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Choosing the Right Research Methodology: A Guide for Researchers

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Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

Why is data validation important in research?

When Data Speak, Listen: Importance of Data Collection and Analysis Methods

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Research Methods Guide: Research Design & Method

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Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

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 ...

Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I struggle so much with designs of the research for sure!

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.

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Research Methodologies

• What are research designs?
• What are research methodologies?

What are research methods?

Quantitative research methods, qualitative research methods, mixed method approach, selecting the best research method.

• Additional Sources

Research methods are different from research methodologies because they are the ways in which you will collect the data for your research project.  The best method for your project largely depends on your topic, the type of data you will need, and the people or items from which you will be collecting data.  The following boxes below contain a list of quantitative, qualitative, and mixed research methods.

• Closed-ended questionnaires/survey: These types of questionnaires or surveys are like "multiple choice" tests, where participants must select from a list of premade answers.  According to the content of the question, they must select the one that they agree with the most.  This approach is the simplest form of quantitative research because the data is easy to combine and quantify.
• Structured interviews: These are a common research method in market research because the data can be quantified.  They are strictly designed for little "wiggle room" in the interview process so that the data will not be skewed.  You can conduct structured interviews in-person, online, or over the phone (Dawson, 2019).

Constructing Questionnaires

When constructing your questions for a survey or questionnaire, there are things you can do to ensure that your questions are accurate and easy to understand (Dawson, 2019):

• Keep the questions brief and simple.
• Eliminate any potential bias from your questions.  Make sure that they do not word things in a way that favor one perspective over another.
• If your topic is very sensitive, you may want to ask indirect questions rather than direct ones.  This prevents participants from being intimidated and becoming unwilling to share their true responses.
• If you are using a closed-ended question, try to offer every possible answer that a participant could give to that question.
• Do not ask questions that assume something of the participant.  The question "How often do you exercise?" assumes that the participant exercises (when they may not), so you would want to include a question that asks if they exercise at all before asking them how often.
• Try and keep the questionnaire as short as possible.  The longer a questionnaire takes, the more likely the participant will not complete it or get too tired to put truthful answers.
• Promise confidentiality to your participants at the beginning of the questionnaire.

Quantitative Research Measures

When you are considering a quantitative approach to your research, you need to identify why types of measures you will use in your study.  This will determine what type of numbers you will be using to collect your data.  There are four levels of measurement:

• Nominal: These are numbers where the order of the numbers do not matter.  They aim to identify separate information.  One example is collecting zip codes from research participants.  The order of the numbers does not matter, but the series of numbers in each zip code indicate different information (Adamson and Prion, 2013).
• Ordinal: Also known as rankings because the order of these numbers matter.  This is when items are given a specific rank according to specific criteria.  A common example of ordinal measurements include ranking-based questionnaires, where participants are asked to rank items from least favorite to most favorite.  Another common example is a pain scale, where a patient is asked to rank their pain on a scale from 1 to 10 (Adamson and Prion, 2013).
• Interval: This is when the data are ordered and the distance between the numbers matters to the researcher (Adamson and Prion, 2013).  The distance between each number is the same.  An example of interval data is test grades.
• Ratio: This is when the data are ordered and have a consistent distance between numbers, but has a "zero point."  This means that there could be a measurement of zero of whatever you are measuring in your study (Adamson and Prion, 2013).  An example of ratio data is measuring the height of something because the "zero point" remains constant in all measurements.  The height of something could also be zero.

Focus Groups

This is when a select group of people gather to talk about a particular topic.  They can also be called discussion groups or group interviews (Dawson, 2019).  They are usually lead by a moderator  to help guide the discussion and ask certain questions.  It is critical that a moderator allows everyone in the group to get a chance to speak so that no one dominates the discussion.  The data that are gathered from focus groups tend to be thoughts, opinions, and perspectives about an issue.

Advantages of Focus Groups

• Only requires one meeting to get different types of responses.
• Less researcher bias due to participants being able to speak openly.
• Helps participants overcome insecurities or fears about a topic.
• The researcher can also consider the impact of participant interaction.

Disadvantages of Focus Groups

• Participants may feel uncomfortable to speak in front of an audience, especially if the topic is sensitive or controversial.
• Since participation is voluntary, not every participant may contribute equally to the discussion.
• Participants may impact what others say or think.
• A researcher may feel intimidated by running a focus group on their own.
• A researcher may need extra funds/resources to provide a safe space to host the focus group.
• Because the data is collective, it may be difficult to determine a participant's individual thoughts about the research topic.

Observation

There are two ways to conduct research observations:

• Direct Observation: The researcher observes a participant in an environment.  The researcher often takes notes or uses technology to gather data, such as a voice recorder or video camera.  The researcher does not interact or interfere with the participants.  This approach is often used in psychology and health studies (Dawson, 2019).
• Participant Observation:  The researcher interacts directly with the participants to get a better understanding of the research topic.  This is a common research method when trying to understand another culture or community.  It is important to decide if you will conduct a covert (participants do not know they are part of the research) or overt (participants know the researcher is observing them) observation because it can be unethical in some situations (Dawson, 2019).

Open-Ended Questionnaires

These types of questionnaires are the opposite of "multiple choice" questionnaires because the answer boxes are left open for the participant to complete.  This means that participants can write short or extended answers to the questions.  Upon gathering the responses, researchers will often "quantify" the data by organizing the responses into different categories.  This can be time consuming because the researcher needs to read all responses carefully.

Semi-structured Interviews

This is the most common type of interview where researchers aim to get specific information so they can compare it to other interview data.  This requires asking the same questions for each interview, but keeping their responses flexible.  This means including follow-up questions if a subject answers a certain way.  Interview schedules are commonly used to aid the interviewers, which list topics or questions that will be discussed at each interview (Dawson, 2019).

Theoretical Analysis

Often used for nonhuman research, theoretical analysis is a qualitative approach where the researcher applies a theoretical framework to analyze something about their topic.  A theoretical framework gives the researcher a specific "lens" to view the topic and think about it critically. it also serves as context to guide the entire study.  This is a popular research method for analyzing works of literature, films, and other forms of media.  You can implement more than one theoretical framework with this method, as many theories complement one another.

Common theoretical frameworks for qualitative research are (Grant and Osanloo, 2014):

• Behavioral theory
• Change theory
• Cognitive theory
• Content analysis
• Cross-sectional analysis
• Developmental theory
• Feminist theory
• Gender theory
• Marxist theory
• Queer theory
• Systems theory
• Transformational theory

Unstructured Interviews

These are in-depth interviews where the researcher tries to understand an interviewee's perspective on a situation or issue.  They are sometimes called life history interviews.  It is important not to bombard the interviewee with too many questions so they can freely disclose their thoughts (Dawson, 2019).

• Open-ended and closed-ended questionnaires: This approach means implementing elements of both questionnaire types into your data collection.  Participants may answer some questions with premade answers and write their own answers to other questions.  The advantage to this method is that you benefit from both types of data collection to get a broader understanding of you participants.  However, you must think carefully about how you will analyze this data to arrive at a conclusion.

Other mixed method approaches that incorporate quantitative and qualitative research methods depend heavily on the research topic.  It is strongly recommended that you collaborate with your academic advisor before finalizing a mixed method approach.

How do you determine which research method would be best for your proposal?  This heavily depends on your research objective.  According to Dawson (2019), there are several questions to ask yourself when determining the best research method for your project:

• Are you good with numbers and mathematics?
• Would you be interested in conducting interviews with human subjects?
• Would you enjoy creating a questionnaire for participants to complete?
• Do you prefer written communication or face-to-face interaction?
• What skills or experiences do you have that might help you with your research?  Do you have any experiences from past research projects that can help with this one?
• How much time do you have to complete the research?  Some methods take longer to collect data than others.
• What is your budget?  Do you have adequate funding to conduct the research in the method you  want?
• How much data do you need?  Some research topics need only a small amount of data while others may need significantly larger amounts.
• What is the purpose of your research? This can provide a good indicator as to what research method will be most appropriate.
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• Last Updated: Aug 2, 2022 2:36 PM
• URL: https://library.tiffin.edu/researchmethodologies

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Types of Research Methods: Examples and Tips

What are research methods?

Research methods are the techniques and procedures used to collect and analyze data in order to answer research questions and test a research hypothesis . There are several different types of research methods, each with its own strengths and weaknesses. 

Common Types of Research Methods

There are several main types of research methods that are employed in academic articles. The type of research method applied depends on the nature of the data to be collected and analyzed, as well as any restrictions or limitations that dictate the study’s resources and methodology. Surveying articles from your target journal and identifying the methods commonly used in these studies is also recommended before choosing a research method or methods.

It’s important to note that research methods can be combined for a more complete understanding of a research question or hypothesis. For example, an experiment can be followed by a survey to gather more information about participants’ attitudes and behaviors.

Overall, the choice of research method depends on the research question, the type of data needed, and the resources available to the researcher.

Data Collection Methods

Data is information collected in order to answer research questions . The kind of data you choose to collect will depend on the nature of your research question and the aims of your study. There are a few main category distinctions of data a researcher can collect.

Quantitative vs qualitative data

Qualitative and quantitative data are two types of data that are often used in research studies. They are different in terms of their characteristics, how they are collected, and how they are analyzed.

Quantitative data is numerical and is collected through methods such as surveys, polls, and experiments. It is often used to measure and describe the characteristics of a large group of people or objects. This data can be analyzed using statistical methods to find patterns and trends.

Qualitative data, on the other hand, is non-numerical and is collected through methods such as interviews, observations, and focus groups. It is often used to understand the experiences, attitudes, and perceptions of individuals or small groups. This data is analyzed using methods such as content analysis, thematic analysis, and discourse analysis to identify patterns and themes.

Overall, quantitative data provides a more objective and generalizable understanding of a phenomenon, while qualitative data provides a more subjective and in-depth understanding. Both types of data are important and can be used together to gain a more comprehensive understanding of a topic.

You can also make use of both qualitative and quantitative research methods in your study.

Primary vs secondary data

Primary and secondary research are two different types of research methods that are used in the field of academia and market research. Both primary and secondary sources can be applied in most studies.

Primary research is research that is conducted by the individual or organization themselves. It involves collecting original data through methods such as surveys, interviews, or experiments. The data collected through primary research is specific to the research question and objectives, and is not typically available through other sources.

Secondary research, on the other hand, involves the use of existing data that has already been collected by someone else. This can include data from government reports, academic journals, or industry publications. The advantage of secondary research is that it is typically less time-consuming and less expensive than primary research, as the data has already been collected. However, the data may not be as specific or relevant to the research question and objectives.

The choice between using primary and secondary research will depend on the research question, study budget, and time constraints of the project, as well as the target journal to which you are submitting your manuscript.

Experimental vs descriptive data collection

Experimental data is collected through a controlled experiment, in which the researcher manipulates one or more variables to observe the effect on another variable. The goal of experimental data is to determine cause-and-effect relationships. For example, in a study on the effectiveness of a new drug for treating a certain condition, the researchers would randomly assign participants to either a group that receives the drug or a group that receives a placebo, and then compare the outcomes between the two groups. The data collected in this study would be considered experimental data.

Descriptive data, on the other hand, is data that is collected through observation or surveys and is used to describe the characteristics of a population or phenomenon. The goal of descriptive data is to provide a snapshot of the current state of a certain population or phenomenon, rather than to determine cause-and-effect relationships. For example, in a study on the dietary habits of a certain population, the researchers would collect data on what types of food the participants typically eat and how often they eat them. This data would be considered descriptive data.

In summary, experimental data is collected through a controlled experiment to determine cause-and-effect relationships, while descriptive data is collected through observation or surveys to describe the characteristics of a population or phenomenon.

Descriptive data examples:

• A survey that asks people about their favorite type of music
• A census that counts the number of people living in a certain area
• A poll that asks people about their political affiliation

Experimental data examples:

• A study comparing the effectiveness of two different medications for treating a certain condition
• An experiment measuring the effect of different levels of a certain chemical on plant growth
• A clinical trial comparing the side effects of a new treatment to a standard treatment for a disease

Examples of Difference Data Collection Methods

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Introduction to qualitative research methods – Part I

Shagufta bhangu.

Department of Global Health and Social Medicine, King's College London, London, United Kingdom

Fabien Provost

Carlo caduff.

Qualitative research methods are widely used in the social sciences and the humanities, but they can also complement quantitative approaches used in clinical research. In this article, we discuss the key features and contributions of qualitative research methods.

INTRODUCTION

Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures. In this article, we describe the strengths and role of qualitative research methods and how these can be employed in clinical research.

Although frequently employed in the social sciences and humanities, qualitative research methods can complement clinical research. These techniques can contribute to a better understanding of the social, cultural, political, and economic dimensions of health and illness. Social scientists and scholars in the humanities rely on a wide range of methods, including interviews, surveys, participant observation, focus groups, oral history, and archival research to examine both structural conditions and lived experience [ Figure 1 ]. Such research can not only provide robust and reliable data but can also humanize and add richness to our understanding of the ways in which people in different parts of the world perceive and experience illness and how they interact with medical institutions, systems, and therapeutics.

Examples of qualitative research techniques

Qualitative research methods should not be seen as tools that can be applied independently of theory. It is important for these tools to be based on more than just method. In their research, social scientists and scholars in the humanities emphasize social theory. Departing from a reductionist psychological model of individual behavior that often blames people for their illness, social theory focuses on relations – disease happens not simply in people but between people. This type of theoretically informed and empirically grounded research thus examines not just patients but interactions between a wide range of actors (e.g., patients, family members, friends, neighbors, local politicians, medical practitioners at all levels, and from many systems of medicine, researchers, policymakers) to give voice to the lived experiences, motivations, and constraints of all those who are touched by disease.

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

In identifying the factors that contribute to the occurrence and persistence of a phenomenon, it is paramount that we begin by asking the question: what do we know about this reality? How have we come to know this reality? These two processes, which we can refer to as the “what” question and the “how” question, are the two that all scientists (natural and social) grapple with in their research. We refer to these as the ontological and epistemological questions a research study must address. Together, they help us create a suitable methodology for any research study[ 1 ] [ Figure 2 ]. Therefore, as with quantitative methods, there must be a justifiable and logical method for understanding the world even for qualitative methods. By engaging with these two dimensions, the ontological and the epistemological, we open a path for learning that moves away from commonsensical understandings of the world, and the perpetuation of stereotypes and toward robust scientific knowledge production.

Developing a research methodology

Every discipline has a distinct research philosophy and way of viewing the world and conducting research. Philosophers and historians of science have extensively studied how these divisions and specializations have emerged over centuries.[ 1 , 2 , 3 ] The most important distinction between quantitative and qualitative research techniques lies in the nature of the data they study and analyze. While the former focus on statistical, numerical, and quantitative aspects of phenomena and employ the same in data collection and analysis, qualitative techniques focus on humanistic, descriptive, and qualitative aspects of phenomena.[ 4 ]

For the findings of any research study to be reliable, they must employ the appropriate research techniques that are uniquely tailored to the phenomena under investigation. To do so, researchers must choose techniques based on their specific research questions and understand the strengths and limitations of the different tools available to them. Since clinical work lies at the intersection of both natural and social phenomena, it means that it must study both: biological and physiological phenomena (natural, quantitative, and objective phenomena) and behavioral and cultural phenomena (social, qualitative, and subjective phenomena). Therefore, clinical researchers can gain from both sets of techniques in their efforts to produce medical knowledge and bring forth scientifically informed change.

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

In this section, we discuss the key features and contributions of qualitative research methods [ Figure 3 ]. We describe the specific strengths and limitations of these techniques and discuss how they can be deployed in scientific investigations.

Key features of qualitative research methods

One of the most important contributions of qualitative research methods is that they provide rigorous, theoretically sound, and rational techniques for the analysis of subjective, nebulous, and difficult-to-pin-down phenomena. We are aware, for example, of the role that social factors play in health care but find it hard to qualify and quantify these in our research studies. Often, we find researchers basing their arguments on “common sense,” developing research studies based on assumptions about the people that are studied. Such commonsensical assumptions are perhaps among the greatest impediments to knowledge production. For example, in trying to understand stigma, surveys often make assumptions about its reasons and frequently associate it with vague and general common sense notions of “fear” and “lack of information.” While these may be at work, to make such assumptions based on commonsensical understandings, and without conducting research inhibit us from exploring the multiple social factors that are at work under the guise of stigma.

In unpacking commonsensical understandings and researching experiences, relationships, and other phenomena, qualitative researchers are assisted by their methodological commitment to open-ended research. By open-ended research, we mean that these techniques take on an unbiased and exploratory approach in which learnings from the field and from research participants, are recorded and analyzed to learn about the world.[ 5 ] This orientation is made possible by qualitative research techniques that are particularly effective in learning about specific social, cultural, economic, and political milieus.

Second, qualitative research methods equip us in studying complex phenomena. Qualitative research methods provide scientific tools for exploring and identifying the numerous contributing factors to an occurrence. Rather than establishing one or the other factor as more important, qualitative methods are open-ended, inductive (ground-up), and empirical. They allow us to understand the object of our analysis from multiple vantage points and in its dispersion and caution against predetermined notions of the object of inquiry. They encourage researchers instead to discover a reality that is not yet given, fixed, and predetermined by the methods that are used and the hypotheses that underlie the study.

Once the multiple factors at work in a phenomenon have been identified, we can employ quantitative techniques and embark on processes of measurement, establish patterns and regularities, and analyze the causal and correlated factors at work through statistical techniques. For example, a doctor may observe that there is a high patient drop-out in treatment. Before carrying out a study which relies on quantitative techniques, qualitative research methods such as conversation analysis, interviews, surveys, or even focus group discussions may prove more effective in learning about all the factors that are contributing to patient default. After identifying the multiple, intersecting factors, quantitative techniques can be deployed to measure each of these factors through techniques such as correlational or regression analyses. Here, the use of quantitative techniques without identifying the diverse factors influencing patient decisions would be premature. Qualitative techniques thus have a key role to play in investigations of complex realities and in conducting rich exploratory studies while embracing rigorous and philosophically grounded methodologies.

Third, apart from subjective, nebulous, and complex phenomena, qualitative research techniques are also effective in making sense of irrational, illogical, and emotional phenomena. These play an important role in understanding logics at work among patients, their families, and societies. Qualitative research techniques are aided by their ability to shift focus away from the individual as a unit of analysis to the larger social, cultural, political, economic, and structural forces at work in health. As health-care practitioners and researchers focused on biological, physiological, disease and therapeutic processes, sociocultural, political, and economic conditions are often peripheral or ignored in day-to-day clinical work. However, it is within these latter processes that both health-care practices and patient lives are entrenched. Qualitative researchers are particularly adept at identifying the structural conditions such as the social, cultural, political, local, and economic conditions which contribute to health care and experiences of disease and illness.

For example, the decision to delay treatment by a patient may be understood as an irrational choice impacting his/her chances of survival, but the same may be a result of the patient treating their child's education as a financial priority over his/her own health. While this appears as an “emotional” choice, qualitative researchers try to understand the social and cultural factors that structure, inform, and justify such choices. Rather than assuming that it is an irrational choice, qualitative researchers try to understand the norms and logical grounds on which the patient is making this decision. By foregrounding such logics, stories, fears, and desires, qualitative research expands our analytic precision in learning about complex social worlds, recognizing reasons for medical successes and failures, and interrogating our assumptions about human behavior. These in turn can prove useful in arriving at conclusive, actionable findings which can inform institutional and public health policies and have a very important role to play in any change and transformation we may wish to bring to the societies in which we work.

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Conflicts of interest.

There are no conflicts of interest.

Research Methods In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

The researcher decides where the experiment will take place, at what time, with which participants, in what circumstances,  using a standardized procedure.

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

Meta-analysis is a statistical procedure used to combine and synthesize findings from multiple independent studies to estimate the average effect size for a particular research question.

Meta-analysis goes beyond traditional narrative reviews by using statistical methods to integrate the results of several studies, leading to a more objective appraisal of the evidence.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

• Strengths : Increases the conclusions’ validity as they’re based on a wider range.
• Weaknesses : Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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A comprehensive review of theories, methods, and techniques for bottleneck identification and management in manufacturing systems.

1. Introduction

2. categories of manufacturing bottlenecks, 2.1. definition of bottlenecks in manufacturing systems, 2.1.1. bottleneck machines, 2.1.2. logistics bottlenecks, 2.1.3. bottleneck processes, 2.1.4. bottleneck workpieces, 2.1.5. human resource bottlenecks, 2.1.6. bottlenecks caused by maintenance and upkeep, 2.2. extensional concept of manufacturing bottlenecks, 3. manufacturing system bottleneck research, 3.1. bottleneck identifications, 3.1.1. bottleneck identification based on static models, 3.1.2. bottleneck identification based on simulations, 3.1.3. bottleneck identification based on data-driven approaches, 3.2. shifting bottlenecks, 3.2.1. causes of bottleneck shifting, 3.2.2. consequence of system bottlenecks, 3.3. bottleneck management, 3.3.1. proactive bottleneck prediction, 3.3.2. reactive mitigation strategies, 4. bottleneck research trends in manufacturing system, 4.1. digital twin technology promotes the development of data-driven bottleneck analysis, 4.2. use neural networks to identify and analyze manufacturing system bottlenecks, 5. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Tang, J.; Dai, Z.; Jiang, W.; Wu, X.; Zhuravkov, M.A.; Xue, Z.; Wang, J. A Comprehensive Review of Theories, Methods, and Techniques for Bottleneck Identification and Management in Manufacturing Systems. Appl. Sci. 2024 , 14 , 7712. https://doi.org/10.3390/app14177712

Tang J, Dai Z, Jiang W, Wu X, Zhuravkov MA, Xue Z, Wang J. A Comprehensive Review of Theories, Methods, and Techniques for Bottleneck Identification and Management in Manufacturing Systems. Applied Sciences . 2024; 14(17):7712. https://doi.org/10.3390/app14177712

Tang, Jiachao, Zongxu Dai, Wenrui Jiang, Xuemei Wu, Michael Anatolievich Zhuravkov, Zheng Xue, and Jiazhi Wang. 2024. "A Comprehensive Review of Theories, Methods, and Techniques for Bottleneck Identification and Management in Manufacturing Systems" Applied Sciences 14, no. 17: 7712. https://doi.org/10.3390/app14177712

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Sequencing Techniques: A Comparative Overview

1. shotgun metagenomic sequencing.

Shotgun metagenomic sequencing, or simply whole genome sequencing (WGS), is the indiscriminate sequencing of all genetic material present in a sample. This may include sequence from all domains of life (viruses, archaea, prokaryotes, and eukaryotes) as well as host genetic material if no selection is applied. With more advanced technologies, there are multiple methods to prepare a Shotgun library; however, the principle still stands. The input DNA will need to be sheared into appropriate fragment size, followed by adapter ligation. Barcode index sequence will be added onto adapter-ligated fragment via PCR reaction. The end-products are cleaned up and pooled into a single library pool which is now ready for sequencing.

A typical workflow for taxonomy analysis of shotgun metagenomic data includes trimming to remove technical sequence, such as adapter, and poor quality reads. Additional filtering may be carried out to remove host-origin, contaminant, and low-complexity reads . Finally, comparison to a reference database comprising whole genomes (e.g. Kraken2 and Centrifuge3) or selected marker genes (MetaPhlAn4 and mOTU5) to generate a taxonomic profile. This generated profile provides the compositional information necessary for cataloguing the entirety of organisms present in a given microbiome. This coverage is complete and includes the totality of genetic information present. This may provide additional analyses beyond taxonomic identification, including insight into functional pathways or identifying presence of antibiotic resistance genes.

2. Targeted 16S rRNA Gene Sequencing

Targeted 16S rRNA gene sequencing targets regions of specific and highly conserved sections of genes, named after the portion that is often targeted within the gene that encodes the small 30S subunit found in prokaryotes: the 16S rRNA gene (Figure. 1). The 16S rRNA gene is around 1500 base pairs long and contains nine hypervariable regions interspaced by conserved regions [1]. By synthesizing primers that target the conserved regions shared across phylogenetic groups, entire microbial communities can be characterized and genotyped by comparing the small differences within these hypervariable regions. The qPCR amplified regions are cleaned up, barcoded, pooled into a library, and sequenced. Since its inception, gene amplicon sequencing has been a major technique for resolving taxonomic profiles of complex microbiomes.

Microbiome Profiling: Applications and Considerations

One of the most widely sought applications shared between shotgun metagenomic sequencing and Targeted 16S sequencing is the characterization of microbiome communities. Given that microbes exist in nearly every environment, the variety of samples that can be sequenced is endless, thus the potential data to be generated equally so. Depending on the scope of scientific investigation, researchers may be interested in identifying what microbes are present in a sample, how diverse and abundant are those microbes, and what type of metabolic or other activity are they carrying out.

Targeted (16S) sequencing, by nature of its own mechanism of specificity, is limited to the scope of the targeted regions amplified in microbial identification. Researchers may choose to utilize this sequencing technique when looking for something specific within a sample, or preparing parallel libraries of samples targeting various regions (i.e. ITS region for fungal species and V3V4 for bacteria and archaea) if microbial discovery is desired.

Shotgun sequencing is a comprehensive technique that indexes all genetic information within a sample, complex or otherwise. In this regard, it is superior to targeted gene amplicon sequencing as it allows for identification of all organisms present rather than being limited to organisms containing the targeted region. Additionally, the complete genomic decoding of an organism intrinsically provides greater resolution during taxonomic identification since the entire range of the genome may be referenced to a database [2]. With shotgun sequencing it is possible to generate strain level resolution, something that targeted 16S sequencing is incapable of.

The genomic data generated by shotgun metagenomic sequencing may also provide insight in postulating metabolic activity of a community since all genes are sequenced. Without the ability to determine active expression, however, this only permits speculation rather than confirmation. Regardless, this is useful information, as it could establish grounds for further research utilizing metatanscriptomic analysis to determine the expressed metabolic activity of the community.

Accessibility and Practical Considerations

1. Robustness of Workflow

Sample input is a critical variable to weigh when choosing a sequencing technique. The aim of a research project outlines the criteria of samples to be selected, and the samples selected may very well dictate the technique utilized. To evaluate the microbiome profile of low biomass samples and samples containing heavy host DNA presence, choosing WGS becomes a more costly option.

Historically, shotgun metagenomic sequencing offers complete genomic information but is slightly limited in its range of accessible samples. The shallow PCR amplification requires sufficient sample input concentration, needing at least 1 ng/µl of purified DNA. This limits the options of viable sample inputs, but not so drastically since a yield of >1 ng/µl is generally within reach of most sample types. With innovative improvements in sample processing, viable libraries can be generated with ultra-low input of as low as 100 femtogram.

The human microbiome is a heavily studied microbial community, but some sample types are often saturated with host DNA. This stands as another factor to consider. Pursuing shotgun sequencing of host-rich samples can quickly become costly as a much higher sequencing depths is required to compensate for a considerable portion of the reads toward host genome. This is regarded as an acceptable expense if one is interested in the data that only shotgun sequencing can provide such as data on viromes, metabolomes, the detection of antibiotic resistance genes, and other lower abundance microbial species. In studies only interested in defining microbiome communities, however, this cost and breadth of information is not necessary and targeted gene amplicon sequencing serves as an effective alternative.

Targeted 16S rRNA sequencing does not offer the extensive genetic information generated by shotgun metagenomic sequencing. Despite this, there are several advantageous qualities. The workflow for gene amplicon sequencing involves a high-cycle targeted amplification step that, in some protocols, reaches upwards of 40 rounds of replication. At these depths, it is possible for samples with concentrations as low as picograms per microliter to be successfully amplified and sequenced [3]. Coupled with the highly specific targeted 16S primers, gene amplicon sequencing is a sensible option for processing samples with low biomass and host-rich environments at, if only the microbial profile of a sample is sought.

2. Operational Demands

One of the primary factors that turns researchers away from the WGS technique are the extensive operational demands of running the service. The startup costs alone can turn away smaller research groups, with the latest Illumina sequencers often costing about a million dollars and reagents often costing over ten thousand dollars per run. For many, the experimental demand for sequencing does not justify the fiscal investment into such expensive systems.

Shotgun sequencing is capable of analyzing samples with incredible depth, with flow cells ranging from 1-25 billion paired-end reads. The consequence being that the output files for a NovaSeq frequently reach into the terabytes (TB), and quickly requires massive data infrastructure to transfer and house this information [4]. For some researchers, this level of information is a tremendous incentive in selecting their workflow for reasons mentioned earlier in the Microbiome Profiling section. But, for scientists only interested in knowing the microbial composition of their experimental sample, the cost to store and catalog this massive amount of data becomes excessive when there is such a cost-friendly alternative in the targeted 16S sequencing technique.

Targeted 16S rRNA sequencing produces high quality data within a narrower range compared to shotgun sequencing, and as a result is less expensive to perform. While the $128,000 price tag for an Illumina MiSeq is much more affordable, a similar concern persists. Research groups are not always capable of bearing the costs of establishing and maintaining the infrastructure for operating a sequencing system compared to outsourcing these techniques at a fraction of the cost.

Additional factors limit accessibility to NGS sequencing and play a major role in deciding a microbiome company’s long term developmental rollout plan. These factors include proper sample collection and storage, complete and unbiased DNA purification, competent library preparation, and a high-grade bioinformatics pipeline. In the case of all sequencing, be it shotgun or gene amplicon sequencing, a team of bioinformaticians is required to build and maintain a pipeline through which the sequencing data is polished and converted into interpretable and accurate information.

With all of this taken into consideration, fully developing a sequencing workflow becomes a daunting endeavor. These considerations further distance researchers from realistic and achievable fiscal goals. Microbiome groups in need of affordable sequencing may opt to outsource and offset the high startup and maintenance costs. Companies, like Zymo Research, offer a complete, high throughput sequencing service that includes bioinformatics analysis on a price-per-sample basis, all within a turnaround time of a few weeks. This greatly increases ease-of-access for scientists to gather high quality data on the microbial communities of their samples.

Selecting an appropriate microbiome methodology is critical to increasing the odds of success in microbiome studies. While the data provided by targeted (16S) sequencing is limited to microbial identification, often with resolution limited to genus or species, this method provides an excellent solution to initially characterize a microbial population and begin making inferences about the capabilities of that population. Additionally, the ease of preparing the libraries and its tolerance to low-input samples, samples contaminated with host genetic material, and ability to select for a single class of organism can allow targeted methods to work where shotgun (WGS) methods would likely either fail or prove extremely inefficient and costly.

For laboratories just beginning microbiome analysis or with limited headcount or equipment, selecting a microbiome service provider that is capable of running the full pipeline can enable them to reap the benefits of a more experienced and equipped team on a per-sample fee basis. Zymo Research is one such organization, with team members who have expertise in all relevant aspects of microbiome pipelines from sample collection and extraction to advanced microbiology, and even bioinformatic processing of results and analytic pipeline development.

As genomics technology continues to evolve and spread around the globe, we hope this article can serve to help inform researchers beginning to study the microbiome better identify their own needs and the best ways to meet them. As technology changes, this article will be updated to reflect any major shifts in short-read sequencing-based microbiome analysis.

Discover Zymo Research’s Shotgun Metagenomic Sequencing Service

Learn more about Zymo Research’s 16S/ITS Amplicon Sequencing Service

• Richa Bharti, Dominik G Grimm, Current challenges and best-practice protocols for microbiome analysis, Briefings in Bioinformatics, Volume 22, Issue 1, January 2021, Pages 178–193, https://doi.org/10.1093/bib/bbz155
• Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nature methods, 13(7), 581-583.
• Jakob Brandt, Albertsen Mads, Investigation of Detection Limits of DNA extraction and Primer Choice on the Observed Microbial Communities in Drinking Water Samples Using 16S rRNA Gene Amplicon Sequencing, Frontiers in Microbiology, Volume 9, 2018, https://www.frontiersin.org/articles/10.3389/fmicb.2018.02140
• Tanjo, T., Kawai, Y., Tokunaga, K. et al. Practical guide for managing large-scale human genome data in research. J Hum Genet 66, 39–52 (2021). https://doi.org/10.1038/s10038-020-00862-1
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• DOI: 10.2196/59587
• Corpus ID: 269185679

Data Preprocessing Techniques for Artificial Learning (AI)/Machine Learning (ML)-Readiness: Systematic Review of Wearable Sensor Data in Cancer Care.

• Bengie L. Ortiz , Vibhuti Gupta , +6 authors Muneesh Tewari
• Published in JMIR mHealth and uHealth 16 April 2024
• Medicine, Computer Science

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Large language models for wearable sensor-based human activity recognition, health monitoring, and behavioral modeling: a survey of early trends, datasets, and challenges, 89 references, dyadic and individual variation in 24-hour heart rates of cancer patients and their caregivers, an innovative non-invasive approach to personalized nutrition and metabolism monitoring using wearable sensors, evaluating the potential of machine learning and wearable devices in end-of-life care in predicting 7-day death events among patients with terminal cancer: cohort study, physical fitness assessment for cancer patients using multi-model decision fusion based on multi-source data, updating the definition of cancer, daily physical activity monitoring in older adults with metastatic prostate cancer on active treatment: feasibility and associations with toxicity., assessing upper limb function in breast cancer survivors using wearable sensors and machine learning in a free-living environment, wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of hematologic malignancies, machine learning for postoperative continuous recovery scores of oncology patients in perioperative care with data from wearables, persistent homology-based machine learning method for filtering and classifying mammographic microcalcification images in early cancer detection, related papers.

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Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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 goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

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.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Meditation and Mindfulness: Effectiveness and Safety

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Meditation has a history that goes back thousands of years, and many meditative techniques began in Eastern traditions. The term “meditation” refers to a variety of practices that focus on mind and body integration and are used to calm the mind and enhance overall well-being. Some types of meditation involve maintaining mental focus on a particular sensation, such as breathing, a sound, a visual image, or a mantra, which is a repeated word or phrase. Other forms of meditation include the practice of mindfulness, which involves maintaining attention or awareness on the present moment without making judgments.

Programs that teach meditation or mindfulness may combine the practices with other activities. For example, mindfulness-based stress reduction is a program that teaches mindful meditation, but it also includes discussion sessions and other strategies to help people apply what they have learned to stressful experiences. Mindfulness-based cognitive therapy integrates mindfulness practices with aspects of cognitive behavioral therapy.

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Meditation and mindfulness practices usually are considered to have few risks. However, few studies have examined these practices for potentially harmful effects, so it isn’t possible to make definite statements about safety. 

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} More

A 2020 review examined 83 studies (a total of 6,703 participants) and found that 55 of those studies reported negative experiences related to meditation practices. The researchers concluded that about 8 percent of participants had a negative effect from practicing meditation, which is similar to the percentage reported for psychological therapies. The most commonly reported negative effects were anxiety and depression. In an analysis limited to 3 studies (521 participants) of mindfulness-based stress reduction programs, investigators found that the mindfulness practices were not more harmful than receiving no treatment.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} How popular are meditation and mindfulness?

According to the National Health Interview Survey, an annual nationally representative survey, the percentage of U.S. adults who practiced meditation more than doubled between 2002 and 2022, from 7.5 to 17.3 percent. Of seven complementary health approaches for which data were collected in the 2022 survey, meditation was the most popular, beating out yoga (used by 15.8 percent of adults), chiropractic care (11.0 percent), massage therapy (10.9 percent), guided imagery/progressive muscle relaxation (6.4 percent), acupuncture (2.2 percent), and naturopathy (1.3 percent).

For children aged 4 to 17 years, data are available for 2017; in that year, 5.4 percent of U.S. children used meditation. 

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Why do people practice mindfulness meditation?

In a 2012 U.S. survey, 1.9 percent of 34,525 adults reported that they had practiced mindfulness meditation in the past 12 months. Among those responders who practiced mindfulness meditation exclusively, 73 percent reported that they meditated for their general wellness and to prevent diseases, and most of them (approximately 92 percent) reported that they meditated to relax or reduce stress. In more than half of the responses, a desire for better sleep was a reason for practicing mindfulness meditation.

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Meditation and mindfulness practices may have a variety of health benefits and may help people improve the quality of their lives. Recent studies have investigated if meditation or mindfulness helps people manage anxiety, stress, depression, pain, or symptoms related to withdrawal from nicotine, alcohol, or opioids. 

Other studies have looked at the effects of meditation or mindfulness on weight control or sleep quality. 

However, much of the research on these topics has been preliminary or not scientifically rigorous. Because the studies examined many different types of meditation and mindfulness practices, and the effects of those practices are hard to measure, results from the studies have been difficult to analyze and may have been interpreted too optimistically.

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• A 2018 NCCIH-supported analysis of 142 groups of participants with diagnosed psychiatric disorders such as anxiety or depression examined mindfulness meditation approaches compared with no treatment and with established evidence-based treatments such as cognitive behavioral therapy and antidepressant medications. The analysis included more than 12,000 participants, and the researchers found that for treating anxiety and depression, mindfulness-based approaches were better than no treatment at all, and they worked as well as the evidence-based therapies.
• A 2021 analysis of 23 studies (1,815 participants) examined mindfulness-based practices used as treatment for adults with diagnosed anxiety disorders. The studies included in the analysis compared the mindfulness-based interventions (alone or in combination with usual treatments) with other treatments such cognitive behavioral therapy, psychoeducation, and relaxation. The analysis showed mixed results for the short-term effectiveness of the different mindfulness-based approaches. Overall, they were more effective than the usual treatments at reducing the severity of anxiety and depression symptoms, but only some types of mindfulness approaches were as effective as cognitive behavioral therapy. However, these results should be interpreted with caution because the risk of bias for all of the studies was unclear. Also, the few studies that followed up with participants for periods longer than 2 months found no long-term effects of the mindfulness-based practices.
• A 2019 analysis of 23 studies that included a total of 1,373 college and university students looked at the effects of yoga, mindfulness, and meditation practices on symptoms of stress, anxiety, and depression. Although the results showed that all the practices had some effect, most of the studies included in the review were of poor quality and had a high risk of bias.

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Few high-quality studies have examined the effects of meditation and mindfulness on blood pressure. According to a 2017 statement from the American Heart Association, the practice of meditation may have a possible benefit, but its specific effects on blood pressure have not been determined.

• A 2020 review of 14 studies (including more than 1,100 participants) examined the effects of mindfulness practices on the blood pressure of people who had health conditions such as hypertension, diabetes, or cancer. The analysis showed that for people with these health conditions, practicing mindfulness-based stress reduction was associated with a significant reduction in blood pressure.

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Studies examining the effects of mindfulness or meditation on acute and chronic pain have produced mixed results.

• A 2020 report by the Agency for Healthcare Research and Quality concluded that mindfulness-based stress reduction was associated with short-term (less than 6 months) improvement in low-back pain but not fibromyalgia pain.
• A 2020 NCCIH-supported analysis of five studies of adults using opioids for acute or chronic pain (with a total of 514 participants) found that meditation practices were strongly associated with pain reduction.
• Acute pain, such as pain from surgery, traumatic injuries, or childbirth, occurs suddenly and lasts only a short time. A 2020 analysis of 19 studies examined the effects of mindfulness-based therapies for acute pain and found no evidence of reduced pain severity. However, the same analysis found some evidence that the therapies could improve a person’s tolerance for pain.
• A 2017 analysis of 30 studies (2,561 participants) found that mindfulness meditation was more effective at decreasing chronic pain than several other forms of treatment. However, the studies examined were of low quality.
• A 2019 comparison of treatments for chronic pain did an overall analysis of 11 studies (697 participants) that evaluated cognitive behavioral therapy, which is the usual psychological intervention for chronic pain; 4 studies (280 participants) that evaluated mindfulness-based stress reduction; and 1 study (341 participants) of both therapies. The comparison found that both approaches were more effective at reducing pain intensity than no treatment, but there was no evidence of any important difference between the two approaches.
• A 2019 review found that mindfulness-based approaches did not reduce the frequency, length, or pain intensity of headaches. However, the authors of this review noted that their results are likely imprecise because only five studies (a total of 185 participants) were included in the analysis, and any conclusions made from the analysis should be considered preliminary.

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Mindfulness meditation practices may help reduce insomnia and improve sleep quality.

• A 2019 analysis of 18 studies (1,654 total participants) found that mindfulness meditation practices improved sleep quality more than education-based treatments. However, the effects of mindfulness meditation approaches on sleep quality were no different than those of evidence-based treatments such as cognitive behavioral therapy and exercise.

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Several clinical trials have investigated if mindfulness-based approaches such as mindfulness-based relapse prevention (MBRP) might help people recover from substance use disorders. These approaches have been used to help people increase their awareness of the thoughts and feelings that trigger cravings and learn ways to reduce their automatic reactions to those cravings.

• A 2018 review of 37 studies (3,531 total participants) evaluated the effectiveness of several mindfulness-based approaches to substance use disorder treatment and found that they significantly decreased participants’ craving levels. The mindfulness-based practices were slightly better than other therapies at promoting abstinence from substance use.
• A 2017 analysis specifically focused on MBRP examined 9 studies (901 total participants) of this approach. The analysis concluded that MBRP was not more effective at preventing substance use relapses than other treatments such as health education and cognitive behavioral therapy. However, MBRP did slightly reduce cravings and symptoms of withdrawal associated with alcohol use disorders.

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Studies have suggested that meditation and mindfulness may help reduce symptoms of post-traumatic stress disorder (PTSD).

• A 2018 review supported by NCCIH examined the effects of meditation (in 2 studies, 179 total participants) and other mindfulness-based practices (in 6 studies, 332 total participants) on symptoms of PTSD. Study participants included veterans, nurses, and people who experienced interpersonal violence. Six of the eight studies reported that participants had a reduction of PTSD symptoms after receiving some form of mindfulness-based treatment.
• A 2018 clinical trial funded by the U.S. Department of Defense compared the effectiveness of meditation, health education, and prolonged exposure therapy, a widely accepted treatment for PTSD recommended by the American Psychological Association. Prolonged exposure therapy helps people reduce their PTSD symptoms by teaching them to gradually remember traumatic memories, feelings, and situations. The study included 203 veterans with PTSD as a result of their active military service. The results of the study showed that meditation was as effective as prolonged exposure therapy at reducing PTSD symptoms and depression, and it was more effective than PTSD health education. The veterans who used meditation also showed improvement in mood and overall quality of life.

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Mindfulness-based approaches may improve the mental health of people with cancer.

• A 2019 analysis of 29 studies (3,274 total participants) of mindfulness-based practices showed that use of mindfulness practices among people with cancer significantly reduced psychological distress, fatigue, sleep disturbance, pain, and symptoms of anxiety and depression. However, most of the participants were women with breast cancer, so the effects may not be similar for other populations or other types of cancer.

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Studies have suggested possible benefits of meditation and mindfulness programs for losing weight and managing eating behaviors.

• A 2017 review of 15 studies (560 total participants) looked at the effects of mindfulness-based practices on the mental and physical health of adults with obesity or who were overweight. The review found that these practices were very effective methods for managing eating behaviors but less effective at helping people lose weight. Mindfulness-based approaches also helped participants manage symptoms of anxiety and depression.
• A 2018 analysis of 19 studies (1,160 total participants) found that mindfulness programs helped people lose weight and manage eating-related behaviors such as binge, emotional, and restrained eating. The results of the analysis showed that treatment programs, such as mindfulness-based stress reduction and mindfulness-based cognitive therapy, that combine formal meditation and mindfulness practices with informal mindfulness exercises were especially effective methods for losing weight and managing eating.

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Several studies have been done on using meditation and mindfulness practices to improve symptoms of attention-deficit hyperactivity disorder (ADHD). However, the studies have not been of high quality and the results have been mixed, so evidence that meditation or mindfulness approaches will help people manage symptoms of ADHD is not conclusive.

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Some research suggests that meditation and mindfulness practices may affect the functioning or structure of the brain. Studies have used various methods of measuring brain activity to look for measurable differences in the brains of people engaged in mindfulness-based practices. Other studies have theorized that training in meditation and mindfulness practices can change brain activity. However, the results of these studies are difficult to interpret, and the practical implications are not clear.

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NCCIH supports a variety of meditation and mindfulness studies, including:

• An evaluation of how the brain responds to the use of mindfulness meditation as part of a combined treatment for migraine pain.
• A study of the effectiveness of mindfulness therapy and medication (buprenorphine) as a treatment for opioid use disorder.
• A study of a mindfulness training program designed to help law enforcement officers improve their mental health by managing stress and increasing resilience.

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• Don’t use meditation or mindfulness to replace conventional care or as a reason to postpone seeing a health care provider about a medical problem.
• Ask about the training and experience of the instructor of the meditation or mindfulness practice you are considering.
• Take charge of your health—talk with your health care providers about any complementary health approaches you use. Together, you can make shared, well-informed decisions

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Nccih clearinghouse.

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

Know the Science

NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.

Explaining How Research Works (NIH)

Know the Science: How To Make Sense of a Scientific Journal Article

Understanding Clinical Studies (NIH)

A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .

Website: https://pubmed.ncbi.nlm.nih.gov/

NIH Clinical Research Trials and You

The National Institutes of Health (NIH) has created a website, NIH Clinical Research Trials and You, to help people learn about clinical trials, why they matter, and how to participate. The site includes questions and answers about clinical trials, guidance on how to find clinical trials through ClinicalTrials.gov and other resources, and stories about the personal experiences of clinical trial participants. Clinical trials are necessary to find better ways to prevent, diagnose, and treat diseases.

Website: https://www.nih.gov/health-information/nih-clinical-research-trials-you

Research Portfolio Online Reporting Tools Expenditures & Results (RePORTER)

RePORTER is a database of information on federally funded scientific and medical research projects being conducted at research institutions.

Website: https://reporter.nih.gov

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• Anheyer D, Leach MJ, Klose P, et al.  Mindfulness-based stress reduction for treating chronic headache: a systematic review and meta-analysis . Cephalalgia . 2019;39(4):544-555.
• Black LI, Barnes PM, Clarke TC, Stussman BA, Nahin RL.  Use of yoga, meditation, and chiropractors among U.S. children aged 4–17 years . NCHS Data Brief, no 324. Hyattsville, MD: National Center for Health Statistics. 2018.
• Breedvelt JJF, Amanvermez Y, Harrer M, et al.  The effects of meditation, yoga, and mindfulness on depression, anxiety, and stress in tertiary education students: a meta-analysis . Frontiers in Psychiatry . 2019;10:193. 
• Burke A, Lam CN, Stussman B, et al.  Prevalence and patterns of use of mantra, mindfulness and spiritual meditation among adults in the United States . BMC Complementary and Alternative Medicine. 2017;17(1):316.
• Carrière K, Khoury B, Günak MM, et al.  Mindfulness‐based interventions for weight loss: a systematic review and meta‐analysis . Obesity Reviews . 2018;19(2):164-177. 
• Cavicchioli M, Movalli M, Maffei C.  The clinical efficacy of mindfulness-based treatments for alcohol and drugs use disorders: a meta-analytic review of randomized and nonrandomized controlled trials . European Addiction Research . 2018;24(3):137-162.
• Cillessen L, Johannsen M, Speckens AEM, et al.  Mindfulness‐based interventions for psychological and physical health outcomes in cancer patients and survivors: a systematic review and meta‐analysis of randomized controlled trials . Psychooncology . 2019;28(12):2257-2269.
• Creswell JD.  Mindfulness interventions . Annual Review of Psychology. 2017;68:491-516.
• Davidson RJ, Kaszniak AW.  Conceptual and methodological issues in research on mindfulness and meditation . American Psychologist. 2015;70(7):581-592.
• Farias M, Maraldi E, Wallenkampf KC, et al.  Adverse events in meditation practices and meditation-based therapies: a systematic review . Acta Psychiatrica Scandinavica. 2020;142(5):374-393. 
• Garland EL, Brintz CE, Hanley AW, et al.  Mind-body therapies for opioid-treated pain: a systematic review and meta-analysis . JAMA Internal Medicine . 2020;180(1):91-105.
• Goldberg SB, Tucker RP, Greene PA, et al. Mindfulness-based interventions for psychiatric disorders: a systematic review and meta-analysis . Clinical Psychology Review . 2018;59:52-60.
• Grant S, Colaiaco B, Motala A, et al.  Mindfulness-based relapse prevention for substance use disorders: a systematic review and meta-analysis . Journal of Addiction Medicine . 2017;11(5):386-396. 
• Haller H, Breilmann P, Schröter M et al.  A systematic review and meta‑analysis of acceptance and mindfulness‑based interventions for DSM‑5 anxiety disorders . Scientific Reports . 2021;11(1):20385.
• Hilton L, Hempel S, Ewing BA, et al.  Mindfulness meditation for chronic pain: systematic review and meta-analysis . Annals of Behavioral Medicine. 2017;51(2):199-213.
• Hirshberg MJ, Goldberg SB, Rosenkranz M, et al.  Prevalence of harm in mindfulness-based stress reduction . Psychological Medicine. August 18, 2020. [Epub ahead of print]. 
• Intarakamhang U, Macaskill A, Prasittichok P.  Mindfulness interventions reduce blood pressure in patients with non-communicable diseases: a systematic review and meta-analysis . Heliyon. 2020;6(4):e03834.
• Khoo E-L, Small R, Cheng W, et al.  Comparative evaluation of group-based mindfulness-based stress reduction and cognitive behavioural therapy for the treatment and management of chronic pain: a systematic review and network meta-analysis . Evidence-Based Mental Health.  2019;22(1):26-35.
• Levine GN, Lange RA, Bairey-Merz CN, et al.  Meditation and cardiovascular risk reduction: a scientific statement from the American Heart Association . Journal of the American Heart Association. 2017;6(10):e002218.
• Nidich S, Mills PJ, Rainforth M, et al.  Non-trauma-focused meditation versus exposure therapy in veterans with post-traumatic stress disorder: a randomised controlled trial . Lancet Psychiatry . 2018;5(12):975-986.
• Niles BL, Mori DL, Polizzi C, et al.  A systematic review of randomized trials of mind-body interventions for PTSD . Journal of Clinical Psychology . 2018;74(9):1485-1508.
• Rogers JM, Ferrari M, Mosely K, et al.  Mindfulness-based interventions for adults who are overweight or obese: a meta-analysis of physical and psychological health outcomes . Obesity Reviews . 2017;18(1):51-67. 
• Rosenkranz MA, Dunne JD, Davidson RJ.  The next generation of mindfulness-based intervention research: what have we learned and where are we headed? Current Opinion in Psychology. 2019;28:179-183.
• Rusch HL, Rosario M, Levison LM, et al.  The effect of mindfulness meditation on sleep quality: a systematic review and meta-analysis of randomized controlled trials . Annals of the New York Academy of Sciences . 2019;1445(1):5-16. 
• Schell LK, Monsef I, Wöckel A, et al. Mindfulness-based stress reduction for women diagnosed with breast cancer. Cochrane Database of Systematic Reviews. 2019;3(3):CD011518. Accessed at cochranelibrary.com on June 3, 2022.
• Semple RJ, Droutman V, Reid BA.  Mindfulness goes to school: things learned (so far) from research and real-world experiences . Psychology in the Schools. 2017;54(1):29-52.
• Shires A, Sharpe L, Davies JN, et al.  The efficacy of mindfulness-based interventions in acute pain: a systematic review and meta-analysis . Pain . 2020;161(8):1698-1707. 
• Van Dam NT, van Vugt MK, Vago DR, et al.  Mind the hype: a critical evaluation and prescriptive agenda for research on mindfulness and meditation . Perspectives on Psychological Science. 2018;13(1):36-61. 

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• American Academy of Pediatrics Section on Integrative Medicine. Mind-body therapies in children and youth. Pediatrics . 2016;138(3):e20161896.
• Coronado-Montoya S, Levis AW, Kwakkenbos L, et al. Reporting of positive results in randomized controlled trials of mindfulness-based mental health interventions. PLoS One . 2016;11(4):e0153220.
• Dakwar E, Levin FR. The emerging role of meditation in addressing psychiatric illness, with a focus on substance use disorders. Harvard Review of Psychiatry . 2009;17(4):254-267.
• Goyal M, Singh S, Sibinga EMS, et al. Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. JAMA Internal Medicine. 2014;174(3):357-368.
• Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research . Washington, DC: National Academies Press; 2011. 
• Kabat-Zinn J, Massion AO, Kristeller J, et al. Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders. American Journal of Psychiatry. 1992;149(7):936-943.
• Ludwig DS, Kabat-Zinn J. Mindfulness in medicine. JAMA. 2008;300(11):1350-1352.
• McKeering P, Hwang Y-S. A systematic review of mindfulness-based school interventions with early adolescents. Mindfulness . 2019;10:593-610.
• Muratori P, Conversano C, Levantini V, et al. Exploring the efficacy of a mindfulness program for boys with attention-deficit hyperactivity disorder and oppositional defiant disorder. Journal of Attention Disorders . 2021;25(11):1544-1553.
• Nahin RL, Rhee A, Stussman B. Use of complementary health approaches overall and for pain management by US adults. JAMA. 2024;331(7):613-615.
• Poissant H, Mendrek A, Talbot N, et al. Behavioral and cognitive impacts of mindfulness-based interventions on adults with attention-deficit hyperactivity disorder: a systematic review. Behavioural Neurology . 2019;2019:5682050.
• Skelly AC, Chou R, Dettori JR, et al. Noninvasive Nonpharmacological Treatment for Chronic Pain: A Systematic Review Update. Comparative Effectiveness Review no. 227. Rockville, MD: Agency for Healthcare Research and Quality; 2020. AHRQ publication no. 20-EHC009.
• Stieger JR, Engel S, Jiang H, et al. Mindfulness improves brain–computer interface performance by increasing control over neural activity in the alpha band. Cerebral Cortex . 2021;31(1):426-438.
• Teasdale JD, Segal ZV, Williams JMG, et al. Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. Journal of Consulting and Clinical Psychology . 2000;68(4):615-623.
• Weng HY, Lewis-Peacock JA, Hecht FM, et al. Focus on the breath: brain decoding reveals internal states of attention during meditation. Frontiers in Human Neuroscience . 2020;14:336.
• Yoshida K, Takeda K, Kasai T, et al. Focused attention meditation training modifies neural activity and attention: longitudinal EEG data in non-meditators. Social Cognitive and Affective Neuroscience . 2020;15(2):215-223.
• Yuan JP, Connolly CG, Henje E, et al. Gray matter changes in adolescents participating in a meditation training. Frontiers in Human Neuroscience . 2020;14:319.
• Zhang J, Díaz-Román A, Cortese S. Meditation-based therapies for attention-deficit/hyperactivity disorder in children, adolescents and adults: a systematic review and meta-analysis.  Evidence-Based Mental Health . 2018;21(3):87-94.

Acknowledgments

Thanks to Elizabeth Ginexi, Ph.D., Erin Burke Quinlan, Ph.D., and David Shurtleff, Ph.D., NCCIH, for their review of this 2022 publication.

This publication is not copyrighted and is in the public domain. Duplication is encouraged.

NCCIH has provided this material for your information. It is not intended to substitute for the medical expertise and advice of your health care provider(s). We encourage you to discuss any decisions about treatment or care with your health care provider. The mention of any product, service, or therapy is not an endorsement by NCCIH.

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