease or condition at one point in time. The relative prevalence of feeding intolerance among human milk–fed preterm infants can be compared with formula-fed preterm infants.
• There is no waiting around for the outcome to occur (fast and inexpensive).
• Avoids the problem of loss to follow-up.
• A cross-sectional study conducted at the beginning of a cohort or clinical trial provides demographic and clinical characteristics at baseline.
• Cannot estimate incidence (the proportion who develop a disease or condition over time).
• Difficult to establish causal relationship (risk of possible spurious associations).
• Impractical for the study of rare diseases and requires a large sample size.

Case-control study. Cases (patients with necrotizing enterocolitis [NEC]) are identified and are matched with controls (eg, preterm infants of the same gestational age and sex born in the same month in the same neonatal intensive care unit [NICU]). Working backwards, investigators can evaluate multiple predictor variables.

PDA = patent ductus arteriosus, PICC = peripherally inserted central catheter, SGA = small for gestational age, TPN = total parenteral nutrition.

• Relatively inexpensive because of smaller sample sizes.
• Because the cases are chosen at the beginning of the study, rare outcomes can be studied using a relatively small sample size (eg, prone vs supine sleeping and sudden infant death syndrome).
• Multiple etiologic factors (antibiotic use, histamine 2 -blocker use, and human milk intake) and predictor variables can be studied. Case-control studies are useful for generating hypotheses about the causes of an outcome variable.
• Low internal validity (data collection is based on event recall).
• High chance of bias because of separate sampling of cases and controls and retrospective measurement of various predictor variables.
• Cannot estimate incidence or prevalence.
• Only one outcome can be studied, whereas cohort and cross-sectional studies can evaluate multiple outcome variables.
• Longitudinal study ( 9 ): In longitudinal studies, a group of individuals are identified at the outset and are followed up over time. Data collection occurs at repeated predetermined intervals. Cohort studies and repeated cross-sectional studies are longitudinal studies.
• Determine causality.
• Monitor trends.
• Time-consuming.

Cohort study. Preterm infants exposed to formula and not exposed to formula born in 2014 form the cohort for a prospective cohort study. They are followed up longitudinally for development of asthma by age 10 years. In a retrospective cohort study, preterm infants born in 2004 are evaluated over time to the current presence of disease (2014). Confounding factors (variables other than the one of interest, eg, feeding) may influence the primary outcome (eg, family history of atopy, socioeconomic status, and severity of bronchopulmonary dysplasia [BPD]). Losses to follow-up may bias the results of a cohort study.

Randomized controlled and double-blinded clinical trial (modified from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Donor Human Milk and Neurodevelopmental Outcomes in Very Low Birth Weight [VLBW] Infants trial; http://clinicaltrials.gov/ct2/show/ {"type":"clinical-trial","attrs":{"text":"NCT01232725","term_id":"NCT01232725"}} NCT01232725 ). Access to maternal breast milk (MBM) cannot be randomized. Hence, infants with access to MBM are excluded from the trial. Preterm infants without access to MBM form the study population and are randomized to donor human milk and formula. Because the color of these 2 products is different, it is delivered in amber syringes and bottles to blind the caregivers and parents. A double-blind approach indicates that both investigators or clinical team and parents or patients are unaware of the allocation. The primary outcome variable is neurodevelopmental outcome at age 2 years. Patients may be lost to follow-up. A primary outcome and several secondary outcomes may be evaluated. A blinded randomized controlled trial is the gold standard and provides strong direct evidence between exposure and outcome but is time-consuming and expensive.

• Can measure incidence.
• Establish temporal association (and strengthens the basis of inferring a causal basis of an association).
• Good for common diseases.
• Good for rare exposures.
• Possible to study associations of an exposure (human milk feeds) with several outcome variables (eg, asthma, neurodevelopmental outcome, and obesity). ( 12 )
• Expensive because of large sample size and long follow-up.
• Not good for rare diseases.
• Confounding factors (factors other than the exposure that might influence the outcome).
• Bias in assessment of the outcome: the pediatrician making the diagnosis of asthma in an infant born preterm knows the history of exposure to infant formula and is aware of the hypothesis being tested.
• Information bias or recall bias: the quality of information obtained from exposed (formula fed) and unexposed (exclusively breastfed) preterm infants may be different. A difference in educational background of the mother may alter ability to recall and provide accurate information.
• Biases from lack of response due to loss to follow-up.
• Analytic bias: epidemiologists and statisticians may have a strong preconception that breast milk is protective against asthma.
• The advantage of the retrospective cohort design is that it is less expensive and fast. The baseline measurements are already made, and the follow-up period is complete.
• The main disadvantages are limited control over the approach to sampling and follow up of the population and over the nature and quality of baseline measurements. The existing data may be incomplete, inaccurate, or measured in ways that are not ideal for answering the research question. ( 3 )
• Random assignment is expected to equally allocate all known and unknown predictor variables in the 2 groups, creating 2 groups with similar prognosis on average. The patients are placed into 2 categories: intervention group (receiving the active treatment, eg, donor human milk) and control group (receiving usual care or inactive treatment, eg, formula feeds). This example illustrates the fact that randomization is not possible in all circumstances. It is not ethically and practically possible to randomize breastfeeding and formula feeding among preterm infants.
• Fetal tracheal occlusion is an intervention to improve lung size in congenital diaphragmatic hernia. However, fetal sham surgery increases the risk of preterm labor and is not ethical. Hence, the control group may not undergo a sham surgery.
• In some cases, blinding is not technically possible. In the cooling trial evaluating infants with moderate to severe encephalopathy, allocation is not blinded because the clinical team and parents can easily assess the infant’s temperature.
• Inclusion criteria should produce a sufficient number of participants (large sample size of very low birth weight [VLBW] infants) who have a high enough rate of primary outcome (death or neurodevelopmental disability) to achieve adequate power to find a clinically significant effect of the intervention on outcome.
• Blocked randomization is a technique to ensure that the number of participants is equally distributed among the study groups. In a study that involves 60 participants, the block size may be 6. Randomization proceeds normally within each block of 6 until 3 VLBW infants are randomized to donor milk or formula group, after which participants are automatically assigned to the other group until the block of 6 is completed. This way the maximum disproportion of allotment of infants to the donor milk and formula groups at a given time will be 3 or less (eg, after enrolling 33 patients, 18:15).
• Stratification of participants by a characteristic (such as gestational age or birth weight group, eg, ≤1,000 g vs 1,001–1,500 g) allows investigators to enroll a desired number of participants with a characteristic that may have an influence on the effect of the treatment or its generalizability.
• Stratified blocked randomization ensures that an important predictor of the outcome (birth weight and gestational age influence the combined outcome of death and neurodevelopmental disability) is more evenly distributed between the study groups than chance alone would dictate.
• Pragmatic clinical trials are randomized controlled trials that are designed to determine the risks, benefits, and costs of an intervention as they would occur in routine clinical practice. They include less restrictive inclusion criteria, a broader range of patients, and many clinical sites (including nonacademic sites) to simulate real-world settings.
• Ability to demonstrate causality—a properly designed randomized, blinded trial can provide the most definitive causal inference of all study designs.
• Minimizes the influence of confounding variables.
• More than one outcome can be measured —usually a single primary outcome and a few secondary outcomes can be evaluated.
• Time-consuming and expensive.
• May expose participants to potential harm.
• Not every research question is amenable to randomized study design. For example, as mentioned above, the study depicted in Figure 3 cannot be performed because breastfeeding is a personal choice. A similar study can be performed among preterm infants without access to maternal breast milk, randomizing these infants to donor human milk or formula ( Figure 4 ).
• To ethically conduct a randomized controlled trial, equipoise must exist between the investigative arm and the control arm. Equipoise is a situation in which it is not known which of the 2 possibilities (eg, donor milk or formula) is more likely to achieve the outcome variable (neurodevelopmental outcome at 2 years).
• The patients enrolled in a randomized controlled trial may not fully represent the target population for the intervention.
• Before-after study design (historical controls) ( 12 ): If randomization is not possible or will not be used, one possible study design is to compare people who received care before a program was established (donor milk availability for VLBW infants in a NICU) with those who received care after the program or health care measure became available on the outcome variable (incidence of NEC).
• Inexpensive and fast.
• Provide suggestion (although not conclusive) demonstrating the effectiveness of a health care intervention.
• Data obtained in each of the 2 periods are frequently not comparable in terms of quality or completeness. Often data collected after implementing the program is complete and of research quality. However, data collected before program implementation is from health care records designed and used only for clinical purposes.
• Other factors may have changed over time. The NICU may have instituted a bronchopulmonary dysplasia (BPD) prevention bundle or a central catheter–associated bloodstream infection bundle that may have an effect on primary outcome variable (NEC).

Unplanned crossover study in a randomized study of Nissen fundoplication vs lansoprazole medical management. Analysis by treatment would analyze patients who underwent Nissen fundoplication vs patients who were treated with lansoprazole alone. However, intent-to-treat analysis would compare patients randomized to fundoplication (all infants with light yellow onesies) to patients randomized to lansoprazole only (all infants with dark red onesies).

Planned crossover design. Preterm infants (<1,500-g birth weight [very low birth weight (VLBW)]) on exclusive maternal breast milk feeds were randomized to standard fortification to 24 kcal/oz and experimental fortification with medium chain triglyceride (MCT) oil for 1 week. At the end of the week, outcome variable (mean weight gain per day) was measured and compared between the 2 groups. After a washout period of 1 week to avoid residual carryover effect, group A was fortified with MCT oil and group B was fortified with liquid human milk fortifier. Within-group comparisons can be performed between 2 fortifications.

• Planned crossover studies are attractive and useful because they evaluate within-group and between-group comparisons ( Figure 6 ). Crossover design removes between-patient variation and requires fewer patients.
• Valid only if there is no residual carryover from the first therapy.
• Not applicable if the therapy is surgical or if it cures the disease.
• Washout period may deprive the patient of a useful therapy (such as fortification).
• Order in which therapy is given may elicit psychological responses and difference in physiologic maturity with increasing postnatal age may influence response.
• Open-label study is a type of clinical trial in which the researchers and participants (or parents) know which treatment is being administered. This contrasts with single-blind and double-blind designs. An open-label study may still be randomized.
• Strengths: A blinded trial is not possible in certain circumstances involving surgery (abdominal drain vs laparotomy for NEC or intestinal perforation) or physical intervention (optimizing cooling trial for hypoxic ischemic encephalopathy [randomization to 33.5°C or 32°C for 72 or 120 hours]; – duration of therapy and infant’s body temperature are known to caregivers, investigators, and parents).
• Limitations: A blinded trial is regarded as being less subject to bias than an open trial because it minimizes the effect of knowledge of treatment allocation on reporting of outcomes.
• Post hoc analysis (from Latin post hoc meaning “after this”): Post hoc analysis consists of looking at the data after the experiment has concluded for patterns that were not specified a priori. If the hypothesis was formulated after the data were analyzed, it is known as a post hoc hypothesis. Because spurious associations may be present just by chance, post hoc analysis should only be hypothesis generating and should be tested in future trials to confirm the effect seen.
• Strengths: A clinically relevant association may be detected during post hoc analysis. In a study evaluating inhaled nitric oxide in pulmonary hypertension, a finding associating respiratory alkalosis with later-onset sensorineural deafness may be detected by post hoc analysis. As noted above, however, this hypothesis ideally should be tested in a future trial before an association can be confirmed. However, such a trial may not be ethically appropriate.
• Limitations: In addition to caution regarding interpretation of a finding on post hoc analysis, if the number of analyses increases, some positive results may be due to chance. As the number of hypotheses increase, the α value should be adjusted (Bonferroni correction). So, if 2 hypotheses are being tested in the same sample, the α to assign significance may be .025. The risk of erroneous conclusion increases with post hoc analysis.
• Subgroup analyses are defined as comparisons between randomized groups in a subset of the trial cohort. The main reason for performing these analyses is to discover effect modification (interaction) in subgroups, for example, whether inhaled nitric oxide is more effective in reducing the incidence of death or BPD in preterm infants with birth weights greater than 1,000 g compared with infants with birth weights of 1,000 g or less. To preserve the value of randomization, subgroups should be defined by measurements that were made before randomization. The subgroup effect should be one of a small number of hypotheses tested. If a large number of hypotheses are tested, some of the statistically significant findings may be due to chance alone.
• To discover whether the effect of treatment is different based on sex, gestational age, or birth weight.
• Easy to misuse and can lead to wrong conclusions.
• Being smaller than the entire trial population, there may not be sufficient power to find important differences. ( 3 )
• The purpose of a systematic review: Systematic reviews summarize the available evidence relating to a specific clinical question. In addition to providing an overall estimate of treatment effect to guide clinical decisions, systematic reviews can also help to inform research by identifying the areas of uncertainty requiring further study and guide policy decisions based on the entire body of evidence.

Characteristics of a forest plot. This plot can be shown on a logarithmic scale (when the line of no effect is at 1) or on a linear scale (when the line of no effect is at 0). The squares represent individual treatment effects, and the diamond represents pooled effect. The width of the diamond represents the 95% confidence interval. If the diamond crosses the line of no effect, the meta-analysis does not significantly favor experimental or control group.

• On a forest plot, the treatment effect of each individual study is represented by a square. The point at the center of the square is the point estimate. The relative weight given to each study is represented by the size of the square (usually determined by the study size), the CI is represented by a horizontal line that runs through the center of the block. The pooled effect estimate is represented by a diamond at the end of all individual study estimates. The point estimate of the pooled effect is at the center of the diamond, and the CI is represented by the width of the diamond. If the diamond crosses the line of no effect, this indicates that after combining all relevant studies, there is no significant difference in effect in the treatment vs comparator.
• It is not always appropriate to pool results of individual studies in a meta-analysis. Data from different studies can be combined when the studies address a common question and measure and report outcomes similarly. Alternatively, if the studies are very dissimilar or if the studies are at a high risk of bias, leading to a low confidence in the estimate of effect, the data should not be statistically pooled.
• The combination provides a pooled quantitative estimate of the effects of the intervention, and the uncertainty associated with the result can be inferred using the CI. Combining studies results in greater power to detect treatment effects and decreases uncertainty (narrower CI and greater precision).
• The differences in included studies can be analyzed to explore differences in treatment effect in different study populations and settings.
• The comparison of different studies may lead to new ideas and hypotheses for future trials.
• Because a systematic review is a retrospective review, similar to other retrospective studies, it is at risk of bias.
• Use of explicit criteria and critical appraisal of the literature reduce the likelihood of a biased review.
• Not recognizing publication bias and bias in the conduct of the studies included in the review may lead to unreliable results. A thorough evaluation of the risk of bias of included studies and assessment of publication bias will limit this possibility. Publication bias is a distortion of the published literature that occurs when published studies are not representative of all studies that have been performed. This bias is secondary to a tendency to submit and publish positive results more often than negative results. ( 3 )
• Careful consideration should be given to the studies that are included in the meta-analysis. The results are a direct reflection of the studies included in the analysis. If the studies included are at a high risk of bias, and the results of the individual studies do not represent the true effect, combining these studies may result in increased precision of the wrong results, giving biased results credibility (garbage in, garbage out). Analyzing the data by different statistical methods may give different results with the same set of studies. Heterogeneity should be considered and explored in the results of the meta-analysis.

The second part of this review covers bias and confounding, causation, incidence and prevalence, screening, sensitivity analysis, and measurement and will appear in a subsequent issue of Neoreviews .

American Board of Pediatrics Neonatal-Perinatal Content Specification

• Understand the purpose of a systematic review.
• Understand the advantages of adding a meta-analysis to a systematic review.
• Interpret the results of a meta-analysis.
• Identify the limitations of a systematic review.
• Identify the limitations of a meta-analysis.
• Distinguish phase I, II, III, and IV clinical trials.
• Recognize a retrospective study.
• Understand the strengths and limitations of retrospective studies.
• Recognize a case series.
• Understand the strengths and limitations of case series.
• Recognize a cross-sectional study.
• Understand the strengths and limitations of cross-sectional studies.
• Recognize a case-control study.
• Understand the strengths and limitations of case-control studies.
• Recognize a longitudinal study.
• Understand the strengths and limitations of longitudinal studies.
• Recognize a cohort study.
• Understand the strengths and limitations of cohort studies.
• Recognize a randomized controlled study.
• Understand the strengths and limitations of randomized controlled studies.
• Recognize a before-after study.
• Understand the strengths and limitations of before-after studies.
• Recognize a crossover study.
• Understand the strengths and limitations of crossover studies.
• Recognize an open-label study.
• Understand the strengths and limitations of open-label studies.
• Recognize a post-hoc analysis.
• Understand the strengths and limitations of post hoc analyses.
• Recognize a subgroup analysis.
• Understand the strengths and limitations of subgroup analyses.

Abbreviations

Author Disclosure

Drs Manja and Lakshminrusimha have disclosed funding from grant 1R01HD072929-0 (S.L.) and that they are consultants and on the speaker bureau of Ikaria, Inc. This commentary does not contain a discussion of an unapproved/ investigative use of a commercial product/device.

Context Matters for Foundation Models in Biology

Just as words can have multiple meanings depending on the context of a sentence, proteins can play different roles in a cell based on their cellular environments. Advances in our understanding of protein and biomolecule functions have been propelled by recent breakthroughs in transformer-based models, such as large language models and generative pre-trained transformers, which automatically learn word semantics from diverse language contexts. Innovating a similar approach for protein functions—viewing them as distributions across various cellular contexts—could enhance the use of foundation models in biology. This would allow the models to dynamically adjust their outputs based on the biological contexts in which they operate. To this end, we have developed PINNACLE , a novel contextual AI model for single-cell biology that supports a broad array of biomedical AI tasks by tailoring its outputs to the cell type context in which the model is asked to make predictions. 

To glean the meaning of a word, we examine nearby words for context clues. For example, “buy an apple” and “grow an apple” yield different recommendations: the first phrase is used to refer to apple products, whereas the second is better associated with apple trees (Figure 1a). To resolve the role of a protein, we interrogate it in the context of the proteins with which it interacts and the cells in which it exists. For instance, H2AFX is a gene that can be involved in homologous recombination or end joining depending on its cellular context (Figure 1b).

Cellular context is critical to understanding protein function and developing molecular therapies. Still, modeling proteins across biological contexts, such as the cell types that they are activated in and the proteins they interact with, remains an algorithmic challenge. Current approaches are context-free: learning on a reference context-agnostic dataset, a single context at a time, or an integrated summary across multiple contexts. As a result, they cannot tailor outputs based on a given context, which can lead to poor predictive performance when applied to a context-specific setting or a never-before-seen context. We develop PINNACLE, a new geometric deep learning approach that generates context-aware protein representations to address these challenges.

Context-specific geometric deep learning PINNACLE model

PINNACLE is a novel geometric deep learning model that learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. By leveraging a multi-organ single-cell atlas Tabula Sapiens from CZ CELLxGENE Discover , we construct 156 cell type specific protein interaction networks that are maximally similar to the global reference protein interaction network while maintaining cell type specificity (left and middle panels of Figure 2). We additionally create a metagraph to capture the tissue hierarchy and cell type communication among the cell type specific protein interaction networks (right panel of Figure 2). There are four distinct edge types in the metagraph: cell type to cell type (i.e., cell type interaction), cell type to tissue, tissue to cell type (i.e., tissue membership of the cell type), and tissue to tissue (i.e., parent-child tissue relationship in a tissue ontology). This results in multi-scale networks representing protein, cell type, and tissue information in a unified data representation.

PINNACLE’s algorithm specifies graph neural message passing transformations on multi-scale protein interaction networks . It performs neural message passing with attention for each cell type specific protein interaction network (component 1 in Figure 3) and metagraph (component 2 in Figure 3), and aligns the protein and cell type embeddings using an attention bridge (component 3 in Figure 3). First, PINNACLE learns a trainable weight matrix, node embeddings, and attention weights for each cell type specific protein interaction network. They are optimized based on two protein-level tasks: link prediction (i.e., whether an edge exists between a pair of proteins) and cell type identity (i.e., which cell type a protein is activated in). Secondly, on the metagraph, PINNACLE learns edge type specific trainable weight matrices, node embeddings, and attention weights and aggregates the edge type specific node embeddings via another attention mechanism. These trainable parameters are optimized using edge type specific link prediction (i.e., whether a specific type of edge exists between a pair of nodes). Thirdly, PINNACLE learns attention weights to bridge protein and cell type embeddings. This attention bridge facilitates the propagation of neural messages from cell types and tissues to the cell type specific protein embeddings. It enables PINNACLE to generate a unified embedding space of proteins, cell types, and tissues. Further, the attention bridge enforces cellular and tissue organization of the latent protein space based on tissue hierarchy and cell type communication, enabling contextualization of protein representations.

Context-specific predictions

PINNACLE’s contextual representations can be adapted for diverse downstream tasks in which context specificity may play a significant role. Designing safe and effective molecular therapies is one such task that requires understanding the mechanisms of proteins across cell type contexts. We hypothesize that, in contrast to context-free protein representations, contextualized protein representations can enhance 3D structure-based protein representations for resolving immuno-oncological protein interactions (Figure 4) and facilitate the investigation of drugs’ effects across cell types (Figure 5).

Contextualizing 3D molecular structures of proteins using existing structure-based models is limited by the scarcity of structures captured in context-specific conformations. We show via demonstrative case studies that PINNACLE’s contextualized protein representations can improve structure-based predictions of binding (and non-binding) proteins (Figure 4a). For two immuno-oncological protein interactors, PD-1/PD-L1 and CTLA-4/B7-1, we generate embeddings of each protein using a state-of-the-art structure-based model, MaSIF. We aggregate these structure-based embeddings with a corresponding genomic-based context-free or contextualized protein embedding (i.e., from PINNACLE). By calculating a binding score (i.e., cosine similarity between a pair of protein embeddings), we find that contextualized embeddings enable better differentiation between binding and non-binding proteins (Figure 4b). This zero-shot analysis of contextualizing 3D structure-based representations exemplifies the potential of contextual learning to improve the modeling of molecular structures across biological contexts.

Nominating therapeutic targets with cell type resolution holds the promise of maximizing the efficacy and safety of a candidate drug. However, it is not possible with current models to systematically predict cell type specific therapeutic potential across all proteins and cell type contexts. By finetuning PINNACLE’s contextualized protein representations, we demonstrate that PINNACLE outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis (RA) and inflammatory bowel diseases (IBD). We can pinpoint cell type contexts with higher predictive capability than context-free models (Figure 5a). In collaboration with RA and IBD clinical experts, we find that the most predictive cell types are indeed relevant to RA and IBD. Further, examining predictions of individual proteins across cell types allows us to interrogate each candidate target’s therapeutic potential in each cell type context.

PINNACLE is a contextual AI model for representing proteins with cell type resolution. While we demonstrate PINNACLE’s capabilities through cell type specific protein interaction networks, the model can easily be re-trained on any protein network. Our study focuses on single-cell transcriptomic data of healthy individuals, but we expect that training disease-specific PINNACLE models can enable even more accurate predictions of candidate therapeutic targets across cell type contexts. PINNACLE’s ability to adjust its outputs based on the context in which it operates paves the way for large-scale context-specific predictions in biology.  

PINNACLE exemplifies the potential of contextual AI to mimic distinctly human behavior, operating within specific contexts. As humans, we naturally consider and utilize context without conscious effort in our daily interactions and decision-making processes. For instance, we adjust our language, tone, and actions based on the environment and the people we interact with. This inherent ability to dynamically adapt to varying contexts is a cornerstone of human intelligence. In contrast, many current biomedical AI models often lack this contextual adaptability. They tend to operate in a static manner, applying the same logic and patterns regardless of differing biological environments. This limitation can hinder their effectiveness and accuracy in biological systems where context plays a crucial role.

By integrating contextual awareness, models such as PINNACLE can transform biomedical AI. We envision that context-aware models will dynamically adjust their outputs based on the specific cellular environments they encounter, leading to more accurate and relevant predictions and insights. This advancement enhances the functionality of AI models in biology and brings them a step closer to emulating the nuanced and adaptable nature of human thought processes. 

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9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

Don't forget to share this post!

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New research led by UTHealth Houston sheds light on the behavioral and psychological symptoms of Alzheimer’s disease and related dementias

Written by: Laura Frnka-Davis | Updated: August 15, 2024

A study offering insights into understanding and managing the behavioral and psychological symptoms of Alzheimer’s disease and related dementias led by a team of UTHealth Houston researchers has been published in Alzheimer’s & Dementia , the journal of the Alzheimer’s Association.

Carolyn Pickering, PhD, RN, professor and Isla Carrol Turner Chair in Gerontological Nursing at Cizik School of Nursing at UTHealth Houston and a noted expert in dementia family caregiving, led the research applying an innovative framework to identify patterns of behavioral and psychological symptoms of dementia. Her research distinguishes between general profiles of symptoms common among different people, known as subsyndromes, and the way symptoms group together based on daily variations in symptoms experienced by individuals, referred to as symptom clusters.

“Behavioral and psychological symptoms of dementia can vary within the same person due to factors like the time of day, noise levels, and even hydration status,” said Pickering, first and corresponding author on the study, and head of the Pickering Caregiver Lab team, a research hub to advance the care of people with dementia. “Our study is significant because it offers a new way of thinking about how to manage symptoms for persons living with dementia that may be more effective than current approaches that tend to lump all symptoms together as one experience.”

The study followed 68 family members who live with and care for family members living with dementia. Caregivers reported daily on their loved ones’ 23 different symptoms related to dementia, including eating difficulties, uncooperativeness, delusions, depression, anxiety, apathy, and wandering, producing a total of 443 daily reports over the course of eight days. To analyze the data, Pickering and her team used a novel statistical method that considered the variation within each person and between different people.

Researchers noted a high occurrence of possible delirium in home care settings, usually associated with hospitals. The finding highlights the need for better awareness and education on how caregivers can manage delirium at home.

“By understanding and addressing both subsyndromes and symptom clusters, we can create new and targeted ways to help health care professionals and caregivers manage dementia symptoms more effectively,” said Pickering. “This comprehensive approach will improve the quality of care and support for individuals with dementia and their families.”

Funded by the Alzheimer’s Association, this research makes significant strides in managing behavioral and psychological symptoms of dementia.

Additional UTHealth Houston authors include Vicki Winstead, PhD, MA, senior program manager at the Pickering Caregiver Lab; Mustafa Yildiz, PhD, assistant professor at Cizik School of Nursing; and Andrew Pickering, PhD, associate professor at McGovern Medical School at UTHealth Houston. Other authors are Danny Wang and Maria Yefimova, lead nurse scientist at University of California San Francisco. 

Laura Frnka-Davis

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SLU Research: PTSD Is a Modifiable Risk Factor for Type 2 Diabetes, Related Adverse Outcomes

Bridjes O'Neil Communications Specialist [email protected] 314-282-5007

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ST. LOUIS — Patients with post-traumatic stress disorder (PTSD) and type 2 diabetes have worse glycemic control, increased risk of hospitalization, and poorer self-reported health compared with type 2 diabetes alone, according to a Saint Louis University study.

The study , published online Aug. 13 in JAMA Network Open , shows that treating PTSD is associated with better overall well-being and lower risk of some chronic health conditions, including type 2 diabetes. 

Jeffrey Scherrer, Ph.D., professor of family and community medicine and professor of psychiatry and behavioral neuroscience at Saint Louis University School of Medicine. Scherrer is also co-director of research for SLU’s Advanced HEAlth Data Research Institute. SLU file photo.

“To our knowledge, this is the first evidence that PTSD is a modifiable risk factor, albeit a modest one, for some adverse diabetes outcomes such as microvascular complications,” said Jeffrey Scherrer, Ph.D. , professor of family and community medicine and professor of psychiatry and behavioral neuroscience at Saint Louis University School of Medicine, the paper’s senior author. 

Scherrer is also co-director of research for SLU’s Advanced HEAlth Data ( AHEAD ) Research Institute, a center that addresses public health issues to improve patient health outcomes through data-driven innovation.

"This is further evidence that we should not separate mental from physical health. Treating the whole patient with comorbid PTSD and diabetes should address both conditions to optimize outcomes. Screening for and treating PTSD as part of diabetes care may lead to better clinical outcomes for both conditions," he said. 

In this retrospective study, Scherrer and his co-authors collected patient data from 2011 to 2022 from the Veterans Health Administration (VHA). The data sets were comprised of 10,002 VHA patients, ages 18 to 80, with comorbid PTSD and type 2 diabetes.

Scherrer and his co-authors observed that when patients’ PTSD improved to a level where they no longer met the criteria for PTSD, that improvement was associated with an 8% reduction in risk for microvascular complications compared to those who continued to meet the criteria associated with a PTSD diagnosis. Among those ages 18-49, but not among older patients, no longer meeting PTSD criteria was linked to a significantly lower risk of insulin initiation and all-cause mortality.

PTSD is a metabolic disease related to the body’s inflammatory response. Scherrer and his co-authors found that it is possible that physiological abnormalities in the hypothalamic-pituitary-adrenal axis, changes in metabolic hormones, poor diet and a lack of exercise could explain the connection between PTSD and prediabetes and type 2 diabetes.

Other authors include Joanne Salas, of the AHEAD Research Institute, Saint Louis University School of Medicine; Wenjin Wang, of the Department of Family and Community Medicine, Saint Louis University School of Medicine; Kenneth E. Freedland, Ph.D., of the Department of Psychiatry, Washington University School of Medicine; Patrick J. Lustman, Ph.D., of the Department of Psychiatry, Washington University School of Medicine; Paula P. Schnurr, Ph.D., of the National Center for PTSD, White River Junction, Vermont, and the Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire; Beth E. Cohen, M.D., of the Department of Medicine, University of California San Francisco School of Medicine and San Francisco VA Medical Center, San Francisco, California; Allan S. Jaffe, M.D. of the Department of Cardiovascular Medicine and Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota; Matthew J. Friedman, MD, Ph.D., of the Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.

This study was supported by grant R01HL160553 from the National Heart, Lung, and Blood Institute. 

Heavy cannabis use may increase risk of certain cancers, new study finds

Regular heavy marijuana use may increase a person’s risk of developing some head and neck cancers, a study published Thursday in JAMA Otolaryngology-Head & Neck Surgery found. 

The study found that heavy cannabis users were between 3.5 and 5 times more likely to develop head and neck cancers than those who didn’t use the drug regularly.

Head and neck cancers are cancers of the mouth, throat and nasal cavity and are twice as common in men than in women, according to the Centers for Disease Control and Prevention . 

The research adds to a somewhat muddy body of evidence that’s just beginning to explore the impact cannabis may have on cancer risk . 

“The cannabinoids themselves could be carcinogenic, and it could also be that the smoke itself has potential for carcinogenesis,” said lead study author Dr. Niels Kokot, a head and neck surgeon at Keck Medicine of the University of Southern California.

Kokot and his team used 20 years of data from a global database called TriNetX, which included patient information from 64 health centers throughout the U.S. Of the more than 4 million people included, about 116,000 had been diagnosed with cannabis use disorder from April 2004 to April 2024. The diagnoses were based on self-reported use of the drug — in other words, the patient had to tell a health care provider how often they used cannabis and how it affected them.

The researchers compared people with cannabis use disorder to those of a similar age and sex in the study without cannabis use disorder. The majority of the people with cannabis use disorder in the study were white. None had a history of head and neck cancer before the study began. 

The cannabis users in the study were more likely to develop all types of head and neck cancer except hypopharyngeal cancer, a type of lower throat cancer, compared with nonusers. The most common types of head and neck cancer in both groups were oral cancers and cancer of the larynx. 

The study authors noted there were several limitations that could skew the study’s findings. The first is that it’s very difficult to measure how many people regularly use cannabis when relying on self-reported data. 

To be in this group, people would have had to tell a health care provider during a visit that they used cannabis very frequently — the equivalent of smoking about a joint per day, said Dr. Joseph Califano, director of the Gleiberman Head and Neck Cancer Center at the Moores Cancer Center at the University of California, San Diego, who was not involved with the research. 

The same is true for alcohol and tobacco use. 

“The tough thing about this study and almost every study that looks at cannabis use is that we just don’t have the data,” said Califano, who wrote an editorial that was published alongside the new study.

The study also looked at head and neck cancer diagnoses at two points in time: within the first year of being diagnosed with cannabis use disorder and five years or beyond the diagnosis. It excluded, however, cancers found one to four years after a cannabis use disorder diagnosis. This secondary analysis was used to determine how strong the association was: If it remained after five years, it’s more likely the association was strong. 

They found that after five or more years, the cannabis use disorder group still had higher overall rates of head and neck cancer, but there was no longer a significantly increased risk for some specific types, including oral, laryngeal and oropharyngeal cancer.

“You would expect that over time, there would be an accumulative effect,” said Dr. Glenn Hanna, a medical oncologist at the Dana-Farber Cancer Institute’s Center for Head and Neck Oncology.

It’s still unclear what role dose, frequency and how a person ingests cannabis may affect their cancer risk. Califano noted that the new research links the increased risk to self-reported heavy users of cannabis, not occasional users. 

Past studies that have investigated whether cannabis use increases a person’s risk for head and neck cancer have yielded mixed results. The new study highlights the need for continued research on the subject, especially as daily marijuana use grows in popularity , Hanna said.

Despite the study’s limitations, “it still suggests there could be some association,” he said.

Cannabis and cancer risk

The cannabis plant contains more than 100 cannabinoids , including THC and CBD, and hundreds of non-cannabinoid chemicals. Scientists are just beginning to understand the effect these compounds have on the human body, both good and bad. 

“Cannabinoids are powerful drugs that exert their effects at concentrations found with recreational use. We know that because you get high,” Califano said, noting that it’s almost unheard of to find a truly pure form of a single cannabinoid, such as CBD. 

Hanna said that smoking anything, including cannabis, activates inflammatory pathways that could be involved in cancer formation. Cannabinoids could also become carcinogenic in the body. 

“There are biological reasons why someone would hypothesize that cannabis smoke could be harmful, but we’re not sure what the degree of increased risk is, if there is one, with head and neck cancer,” he said, noting that it’s difficult to tease out other risk factors, such as alcohol and tobacco use. About 20% of the people in the study with cannabis use disorder reported frequent tobacco and alcohol use, compared with about 2% in the group without cannabis use disorder.

Keeping this in mind, edible cannabis may be safer than smoking the plant, Hanna said, acknowledging some of his patients use medical marijuana for health conditions. 

But Califano said there’s reason to suspect some cannabinoids themselves — rather than the smoke — might be behind the increased cancer risk, if future research establishes a clear connection. 

“Cannabinoids have a lot of effects that drive immune response, and all these other things that are involved with either how cancer develops or how our bodies fight cancer,” he said. “So it’s not unreasonable to think about the cancer-associated effects of cannabis use to be independent of whether or not you ingest smoke or vape or bake them into your brownies.” 

Hanna and Califano acknowledge that cannabis likely has both positive and negative effects on health. As research uncovers more about the impact cannabis has on the body, it’s likely that different cannabinoids will be found to have different effects on cancer risk. 

“Some of them may be associated with cancer development; some may actually inhibit cancer development,” Califano said.

Kaitlin Sullivan is a contributor for NBCNews.com who has worked with NBC News Investigations. She reports on health, science and the environment and is a graduate of the Craig Newmark Graduate School of Journalism at City University of New York.