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Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.

Quantitative Research Method

Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.

Mixed Method Research

Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.

Quantitative Research Example:

A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.

Mixed Research Example:

A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.

Applications of Research Methods

Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:

• Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
• Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
• Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
• Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
• Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
• Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
• Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

• Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
• Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
• Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
• Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
• Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
• Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
• Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.

Here are some situations when research methods may be appropriate:

• To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
• To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
• To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
• To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
• To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

• Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
• Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
• Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
• Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
• Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
• Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 3, 2023 3:14 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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198 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

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how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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A Comprehensive Guide to Methodology in Research

Table of Contents

Research methodology plays a crucial role in any study or investigation. It provides the framework for collecting, analyzing, and interpreting data, ensuring that the research is reliable, valid, and credible. Understanding the importance of research methodology is essential for conducting rigorous and meaningful research.

In this article, we'll explore the various aspects of research methodology, from its types to best practices, ensuring you have the knowledge needed to conduct impactful research.

What is Research Methodology?

Research methodology refers to the system of procedures, techniques, and tools used to carry out a research study. It encompasses the overall approach, including the research design, data collection methods, data analysis techniques, and the interpretation of findings.

Research methodology plays a crucial role in the field of research, as it sets the foundation for any study. It provides researchers with a structured framework to ensure that their investigations are conducted in a systematic and organized manner. By following a well-defined methodology, researchers can ensure that their findings are reliable, valid, and meaningful.

When defining research methodology, one of the first steps is to identify the research problem. This involves clearly understanding the issue or topic that the study aims to address. By defining the research problem, researchers can narrow down their focus and determine the specific objectives they want to achieve through their study.

How to Define Research Methodology

Once the research problem is identified, researchers move on to defining the research questions. These questions serve as a guide for the study, helping researchers to gather relevant information and analyze it effectively. The research questions should be clear, concise, and aligned with the overall goals of the study.

After defining the research questions, researchers need to determine how data will be collected and analyzed. This involves selecting appropriate data collection methods, such as surveys, interviews, observations, or experiments. The choice of data collection methods depends on various factors, including the nature of the research problem, the target population, and the available resources.

Once the data is collected, researchers need to analyze it using appropriate data analysis techniques. This may involve statistical analysis, qualitative analysis, or a combination of both, depending on the nature of the data and the research questions. The analysis of data helps researchers to draw meaningful conclusions and make informed decisions based on their findings.

Role of Methodology in Research

Methodology plays a crucial role in research, as it ensures that the study is conducted in a systematic and organized manner. It provides a clear roadmap for researchers to follow, ensuring that the research objectives are met effectively. By following a well-defined methodology, researchers can minimize bias, errors, and inconsistencies in their study, thus enhancing the reliability and validity of their findings.

In addition to providing a structured approach, research methodology also helps in establishing the reliability and validity of the study. Reliability refers to the consistency and stability of the research findings, while validity refers to the accuracy and truthfulness of the findings. By using appropriate research methods and techniques, researchers can ensure that their study produces reliable and valid results, which can be used to make informed decisions and contribute to the existing body of knowledge.

Steps in Choosing the Right Research Methodology

Choosing the appropriate research methodology for your study is a critical step in ensuring the success of your research. Let's explore some steps to help you select the right research methodology:

Identifying the Research Problem

The first step in choosing the right research methodology is to clearly identify and define the research problem. Understanding the research problem will help you determine which methodology will best address your research questions and objectives.

Identifying the research problem involves a thorough examination of the existing literature in your field of study. This step allows you to gain a comprehensive understanding of the current state of knowledge and identify any gaps that your research can fill. By identifying the research problem, you can ensure that your study contributes to the existing body of knowledge and addresses a significant research gap.

Once you have identified the research problem, you need to consider the scope of your study. Are you focusing on a specific population, geographic area, or time frame? Understanding the scope of your research will help you determine the appropriate research methodology to use.

Reviewing Previous Research

Before finalizing the research methodology, it is essential to review previous research conducted in the field. This will allow you to identify gaps, determine the most effective methodologies used in similar studies, and build upon existing knowledge.

Reviewing previous research involves conducting a systematic review of relevant literature. This process includes searching for and analyzing published studies, articles, and reports that are related to your research topic. By reviewing previous research, you can gain insights into the strengths and limitations of different methodologies and make informed decisions about which approach to adopt.

During the review process, it is important to critically evaluate the quality and reliability of the existing research. Consider factors such as the sample size, research design, data collection methods, and statistical analysis techniques used in previous studies. This evaluation will help you determine the most appropriate research methodology for your own study.

Formulating Research Questions

Once the research problem is identified, formulate specific and relevant research questions. These questions will guide your methodology selection process by helping you determine what type of data you need to collect and how to analyze it.

Formulating research questions involves breaking down the research problem into smaller, more manageable components. These questions should be clear, concise, and measurable. They should also align with the objectives of your study and provide a framework for data collection and analysis.

When formulating research questions, consider the different types of data that can be collected, such as qualitative or quantitative data. Depending on the nature of your research questions, you may need to employ different data collection methods, such as interviews, surveys, observations, or experiments. By carefully formulating research questions, you can ensure that your chosen methodology will enable you to collect the necessary data to answer your research questions effectively.

Implementing the Research Methodology

After choosing the appropriate research methodology, it is time to implement it. This stage involves collecting data using various techniques and analyzing the gathered information. Let's explore two crucial aspects of implementing the research methodology:

Data Collection Techniques

Data collection techniques depend on the chosen research methodology. They can include surveys, interviews, observations, experiments, or document analysis. Selecting the most suitable data collection techniques will ensure accurate and relevant data for your study.

Data Analysis Methods

Data analysis is a critical part of the research process. It involves interpreting and making sense of the collected data to draw meaningful conclusions. Depending on the research methodology, data analysis methods can include statistical analysis, content analysis, thematic analysis, or grounded theory.

Ensuring the Validity and Reliability of Your Research

In order to ensure the validity and reliability of your research findings, it is important to address these two key aspects:

Understanding Validity in Research

Validity refers to the accuracy and soundness of a research study. It is crucial to ensure that the research methods used effectively measure what they intend to measure. Researchers can enhance validity by using proper sampling techniques, carefully designing research instruments, and ensuring accurate data collection.

Ensuring Reliability in Your Study

Reliability refers to the consistency and stability of the research results. It is important to ensure that the research methods and instruments used yield consistent and reproducible results. Researchers can enhance reliability by using standardized procedures, ensuring inter-rater reliability, and conducting pilot studies.

A comprehensive understanding of research methodology is essential for conducting high-quality research. By selecting the right research methodology, researchers can ensure that their studies are rigorous, reliable, and valid. It is crucial to follow the steps in choosing the appropriate methodology, implement the chosen methodology effectively, and address validity and reliability concerns throughout the research process. By doing so, researchers can contribute valuable insights and advances in their respective fields.

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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• Research Process

Choosing the Right Research Methodology: A Guide for Researchers

• 3 minute read
• 33.4K views

Table of Contents

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.

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15 Types of Research Methods

Research methods refer to the strategies, tools, and techniques used to gather and analyze data in a structured way in order to answer a research question or investigate a hypothesis (Hammond & Wellington, 2020).

Generally, we place research methods into two categories: quantitative and qualitative. Each has its own strengths and weaknesses, which we can summarize as:

• Quantitative research can achieve generalizability through scrupulous statistical analysis applied to large sample sizes.
• Qualitative research achieves deep, detailed, and nuance accounts of specific case studies, which are not generalizable.

Some researchers, with the aim of making the most of both quantitative and qualitative research, employ mixed methods, whereby they will apply both types of research methods in the one study, such as by conducting a statistical survey alongside in-depth interviews to add context to the quantitative findings.

Below, I’ll outline 15 common research methods, and include pros, cons, and examples of each .

Types of Research Methods

Research methods can be broadly categorized into two types: quantitative and qualitative.

• Quantitative methods involve systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques, providing an in-depth understanding of a specific concept or phenomenon (Schweigert, 2021). The strengths of this approach include its ability to produce reliable results that can be generalized to a larger population, although it can lack depth and detail.
• Qualitative methods encompass techniques that are designed to provide a deep understanding of a complex issue, often in a specific context, through collection of non-numerical data (Tracy, 2019). This approach often provides rich, detailed insights but can be time-consuming and its findings may not be generalizable.

These can be further broken down into a range of specific research methods and designs:

Combining the two methods above, mixed methods research mixes elements of both qualitative and quantitative research methods, providing a comprehensive understanding of the research problem . We can further break these down into:

• Sequential Explanatory Design (QUAN→QUAL): This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
• Sequential Exploratory Design (QUAL→QUAN): This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with qualitative research methods.

Qualitative Research Methods

Qualitative research methods allow for the exploration of phenomena in their natural settings, providing detailed, descriptive responses and insights into individuals’ experiences and perceptions (Howitt, 2019).

These methods are useful when a detailed understanding of a phenomenon is sought.

1. Ethnographic Research

Ethnographic research emerged out of anthropological research, where anthropologists would enter into a setting for a sustained period of time, getting to know a cultural group and taking detailed observations.

Ethnographers would sometimes even act as participants in the group or culture, which many scholars argue is a weakness because it is a step away from achieving objectivity (Stokes & Wall, 2017).

In fact, at its most extreme version, ethnographers even conduct research on themselves, in a fascinating methodology call autoethnography .

The purpose is to understand the culture, social structure, and the behaviors of the group under study. It is often useful when researchers seek to understand shared cultural meanings and practices in their natural settings.

However, it can be time-consuming and may reflect researcher biases due to the immersion approach.

Example of Ethnography

Liquidated: An Ethnography of Wall Street  by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

2. Phenomenological Research

Phenomenological research is a qualitative method focused on the study of individual experiences from the participant’s perspective (Tracy, 2019).

It focuses specifically on people’s experiences in relation to a specific social phenomenon ( see here for examples of social phenomena ).

This method is valuable when the goal is to understand how individuals perceive, experience, and make meaning of particular phenomena. However, because it is subjective and dependent on participants’ self-reports, findings may not be generalizable, and are highly reliant on self-reported ‘thoughts and feelings’.

Example of Phenomenological Research

A phenomenological approach to experiences with technology  by Sebnem Cilesiz represents a good starting-point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

3. Historical Research

Historical research is a qualitative method involving the examination of past events to draw conclusions about the present or make predictions about the future (Stokes & Wall, 2017).

As you might expect, it’s common in the research branches of history departments in universities.

This approach is useful in studies that seek to understand the past to interpret present events or trends. However, it relies heavily on the availability and reliability of source materials, which may be limited.

Common data sources include cultural artifacts from both material and non-material culture , which are then examined, compared, contrasted, and contextualized to test hypotheses and generate theories.

Example of Historical Research

A historical research example might be a study examining the evolution of gender roles over the last century. This research might involve the analysis of historical newspapers, advertisements, letters, and company documents, as well as sociocultural contexts.

4. Content Analysis

Content analysis is a research method that involves systematic and objective coding and interpreting of text or media to identify patterns, themes, ideologies, or biases (Schweigert, 2021).

A content analysis is useful in analyzing communication patterns, helping to reveal how texts such as newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

However, interpretations can be very subjective, which often requires scholars to engage in practices such as cross-comparing their coding with peers or external researchers.

Content analysis can be further broken down in to other specific methodologies such as semiotic analysis, multimodal analysis , and discourse analysis .

Example of Content Analysis

How is Islam Portrayed in Western Media?  by Poorebrahim and Zarei (2013) employs a type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research ). This study by Poorebrahum and Zarei combs through a corpus of western media texts to explore the language forms that are used in relation to Islam and Muslims, finding that they are overly stereotyped, which may represent anti-Islam bias or failure to understand the Islamic world.

5. Grounded Theory Research

Grounded theory involves developing a theory  during and after  data collection rather than beforehand.

This is in contrast to most academic research studies, which start with a hypothesis or theory and then testing of it through a study, where we might have a null hypothesis (disproving the theory) and an alternative hypothesis (supporting the theory).

Grounded Theory is useful because it keeps an open mind to what the data might reveal out of the research. It can be time-consuming and requires rigorous data analysis (Tracy, 2019).

Grounded Theory Example

Developing a Leadership Identity   by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

6. Action Research

Action research is an approach which aims to solve real-world problems and bring about change within a setting. The study is designed to solve a specific problem – or in other words, to take action (Patten, 2017).

This approach can involve mixed methods, but is generally qualitative because it usually involves the study of a specific case study wherein the researcher works, e.g. a teacher studying their own classroom practice to seek ways they can improve.

Action research is very common in fields like education and nursing where practitioners identify areas for improvement then implement a study in order to find paths forward.

Action Research Example

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing   by Ellison and Drew was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

7. Natural Observational Research

Observational research can also be quantitative (see: experimental research), but in naturalistic settings for the social sciences, researchers tend to employ qualitative data collection methods like interviews and field notes to observe people in their day-to-day environments.

This approach involves the observation and detailed recording of behaviors in their natural settings (Howitt, 2019). It can provide rich, in-depth information, but the researcher’s presence might influence behavior.

While observational research has some overlaps with ethnography (especially in regard to data collection techniques), it tends not to be as sustained as ethnography, e.g. a researcher might do 5 observations, every second Monday, as opposed to being embedded in an environment.

Observational Research Example

A researcher might use qualitative observational research to study the behaviors and interactions of children at a playground. The researcher would document the behaviors observed, such as the types of games played, levels of cooperation , and instances of conflict.

8. Case Study Research

Case study research is a qualitative method that involves a deep and thorough investigation of a single individual, group, or event in order to explore facets of that phenomenon that cannot be captured using other methods (Stokes & Wall, 2017).

Case study research is especially valuable in providing contextualized insights into specific issues, facilitating the application of abstract theories to real-world situations (Patten, 2017).

However, findings from a case study may not be generalizable due to the specific context and the limited number of cases studied (Walliman, 2021).

See More: Case Study Advantages and Disadvantages

Example of a Case Study

Scholars conduct a detailed exploration of the implementation of a new teaching method within a classroom setting. The study focuses on how the teacher and students adapt to the new method, the challenges encountered, and the outcomes on student performance and engagement. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other classrooms, as statistical significance has not been established through this qualitative approach.

Quantitative Research Methods

Quantitative research methods involve the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques (Pajo, 2022). The focus is on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

9. Experimental Research

Experimental research is a quantitative method where researchers manipulate one variable to determine its effect on another (Walliman, 2021).

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

This type of research is useful in situations where researchers want to determine causal relationships between variables. However, experimental conditions may not reflect real-world conditions.

Example of Experimental Research

A researcher may conduct an experiment to determine the effects of a new educational approach on student learning outcomes. Students would be randomly assigned to either the control group (traditional teaching method) or the experimental group (new educational approach).

10. Surveys and Questionnaires

Surveys and questionnaires are quantitative methods that involve asking research participants structured and predefined questions to collect data about their attitudes, beliefs, behaviors, or characteristics (Patten, 2017).

Surveys are beneficial for collecting data from large samples, but they depend heavily on the honesty and accuracy of respondents.

They tend to be seen as more authoritative than their qualitative counterparts, semi-structured interviews, because the data is quantifiable (e.g. a questionnaire where information is presented on a scale from 1 to 10 can allow researchers to determine and compare statistical means, averages, and variations across sub-populations in the study).

Example of a Survey Study

A company might use a survey to gather data about employee job satisfaction across its offices worldwide. Employees would be asked to rate various aspects of their job satisfaction on a Likert scale. While this method provides a broad overview, it may lack the depth of understanding possible with other methods (Stokes & Wall, 2017).

11. Longitudinal Studies

Longitudinal studies involve repeated observations of the same variables over extended periods (Howitt, 2019). These studies are valuable for tracking development and change but can be costly and time-consuming.

With multiple data points collected over extended periods, it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes a detailed analysis of change possible.

Perhaps the most relatable example of a longitudinal study is a national census, which is taken on the same day every few years, to gather comparative demographic data that can show how a nation is changing over time.

While longitudinal studies are commonly quantitative, there are also instances of qualitative ones as well, such as the famous 7 Up study from the UK, which studies 14 individuals every 7 years to explore their development over their lives.

Example of a Longitudinal Study

A national census, taken every few years, uses surveys to develop longitudinal data, which is then compared and analyzed to present accurate trends over time. Trends a census can reveal include changes in religiosity, values and attitudes on social issues, and much more.

12. Cross-Sectional Studies

Cross-sectional studies are a quantitative research method that involves analyzing data from a population at a specific point in time (Patten, 2017). They provide a snapshot of a situation but cannot determine causality.

This design is used to measure and compare the prevalence of certain characteristics or outcomes in different groups within the sampled population.

The major advantage of cross-sectional design is its ability to measure a wide range of variables simultaneously without needing to follow up with participants over time.

However, cross-sectional studies do have limitations . This design can only show if there are associations or correlations between different variables, but cannot prove cause and effect relationships, temporal sequence, changes, and trends over time.

Example of a Cross-Sectional Study

Our longitudinal study example of a national census also happens to contain cross-sectional design. One census is cross-sectional, displaying only data from one point in time. But when a census is taken once every few years, it becomes longitudinal, and so long as the data collection technique remains unchanged, identification of changes will be achievable, adding another time dimension on top of a basic cross-sectional study.

13. Correlational Research

Correlational research is a quantitative method that seeks to determine if and to what degree a relationship exists between two or more quantifiable variables (Schweigert, 2021).

This approach provides a fast and easy way to make initial hypotheses based on either positive or  negative correlation trends  that can be observed within dataset.

While correlational research can reveal relationships between variables, it cannot establish causality.

Methods used for data analysis may include statistical correlations such as Pearson’s or Spearman’s.

Example of Correlational Research

A team of researchers is interested in studying the relationship between the amount of time students spend studying and their academic performance. They gather data from a high school, measuring the number of hours each student studies per week and their grade point averages (GPAs) at the end of the semester. Upon analyzing the data, they find a positive correlation, suggesting that students who spend more time studying tend to have higher GPAs.

14. Quasi-Experimental Design Research

Quasi-experimental design research is a quantitative research method that is similar to experimental design but lacks the element of random assignment to treatment or control.

Instead, quasi-experimental designs typically rely on certain other methods to control for extraneous variables.

The term ‘quasi-experimental’ implies that the experiment resembles a true experiment, but it is not exactly the same because it doesn’t meet all the criteria for a ‘true’ experiment, specifically in terms of control and random assignment.

Quasi-experimental design is useful when researchers want to study a causal hypothesis or relationship, but practical or ethical considerations prevent them from manipulating variables and randomly assigning participants to conditions.

Example of Quasi-Experimental Design

A researcher wants to study the impact of a new math tutoring program on student performance. However, ethical and practical constraints prevent random assignment to the “tutoring” and “no tutoring” groups. Instead, the researcher compares students who chose to receive tutoring (experimental group) to similar students who did not choose to receive tutoring (control group), controlling for other variables like grade level and previous math performance.

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

Meta-analysis statistically combines the results of multiple studies on a specific topic to yield a more precise estimate of the effect size. It’s the gold standard of secondary research .

Meta-analysis is particularly useful when there are numerous studies on a topic, and there is a need to integrate the findings to draw more reliable conclusions.

Some meta-analyses can identify flaws or gaps in a corpus of research, when can be highly influential in academic research, despite lack of primary data collection.

However, they tend only to be feasible when there is a sizable corpus of high-quality and reliable studies into a phenomenon.

Example of a Meta-Analysis

The power of feedback revisited (Wisniewski, Zierer & Hattie, 2020) is a meta-analysis that examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes.

Choosing a research method requires a lot of consideration regarding what you want to achieve, your research paradigm, and the methodology that is most valuable for what you are studying. There are multiple types of research methods, many of which I haven’t been able to present here. Generally, it’s recommended that you work with an experienced researcher or research supervisor to identify a suitable research method for your study at hand.

Hammond, M., & Wellington, J. (2020). Research methods: The key concepts . New York: Routledge.

Howitt, D. (2019). Introduction to qualitative research methods in psychology . London: Pearson UK.

Pajo, B. (2022). Introduction to research methods: A hands-on approach . New York: Sage Publications.

Patten, M. L. (2017). Understanding research methods: An overview of the essentials . New York: Sage

Schweigert, W. A. (2021). Research methods in psychology: A handbook . Los Angeles: Waveland Press.

Stokes, P., & Wall, T. (2017). Research methods . New York: Bloomsbury Publishing.

Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Walliman, N. (2021). Research methods: The basics. London: Routledge.

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Research Methodologies

• What are research designs?

What are research methodologies?

Quantitative research methodologies, qualitative research methodologies, mixed method methodologies, selecting a methodology.

• What are research methods?
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According to Dawson (2019),a research methodology is the primary principle that will guide your research.  It becomes the general approach in conducting research on your topic and determines what research method you will use. A research methodology is different from a research method because research methods are the tools you use to gather your data (Dawson, 2019).  You must consider several issues when it comes to selecting the most appropriate methodology for your topic.  Issues might include research limitations and ethical dilemmas that might impact the quality of your research.  Descriptions of each type of methodology are included below.

Quantitative research methodologies are meant to create numeric statistics by using survey research to gather data (Dawson, 2019).  This approach tends to reach a larger amount of people in a shorter amount of time.  According to Labaree (2020), there are three parts that make up a quantitative research methodology:

• Sample population
• How you will collect your data (this is the research method)
• How you will analyze your data

Once you decide on a methodology, you can consider the method to which you will apply your methodology.

Qualitative research methodologies examine the behaviors, opinions, and experiences of individuals through methods of examination (Dawson, 2019).  This type of approach typically requires less participants, but more time with each participant.  It gives research subjects the opportunity to provide their own opinion on a certain topic.

Examples of Qualitative Research Methodologies

• Action research:  This is when the researcher works with a group of people to improve something in a certain environment.  It is a common approach for research in organizational management, community development, education, and agriculture (Dawson, 2019).
• Ethnography:  The process of organizing and describing cultural behaviors (Dawson, 2019).  Researchers may immerse themselves into another culture to receive in "inside look" into the group they are studying.  It is often a time consuming process because the researcher will do this for a long period of time.  This can also be called "participant observation" (Dawson, 2019).
• Feminist research:  The goal of this methodology is to study topics that have been dominated by male test subjects.  It aims to study females and compare the results to previous studies that used male participants (Dawson, 2019).
• Grounded theory:  The process of developing a theory to describe a phenomenon strictly through the data results collected in a study.  It is different from other research methodologies where the researcher attempts to prove a hypothesis that they create before collecting data.  Popular research methods for this approach include focus groups and interviews (Dawson, 2019).

A mixed methodology allows you to implement the strengths of both qualitative and quantitative research methods.  In some cases, you may find that your research project would benefit from this.  This approach is beneficial because it allows each methodology to counteract the weaknesses of the other (Dawson, 2019).  You should consider this option carefully, as it can make your research complicated if not planned correctly.

What should you do to decide on a research methodology?  The most logical way to determine your methodology is to decide whether you plan on conducting qualitative or qualitative research.  You also have the option to implement a mixed methods approach.  Looking back on Dawson's (2019) five "W's" on the previous page , may help you with this process.  You should also look for key words that indicate a specific type of research methodology in your hypothesis or proposal.  Some words may lean more towards one methodology over another.

Quantitative Research Key Words

• How satisfied

Qualitative Research Key Words

• Experiences
• Thoughts/Think
• Relationship
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• Social/Behavioral Sciences Research Guide

Types of Research Methodology

This InfoGuide assists students starting their research proposal and literature review.

• Introduction
• Research Process
• Data Collection Methods
• Anatomy of a Scholarly Article
• Finding a topic
• Identifying a Research Problem
• Problem Statement
• Research Question
• Research Design
• Search Strategies
• Psychology Database Limiters
• Literature Review Search
• Annotated Bibliography
• Writing a Literature Review
• Writing a Research Proposal

This section describes the research methodology: quantitative, qualitative, and mixed methods. Examples of empirical articles for the studies are shown. Mixed methods use both quantitative and qualitative research.

• 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)

Empirical Study

 An empirical study is based on "observation, investigation, or experiment rather than on abstract reasoning, theoretical analysis, or speculation." *  Empirical studies should be divided into the following parts: abstract, introduction, method, results, discussion, and references. Typically these studies also include tables, figures, and charts to display collected data.

An example of APA-cited quantitative and qualitative journal articles are given below. Note that you should include the doi (digital object identifier) if it is available rather than the URL from a database search. The format is this: Last name, Initials. (Year). Article title.  Journal Name ,  Volume (Issue), Page range. DOI or URL.

Convertino, C. M., Marschark, M., Sapere, P., Sarchet, T., & Zupan, M. (2009). Predicting academic success among deaf college students.  Journal of Deaf Studies and Deaf Education,  14 (3), 324-343.  https://doi.org/10.1093/deafed/enp005

Foster, S., & Kinuthia, W. (2003). Deaf persons of Asian American, Hispanic American, and African American backgrounds: A study of intraindividual diversity and identity.  Journal of Deaf Studies and Deaf Education,  8 (3), 271-280.   https://doi.org/10.1093/deafed/eng015

Quantitative vs Qualitative Studies

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Research Methodology & Design

Research methodology.

• Seminal Authors
• Qualitative Research Methods
• Quantitative Research Methods
• Mixed Methods
• Action Research
• Case Study Design
• Constructive Research
• Ethnography
• Phenomenology
• Research Ethics & Quality
• Dissertations
• Organizing Research & Citations This link opens in a new window

NCU Dissertation Center

If you are looking for a document in the Dissertation Center or Applied Doctoral Center and can't find it please contact your Chair or The Center for Teaching and Learning at [email protected]

• NCU Dissertation Center Find valuable resources and support materials to help you through your doctoral journey.
• Applied Doctoral Center Collection of resources to support students in completing their project/dissertation-in-practice as part of the Applied Doctoral Experience (ADE).

Once you are well into your literature review, it is time to start thinking about the study you will design to answer the gap you identified. Which methodology will you use to gather the data for your research? Will you use a qualitative, quantitative, or mixed methods methodology? You will choose a research method that best aligns with your research question.

To evaluate which type of methodology will be most appropriate, you will work closely with your Dissertation Chair. However, as you are reading the literature, take a look at past studies that focus on your topic, or a similar topic. What kind of research methodology do you see being used most often? Once you have an idea about the general methodology type that would suit your research, consult with your Dissertation Chair on the possibility of using that methodology.

Finding a research design strategy is similar to the research process as a whole: first, locate general information on research design and methodologies, then gain background knowledge on the methodology you feel would most appropriately address the type of data you will be collecting, and finally choose a methodology and test/measurement to use in your research. The following techniques outline how to locate information about research methodology from reference books, scholarly articles and dissertations.

• NCU Definition of Terms In order to support your understanding and progress as a scholar, the School of Education has established the following definitions for each of these terms. These definitions are informed by the SAGE Resource Methods Map.
• Books & Videos
• Scholarly Articles

Content: Books, reference works, journal articles, and instructional videos on research methods and design. 

Purpose: Use to learn more about qualitative, quantitative, and mixed methods research. 

Special Features: Includes a methods map, project planner, and "which stats" test

• SAGE Research Methods Title List

The SAGE Research Methods database may be used to locate information about research design and methodology. It includes over 175,000 pages of content from the following sources: encyclopedias, dictionaries, books, journal articles, videos, and major works--resources that bring together the seminal articles about that particular methodology. For a complete list of titles in SAGE Research Methods, please see the SAGE Research Methods Title List.

Using the menu bar, you may browse SAGE Research Methods content by topic, discipline, or content type .

For additional guidance, see the SAGE Research Methods workshop .

After you have located background information about your research design, you may want to locate scholarly journal articles on your research topic that use a particular type of methodology. By looking at research articles that use a particular methodology you can learn a lot about your field. What types of research studies are prevalent? What methodologies are appropriate for a specific research question? How do you construct a research study? What methodologies should you consider for your dissertation research? Few databases allow you to limit your search by research methodology. APA PsycArticles  and APA PsycInfo  are the exceptions; these databases do allow you to limit your search results to show articles that use a particular methodology. 

Content: APA database that offers full-text for journals published by APA, the Canadian Psychological Association, Hogrefe Publishing Group and APA's Educational Publishing Foundation. View the  APA PsycArticles Journal History  for a complete coverage list.

Purpose: Important database for psychology, counseling, and education students.

Special Features: The database is updated bi-weekly all content is available in PDF and HTML formats.

Help using this database.

Content: Journal article database from the American Psychological Association that indexes over 2,500 journals along with book chapters and dissertations.

Purpose: Provides a single source of vetted, authoritative research for users across the behavioral and social sciences.

Special Features: citations in APA Style®, updated bi-weekly, spans 600 years of content

Content: Scholarly journals, e-books, videos and more. 

Purpose: A key multidisciplinary database for most topics. It is one of the library’s main search engines and the most comprehensive single search. 

Note: Certain library databases and publisher content are not searchable in NavigatorSearch, and individual databases may need to be searched to retrieve information due to unique content. NavigatorSearch can be found at https://resources.nu.edu .

Content: National University & NCU student dissertations and literature reviews.

Purpose: Use for foundational research, to locate test instruments and data, and more. 

Special Features: Search by advisor (chair), degree, degree level, or department. Includes a read-aloud feature.

Content: Global student dissertations and literature reviews.

Special Features: Search by advisor (chair), degree, degree level, or department. Includes a read-aloud feature

The ProQuest Dissertations & Theses database (PQDT) is the world's most comprehensive collection of dissertations and theses. It is the database of record for graduate research, with over 2.3 million dissertations and theses included from around the world.

Finally, you may use the ProQuest Dissertations & Theses database to discover graduate and doctoral-level research that has already been conducted on your topic. A similar study may have employed a research methodology appropriate for use in your own dissertation. Check dissertation abstracts to see if the author mentions which methodology was used.

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Types of Research Methods

Business research methods can be defined as “a systematic and scientific procedure of data collection, compilation, analysis, interpretation, and implication pertaining to any business problem” [1] . Types of research methods can be classified into several categories according to nature and purpose of the study, methods of data collection, type of data, research design and other attributes. In methodology chapter of your dissertation, you need to specify and discuss the type of your research according to the following classifications.

Types of Research Methods According to Nature of the Study

Types of the research methods according to the nature of research can be divided into two groups: descriptive and analytical. Descriptive research usually involves surveys and studies that aim to identify the facts. In other words, descriptive research mainly deals with the “description of the state of affairs as it is at present” [2] , and there is no control over variables in descriptive research.

Analytical research, on the other hand, is fundamentally different in a way that “the researcher has to use facts or information already available and analyse these in order to make a critical evaluation of the material”. [3]

Types of Research Methods According to the Purpose of the Study

According to the purpose of the study, types of research methods can be divided into two categories: applied research and fundamental research. Applied research is also referred to as action research, and the fundamental research is sometimes called basic or pure research. The Table 1 below summarizes the main differences between applied research and fundamental research. [4] Similarities between applied and fundamental (basic) research relate to the adoption of a systematic and scientific procedure to conduct the study. [5]

Table 1 Differences between applied and fundamental research

Types of Research Methods according methods of data collection

Types of research methods according to methods of data collection can be broadly divided into two – quantitative and qualitative categories.

Quantitative research “describes, infers, and resolves problems using numbers. Emphasis is placed on the collection of numerical data, the summary of those data and the drawing of inferences from the data” [6] . In simple terms, quantitative research involves figures and calculations in data collection and analysis.  In quantitative studies research findings are presented via tables, graphs and charts.

Qualitative research, on the other hand, is based on words, feelings, emotions, sounds and other non-numerical and unquantifiable elements. It has been noted that “information is considered qualitative in nature if it cannot be analysed by means of mathematical techniques. This characteristic may also mean that an incident does not take place often enough to allow reliable data to be collected” [7]

Types of Research Methods according to the type of data

According to type of data, types of research methods can be divided into two groups – primary research and secondary research. Primary research involves the collection of primary data, i.e. the data which is new, through primary data collection methods such as surveys, interviews, observation etc.

Secondary research, also called desk-based research, is based solely on the secondary data i.e. previously conducted studies. Data sources in secondary researches are books, magazines, industry journals etc. In this type of studies the researcher does not engage in primary data collection.

It is important to note that primary research also involves secondary research, but opposite is not true. Specifically, all primary studies involve collection and analysis of secondary data during literature review stage of the research process. Secondary research, on the other hand, is limited with the collection and analysis of secondary data.

Types of Research Methods according to Research Design

On the basis of research design the types of research methods can be divided into two groups – exploratory and conclusive. Exploratory studies only aim to explore the research area and they do not attempt to offer final and conclusive answers to research questions. Conclusive studies, on the contrary, aim to provide final and conclusive answers to research questions.

Table 2 below illustrates the main differences between exploratory and conclusive research designs:

Table 2 Main differences between exploratory and conclusive research [8]

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance   contains discussions of research types and application of research methods in practice. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis , sampling and others are explained in this e-book in simple words.

John Dudovskiy

[1] Bajpai, N. (2011) “Business Research Methods” Pearson Education India

[2] Herbst, F. & Coldwell, D. (2004) Business Research, Juta and Co Ltd, p.15

[3] Herbst, F. & Coldwell, D. (2004) Business Research, Juta and Co Ltd, p.13

[4] Kumar, R. (2008) “Research Methodology” APH Publishing Corporation

[5] Kumar, R. (2008) “Research Methodology” APH Publishing Corporation

[6] Table adapted from Kumar, R. (2008) “Research Methodology” APH Publishing Corporation

[7] Bajpai, N. (2011) “Business Research Methods” Pearson Education India

[8] Chawla, D. & Sodhi, N. (2011) “Research Methodology: Concepts and Cases” Vikas Publishing House PVT Ltd

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• Published: 25 March 2024

Methods, techniques, assays and protocols

Nature Biomedical Engineering volume  8 ,  pages 201–202 ( 2024 ) Cite this article

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Applied biomedical research needs more of them to be more broadly useful, reproducible and robust.

The scientific process is a structured method, yet not one that is defined in detail. Scientific knowledge and scientific applications advance through rational guesses, observation, the formulation of hypotheses or theories, experimentation and computation, as well as recurrent testing, data analyses, validation and replication (not necessarily in this order).

Acquiring and refining scientific knowledge, and devising science-based solutions to problems, thus requires rationality, objectivity, empiricism, scepticism, peer review and many other scientific practices and values (with accountability and transparency becoming increasingly crucial). Robust methods are also required as well as painstakingly detailed protocols — that is, step-by-step instructions for carrying out a specific method or technique. Regardless of whether a method is referred to as an ‘approach’, ‘a set of techniques’, a ‘workflow’ or an ‘assay’ (or analogous wording, depending on the customs of the research area and the method’s purpose; for instance, an assay usually refers to a test for the detection or quantification of molecules or substances or their activities), a method tends to be broader in scope than a protocol, and sufficiently flexible or modifiable to fit the actual research hypothesis, problem or set-up.

In applied biomedical research, methods and protocols are indispensable for unravelling the workings of biomedically relevant biological systems (molecular, cellular, and at the organ and whole-organism levels) and of mechanisms of disease, and for diagnosing conditions and devising treatments. Biomedical methods and protocols (including laboratory and clinical-trial protocols as well as standard operating procedures) can also serve as common communication and collaboration tools across disciplines, and the most widely used methods have contributed considerably to the most-cited research of all time ( R. Van Noorden et al. Nature 514 , 550–553; 2014 ).

The degree of utility is an essential editorial consideration in how manuscripts that report methods are assessed at Nature Biomedical Engineering . We pursue the publication of methods (but not protocols, which editors at Nature Protocols often commission for recent papers reporting methods) that enable the acquisition of biomedical data or knowledge that were otherwise difficult to capture, that facilitate the efficient analysis of big biomedically relevant datasets, that address a clear biomedical, translational or clinical need, that would seem to have a broader appeal (because they would be applicable to multiple research areas, for instance), that advantageously surpass existing procedures (for example, they are easier, cheaper or more efficient to run or implement), or that enhance the utility of already broadly used methods. (Some of these considerations are also relevant to papers published in Nature Methods , yet the journal focuses on serving researchers actively involved in laboratory practice.)

In this issue of Nature Biomedical Engineering , we highlight eight methods that exemplify these utility considerations.

In one Article, Philipp Holliger and colleagues describe a method for the rapid discovery of antibodies with binding affinities in the low-nanomolar to mid-picomolar range, as they show for the antigens human interleukin-7 and human epidermal growth factor receptor 2. The method leverages array-based assays, next-generation sequencing and high-throughput screening of antibody libraries to probe of the order of 10 8 antibody–antigen interactions, in 3 days. The generated datasets can also be used to train machine-learning models that accelerate the antibody-discovery process. The method has clear broad utility in helping accelerate antibody discovery and the exploration of genotype–phenotype relationships.

Another high-throughput method for the discovery of biomolecules included in this issue serves a clear clinical need. The method allows for the large-scale mass spectrometric quantification of glycopeptides in blood plasma samples as potential disease biomarkers, as Markus Ralser, Christoph Messner and colleagues show by using it to quantify about 1,000 glycopeptide features in the plasma glycoproteomes from patients with COVID-19.

The discovery and development of molecular drugs benefit from knowledge of interactions between the drugs and drug transporters. In another Article in this issue, Giovanni Traverso and co-authors report a method for acquiring interaction profiles between orally administered drugs and intestinal drug transporters. The method requires the modulation of the expression of drug transporters in intact porcine tissue explants via the ultrasound-enhanced delivery of small interfering RNAs. Moreover, the authors used the drug–transporter relationships that they obtained to train a random forest model for the classification of the interaction profiles. Because drug transporters determine the rates of absorption and elimination of therapeutics, by taking into account their interactions with the intestinal transportome, this type of method combining ex vivo tissue and machine learning may help to accelerate the development and formulation of oral drugs.

Technologies for single-cell sequencing allow for the classification of cells into subgroups according to their characteristics and functionality. Yet, the functional profiling of single cells has been a methodological bottleneck, particularly for highly heterogenous immune cells. Lih Feng Cheow and co-authors report an assay for the profiling of the cytotoxicity of killer cells in relation to their cellular phenotype and cytokine secretion at single-cell resolution. It relies on the detection of an initially intracellular fluorescent protein that has been ‘painted’ by a nearby lysed cell on the surface of the lysing killer cell. The assay can be integrated with flow cytometry and single-cell RNA sequencing, and could also be used to analyse molecular pathways associated with cell cytotoxicity and to seek correlates of immune responses.

The secretions of immune cells can affect them and their neighbouring cells, yet identifying genetic regulators of the secretions involves the sorting of a large number of cells according to their secretion patterns. Shana Kelley, Edward Sargent and co-authors describe in an Article also included in this issue a high-throughput method leveraging microfluidics for the analysis of the secretion levels of large populations of immune cells. The method allowed the authors to discover highly co-expressed kinase-coding genes that regulate the secretion of interferon γ by helper T lymphocytes, and may facilitate the discovery of therapeutic targets for autoimmune diseases.

Another microfluidic-based high-throughput screening method, described by Alan Wong and colleagues, enables new possibilities: the discovery of genetic and cellular drivers of the formation of syncytia (multinucleated cells resulting from cell–cell fusions) induced by the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The method takes advantage of droplet microfluidics and strategies for size-exclusion selection to screen (via large-scale mutagenesis and the genome-wide generation of gene knockouts via CRISPR) libraries of spike-variant-expressing ‘sender’ cells fusing with ‘receiver’ cells expressing the receptor angiotensin-converting enzyme 2 (ACE2). This method enables the exploration of any common and unique determinants of the virus-induced formation of syncytia.

This issue also includes an Article describing an extension to the utility of a widely used method. Seok-Hyun Yun, Sheldon Kwok and collaborators show that flow cytometry can be used to track and repeatedly measure the same cells using more markers and fewer colours, as the researchers show for three back-to-back cycles with more than ten markers per cycle. Such multi-pass high-dimensional flow cytometry takes advantage of cellular barcoding via microparticles emitting near-infrared laser light.

In another example of advantageous functionality, Ulrich Keyser and co-authors used DNA barcoding and solid-state nanopores to probe, with higher specificity and speed than had been possible, binding events between catalytically inactive Cas9 (the most used ribonucleoprotein in genome editing) and any pre-defined short sequence of double-stranded DNA. The method requires barcoded linear DNA with Cas9-binding double-stranded DNA overhangs that are sensed via changes in ionic current as the DNA translocates through solid-state nanopores (Fig. 1 ). Assessing the DNA-mismatch tolerance of catalytically inactive nucleases could inform diagnostic applications relying on the detection of single base-pair changes.

The schematic shows a solid-state nanopore (grey), two different DNA nanostructures (11111 and 11001) with two DNA overhangs (green and purple) and either with bound Cas9 (left) or without bound Cas9 (right) mixed together in solution, and the ionic-current traces (bottom) resulting from the translocation of the nanostructures through the nanopore. Figure adapted from the Article by Keyser and colleagues, under a Creative Commons license CC BY 4.0 .

More important than the specific editorial rationale for why we published the methods included in this issue is the reasonable evidence of reproducibility and robustness that they provide. Indeed, validation of the findings with additional datasets or samples, the benchmarking of a new technique against established methods, the verification of the results against alternative methods, and replicability efforts by different experimenters (when possible, under blinded conditions) are a core part of the scientific method.

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Methods, techniques, assays and protocols. Nat. Biomed. Eng 8 , 201–202 (2024). https://doi.org/10.1038/s41551-024-01199-2

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Transforming the understanding and treatment of mental illnesses.

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Decoding the Mind: Basic Science Revolutionizes Treatment of Mental Illnesses

By Linda Brady, Margaret Grabb, Susan Koester, Yael Mandelblat-Cerf, David Panchision, Jonathan Pevsner, Ashlee Van’t-Veer, and Aleksandra Vicentic on behalf of the NIMH Division of Neuroscience and Basic Behavioral Science

March 21, 2024 • 75th Anniversary

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For 75 years, NIMH has transformed the understanding and treatment of mental illnesses through basic and clinical research—bringing hope to millions of people. This Director’s Message, guest written by NIMH’s Division of Neuroscience and Basic Behavioral Science , is part of an anniversary series celebrating this momentous milestone.

The Division of Neuroscience and Basic Behavioral Science (DNBBS) at the National Institute of Mental Health (NIMH) supports research on basic neuroscience, genetics, and basic behavioral science. These are foundational pillars in the quest to decode the human mind and unravel the complexities of mental illnesses.

At NIMH, we are committed to supporting and conducting genomics research as a priority research area . As the institute celebrates its 75th Anniversary , we are spotlighting DNBBS-supported efforts connecting genes to cells to circuits to behavior that have led to a wealth of discoveries and knowledge that can improve the diagnosis, treatment, and prevention of mental illnesses.

Making gene discoveries

Medical conditions often run in families. For instance, if someone in your immediate family has high blood pressure, you are more likely to have it too. It is the same with mental disorders—often they run in families. NIMH is supporting research into human genetics to better understand why this occurs. This research has already led to the discovery of hundreds of gene variants that make us more or less likely to develop a mental disorder.

There are two types of genetic variation: common and rare. Common variation refers to DNA changes often seen in the general population, whereas rare variation is DNA changes found in only a small proportion of the population. Individually, most common gene variants have only a minor impact on the risk for a mental disorder. Instead, most disorders result from many common gene variants that, together, contribute to the risk for and severity of that disorder.

NIMH is committed to uncovering the role of genes in mental disorders with the aim of improving the lives of people who experience them. One of the many ways NIMH contributes to the discovery of common gene variants is by supporting the Psychiatric Genomics Consortium (PGC)   . The consortium of almost 1,000 scientists across the globe, including ones in the NIMH Intramural Research Program and others conducting NIMH-supported research, is one of the largest and most innovative biological investigations in psychiatry.

Global collaborations such as the PGC are critical to amassing the immense sample sizes needed to identify common gene variants. Data from the consortium’s almost one million participants have already led to transformative insights about genetic contributors to mental illnesses and the genetic relationships of these illnesses to each other. To date, studies conducted as part of the consortium have uncovered common variation in over a dozen mental illnesses.

In contrast to common gene variants, rare gene variants are very uncommon in the general population. When they do occur, they often have a major impact on the occurrence of an illness, particularly when they disrupt gene function or regulation. Rare variants involving mutations in a single gene have been linked to several mental disorders, often through NIMH-supported research. For instance, a recent NIMH-funded study found that rare variation in 10 genes substantially increased the risk for schizophrenia. However, it is important to note that genetics is not destiny; even rare variants only raise the risk for mental disorders, but many other factors, including your environment and experiences, play important roles as well.

Because of the strong interest among researchers and the public in understanding how genes translate to changes in the brain and behavior, NIMH has developed a list of human genes associated with mental illnesses. These genes were identified through rare variation studies and are meant to serve as a resource for the research community. The list currently focuses on rare variants, but NIMH plans to continue expanding it as evidence accumulates for additional gene variants (rare or common).

Moreover, mental illnesses are a significant public health burden worldwide . For this reason, NIMH investments in genomics research extend across the globe. NIMH has established the Ancestral Populations Network (APN) to make genomics studies more diverse and shed light on how genetic variation contributes to mental disorders across populations. APN currently includes seven projects with more than 100 researchers across 25 sites worldwide.

Connecting biology to behavior

While hundreds of individual genes have been linked to mental illnesses, the function of most of these genes in the brain remains poorly understood. But high-tech advances and the increased availability of computational tools are enabling researchers to begin unraveling the intricate roles played by genes.

In addition to identifying genetic variation that raises the risk for mental illnesses, NIMH supports research that will help us understand how genes contribute to human behavior. This information is critical to discovering approaches to diagnose, treat, and ultimately prevent or cure mental illnesses.

An NIMH-funded project called the PsychENCODE consortium   focuses on understanding how genes impact brain function. PsychENCODE is furthering knowledge of how gene risk maps onto brain function and dysfunction by cataloging genomic elements in the human brain and studying the actions of different cell types. The PsychENCODE dataset currently includes multidimensional genetic data from the postmortem brains of thousands of people with and without mental disorders.

Findings from the first phase of PsychENCODE were published as a series of 11 papers   examining functional genomics in the developing and adult brains and in mental disorders. A second batch of PsychENCODE papers will be published later this year. These findings help clarify the complex relationships between gene variants and the biological processes they influence.

PsychENCODE and other NIMH-supported projects are committed to sharing biospecimens quickly and openly to help speed research and discovery.

Facilitating these efforts is the NIMH Repository and Genomics Resource (NRGR)   , where samples are stored and shared. NRGR includes hundreds of thousands of samples, such as DNA, RNA, and cell lines, from people with and without mental disorders, along with demographic and diagnostic information.

Another NIMH initiative to connect risk genes to brain function is Scalable and Systematic Neurobiology of Psychiatric and Neurodevelopmental Disorder Risk Genes (SSPsyGene) . This initiative uses cutting-edge techniques to characterize the biological functions of 250 mental health risk genes—within the cells where they are expressed—to better understand how those genes contribute to mental illnesses. By systematically characterizing the biological functions of risk genes in cells, SSPsyGene will empower researchers to learn about biological pathways that may serve as new targets for treatment.

Genes also affect behavior by providing the blueprint for neurons, the basic units of the nervous system. Neurons communicate with each other via circuits in the brain, which enables us to process, integrate, and convey information. NIMH supports many initiatives to study the foundational role of neural networks and brain circuits in shaping diverse mental health-related behaviors like mood, learning, memory, and motivation.

For instance, studies supported through a basic-to-translational science initiative at NIMH focus on modifying neural activity to improve cognitive, emotional, and social processing  . Similarly, another new funding opportunity encourages studies in humans and animals examining how emotional and social cues are represented across brain circuits  to help address a core deficit in many mental disorders. These studies will increase understanding of the biological mechanisms that support behavior throughout life and offer interventions to improve these functions in healthy and clinical populations.

Developing treatments and therapeutics

The gene discovery and biology-to-behavior programs described here will lay the foundation for delivering novel therapeutics. To be prepared to rapidly implement findings from this research, NIMH supports several initiatives to identify behavioral and biological markers for use in clinical studies and increase our ability to translate research into practice.

Through its therapeutics discovery research programs , NIMH advances early stage discovery and development studies in humans and early efficacy trials for mental disorders. Taking these efforts a step further, NIMH supports the National Cooperative Drug Discovery/Development Groups for the Treatment of Mental Disorders , which encourage public–private partnerships to accelerate the discovery and development of novel therapeutics and new biomarkers for use in human trials. Moreover, NIMH is one of several institutes and centers in the NIH Blueprint Neurotherapeutics Network  , launched to enable neuroscientists in academia and biotechnology companies to develop new drugs for nervous system disorders.

For the treatments of tomorrow, NIMH is building a new research program called Pre-Clinical Research on Gene Therapies for Rare Genetic Neurodevelopmental Disorders  , which encourages early stage research to optimize gene therapies to treat disorders with prominent cognitive, social, or affective impairment. In parallel, NIMH’s Planning Grants for Natural History Studies of Rare Genetic Neurodevelopmental Disorders  encourage the analysis of pre-existing data from people with rare disorders to learn about disease progression and enable future clinical trials with these populations.

NIMH's Division of Neuroscience and Basic Behavioral Science supports many different research projects that help us learn about genes and gene functions, how the brain develops and works, and impacts on behavior. By investing in basic neuroscience, genetics, and behavioral research, we're trying to find new targets for treatment and develop better therapies for mental disorders. We're hopeful these efforts will lead to new ways to treat and prevent mental illnesses in the near future and, ultimately, improve the lives of people in this country and across the globe.

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DNA-Based Water Quality Monitoring Methods to Support Aquatic and Human Health Webinar

• April 24, 2024 from 2 to 3pm ET ( Webinar series )
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About the Webinar

1. Becoming Uncultured: Daily Recreational Water Quality Monitoring and Public Notification at Chicago Beaches Using qPCR

Beach water monitoring and notification is vital for protecting the health of beachgoers. Traditionally, beach monitoring has relied on culturing fecal indicator bacteria using methods that require 18-24 hours to generate results, thus providing outdated information for current beach management decisions. In the 2012 Recreational Water Quality Criteria (RWQC), the USEPA endorsed a qPCR method for rapidly measuring enterococci. Since 2015, through a partnership between Chicago Park District (CPD) and the University of Illinois Chicago School of Public Health (UIC SPH), Chicagoans have daily beach monitoring notifications typically within four hours. Having a well-trained team and consistent daily schedule proved essential for delivering timely and high-quality qPCR results to guide same-day beach management decisions. Given that culture-based results from the previous day can often lead to erroneous beach management decisions, the rapid molecular method should be regarded as the benchmark for public health protection. In this presentation, the discussion will focus on how, following a two-year pilot program of rapid molecular testing of beach water samples, in 2017, Chicago became the first large U.S. city to issue same-day water-quality warnings for all its public recreational beaches and has successfully done so every year after that. The CPD and UIC SPH partnership illustrates that true daily beach monitoring using same-day water quality results is an achievable goal.

Presenter: Abhilasha Shrestha, PhD, University of Illinois Chicago Abhilasha is a Research Assistant Professor within the Environmental and Occupational Health Sciences Department at the University of Illinois Chicago School of Public Health (Chicago, IL). Dr. Shrestha conducts research primarily focused on water quality and its implications for public health. Her research interests include investigating various indicator targets and genes to rapidly assess infectious agents in water. Since 2015, she has overseen the management and operation of Chicago’s Lake Michigan beach monitoring project, conducted in collaboration with the Chicago Park District. In addition to her work on beach monitoring, her research also focuses on identifying and mitigating different bacterial sources of pollution at public Lake Michigan beaches in Chicago, utilizing microbial source tracking. Furthermore, Dr. Shrestha spearheaded a wastewater surveillance project for SARS-CoV-2 in Kisumu, Kenya, from 2022 to 2023. She is engaged in ongoing global health water research projects in Kenya and Nepal, contributing significantly to the understanding and management of waterborne health risks on an international scale.

2. Standard Control Material for Quantitative Real-Time PCR Recreational Water Quality Monitoring

Fecal pollution remains a significant challenge for recreational water quality managers. The use of quantitative real-time PCR (qPCR) methods is increasing, allowing for same day beach water quality public notification and identification of key fecal pollution sources. However, widespread implementation requires access to a high-quality standard control material. This presentation describes a collaboration between the U.S. Environmental Protection Agency and the National Institute of Standards and Technology to develop Standard Reference Material® 2917 (SRM 2917), SRM 2917 “fit for purpose” performance assessment, and implications for qPCR recreational water monitoring implementation.

Presenter: Orin Shanks, PhD, EPA Office of Research and Development Orin is a senior scientist with the EPA’s Center for Environmental Measurement and Modeling. His current research activities focus on the development and implementation of quantitative nucleic acid-based fecal pollution diagnostic tools, advances in molecular method data analyses and visualization, as well as the persistence of genetic material in environmental scenarios.  In addition to research activities, he also provides technical assistance to EPA program offices and regions, states, tribes, and other local groups with an interest in clean and safe waters.

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The APS Annual Convention includes these extended educational sessions that offer attendees the opportunity to learn research methods and techniques from prominent psychological scientists.

Workshops are open to Convention registrants only and require additional registration fees. Workshops can be added when you register for the APS Convention. If you’ve finalized your registration, please follow these instructions  to add a workshop .

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THURSDAY, MAY 23

Introduction to Structural Equation Modeling in the Psychological Sciences

Presenter: Tim Hayes, Florida International University

Thursday, May 23, 9:00 AM – 12:50 PM  

Structural Equation Modeling (SEM) combines common factor analysis with multiple regression to allow researchers to assess true score relations among constructs of theoretical interest. This workshop presents an overview of the logic, implementation, and interpretation of SEMs. Topics covered include: path analysis, confirmatory factor analysis, and structural regression analysis. 

Prerequisite :

• A standard graduate course in linear regression analysis
• Software packages: lavaan (R) and Mplus.
• Bring a laptop (fully charged).

Measurement, Not Schmeasurement

Presenter: Jessica Flake, McGill University

We assume that psychological measures produce meaningful numbers: higher satisfaction scores indeed represent more satisfaction. Measurement is a fundamental part of psychological research, and our scores require thorough and transparent evaluation of their validity. This workshop will cover how to evaluate and refine scales using psychometric methods and open science practices.

• An introductory and intermediate courses in statistics with knowledge of regression.

Understanding Bayesian: An Introduction to Key Concepts

Presenter: Brian Leventhal, Jame Madison University

This workshop introduces steps of Bayesian analysis:

• specifying a prior
• using a likelihood
• forming a posterior
• making inferences

Attendees will be able to articulate major considerations of a Bayesian analysis, contrast Bayesian and Frequentist approaches, and identify components of Bayesian research.

Prerequisite:

• Participants should have an understanding of basic descriptive and inferential statistics (e.g., hypothesis testing, p-values, confidence intervals).
• Familiarity with linear regression and maximum likelihood estimation will be beneficial, but not required. 

Data Visualization in R for Researchers Who Do Not Use R

Presenters: Emily Nordmann, University of Glasgow and Wilhelmiina Toivo, University of Glasgow

Thursday, May 23, 1:00 PM – 3:50 PM  

In this session we’ll cover why you should use R for data visualization followed by an introduction to boxplots, histograms, violin-plots, density plots, bar charts, scatterplots, and more complex layered plots using ggplot. No prior knowledge of R is required.

• Please work through the workshop prep at  https://emilynordmann.github.io/aps-dataviz/index.html . This will include installing R and the necessary packages to run the workshop, as well as introducing some basic terminology to help support the session.

Dynamic Structural Equation Modeling (DSEM) in Mplus

Presenter: Ellen Hamaker, Utrecth University

Thursday, May 23, 1:00 PM – 4:50 PM           

This workshop provides a first introduction—both theoretically and practically—into dynamic structural equation modeling (DSEM). DSEM combines time series analysis, multilevel modeling, and structural equation modeling, and allows for a flexible and elegant approach to intensive longitudinal data such as obtained with daily diaries, experience sampling, and ambulatory assessments.

• Bring a laptop (fully charged) with Mplus installed or use the demo version of Mplus to participate in the practical portion.

Power Analysis in Structural Equation Modeling (SEM)

Presenter: Y. Andre Wang, University of Toronto

Thursday, May 23, 1:00 PM – 4:50 PM    

This workshop will introduce attendees to power analysis in SEM and offer a hands-on tutorial. Attendees will learn how to connect power considerations (e.g., to detect specific target effects, to assess model fit) to their research goals, and how to conduct power analysis on their own models using R or pwrSEM, a point-and-click web application.

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• Working knowledge of R is not necessary but preferred.
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This workshop provides a comprehensive introduction to generalized linear models (GLMs), a popular approach for modeling binary and count dependent variables in psychological science. It will provide accessible coverage of both theory and application, including hands-on demonstrations for data analysis and visualization in R and state-of-the-science methods for moderation analysis.

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• A working knowledge of linear regression principles and at least some familiarity with the R statistical software language.

Experience Sampling Methods and Implementation

Presenter: Sabrina Thai, Brock University

Friday, May 24, 10:30 AM – 12:20 PM

Learn how to create your own experience-sampling smartphone app using ExperienceSampler, and how to integrate ExperienceSampler with existing survey software. We will also discuss issues related to conducting experience sampling studies: design decisions, best practices, data organization, and data analysis. 

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• Bring your laptops (fully charged) to follow along with the workshop slides.

A Beginner’s Guide to Qualitative Research

Presenter : Jaclyn Siegel, The University of Chicago

Friday, May 24, 10:30 AM – 12:20 PM

This beginner-friendly workshop will lead researchers through some of the basics of conducting qualitative research (e.g., different analytic methods, positionality), including practical elements of qualitative work (e.g., sample sizes). The workshop will end with a deep dive into thematic analysis, a popular approach to analyzing qualitative data in psychology. 

Prerequisite : none

Creating Computationally Reproducible Manuscripts

Presenter : Jason Geller, Princeton University

Friday, May 24, 1:00 PM – 2:20 PM

Open Science practices, which emphasize transparency, reproducibility, and accessibility, are indeed becoming increasingly important in the psychological community. In this workshop, you will learn to set up a reproducible workflow to create a publication-ready manuscript that combines data, R or Python code, text, and references.  

Prerequisites :

• R/Rstudio 
• Basic knowledge of R or Python

Introduction to Multilevel Modeling

Presenter : Jason Rights, The University of British Columbia

Friday, May 24, 1:00 PM – 2:50 PM

Multilevel modeling (MLM) is widely used in psychology and other fields to analyze nested data structures (e.g., students nested within schools or repeated measures nested within individuals). This workshop will provide a brief introduction to MLM, including both theoretical foundations as well as tools for practical application.

• Knowledge of regression modeling
• Bring a laptop (fully charged) with R & Rstudio installed

SATURDAY, MAY 25

Writing for a Popular Audience to Disseminate Your Work and Broaden Your Impact

Presenter : Andrew Devendorf, University of South Florida

Saturday, May 25, 10:30 AM – 11:50 AM

Although psychologists can benefit society by sharing their research with the public, most lack training on translating ideas into an accessible, engaging, and meaningful package. This workshop will overview the process of writing for, and pitching to, the popular press. Strategies and challenges related to scientific communication will be discussed.    

Data Storytelling Training

Presenter : Lisa Cantrell, Stories of Science

Saturday, May 25, 1:00 PM – 2:20 PM

How do you make a research presentation compelling? One of the biggest secrets in science communication is this: the same narrative strategies that Hollywood uses for creating compelling movies are those that we should be using to talk about our research findings.  In this workshop, participants experience demos of research presentations told with and without storytelling components and then discuss how a story format pushes the audience’s thinking forward about the research. 

Participants will have the opportunity to practice sharing their data story in small groups and by the end of the session, participants will have a draft of their own research data story and a method for turning their future studies into data stories for presentations at conferences, job talks, and speaking engagements.

The Art of the Elevator Pitch

Presenter: Tammy Spence, Stories of Science

Saturday, May 25, 2:30 PM – 3:50 PM

The elevator pitch. We talk about it often. We say it is important for networking and sharing our research. And yet when it comes to actually doing it, we find ourselves using jargon, spending too much time on details that aren’t important, or not giving the right context for our audience. The elevator pitch is, at its core, a story that should be compelling, focused, and clear. 

This workshop is intended to guide participants step by step through building their own compelling, clear, and focused elevator pitch—specifically around their research—and then honing it to 60 seconds. By the end of the session, participants will have a draft of a 60-second elevator pitch and 1-2 short anecdotes that they can use to illustrate their research.

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Title: mm1: methods, analysis & insights from multimodal llm pre-training.

Abstract: In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, including both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

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• Primary Research | Definition, Types, & Examples

Primary Research | Definition, Types, & Examples

Published on January 14, 2023 by Tegan George . Revised on January 12, 2024.

Primary research is a research method that relies on direct data collection , rather than relying on data that’s already been collected by someone else. In other words, primary research is any type of research that you undertake yourself, firsthand, while using data that has already been collected is called secondary research .

Primary research is often used in qualitative research , particularly in survey methodology, questionnaires, focus groups, and various types of interviews . While quantitative primary research does exist, it’s not as common.

Table of contents

When to use primary research, types of primary research, examples of primary research, advantages and disadvantages of primary research, other interesting articles, frequently asked questions.

Primary research is any research that you conduct yourself. It can be as simple as a 2-question survey, or as in-depth as a years-long longitudinal study . The only key is that data must be collected firsthand by you.

Primary research is often used to supplement or strengthen existing secondary research. It is usually exploratory in nature, concerned with examining a research question where no preexisting knowledge exists. It is also sometimes called original research for this reason.

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Primary research can take many forms, but the most common types are:

• Surveys and questionnaires
• Observational studies
• Interviews and focus groups

Surveys and questionnaires collect information about a group of people by asking them questions and analyzing the results. They are a solid choice if your research topic seeks to investigate something about the characteristics, preferences, opinions, or beliefs of a group of people.

Surveys and questionnaires can take place online, in person, or through the mail. It is best to have a combination of open-ended and closed-ended questions, and how the questions are phrased matters. Be sure to avoid leading questions, and ask any related questions in groups, starting with the most basic ones first.

Observational studies are an easy and popular way to answer a research question based purely on what you, the researcher, observes. If there are practical or ethical concerns that prevent you from conducting a traditional experiment , observational studies are often a good stopgap.

There are three types of observational studies: cross-sectional studies , cohort studies, and case-control studies. If you decide to conduct observational research, you can choose the one that’s best for you. All three are quite straightforward and easy to design—just beware of confounding variables and observer bias creeping into your analysis.

Similarly to surveys and questionnaires, interviews and focus groups also rely on asking questions to collect information about a group of people. However, how this is done is slightly different. Instead of sending your questions out into the world, interviews and focus groups involve two or more people—one of whom is you, the interviewer, who asks the questions.

There are 3 main types of interviews:

• Structured interviews ask predetermined questions in a predetermined order.
• Unstructured interviews are more flexible and free-flowing, proceeding based on the interviewee’s previous answers.
• Semi-structured interviews fall in between, asking a mix of predetermined questions and off-the-cuff questions.

While interviews are a rich source of information, they can also be deceptively challenging to do well. Be careful of interviewer bias creeping into your process. This is best mitigated by avoiding double-barreled questions and paying close attention to your tone and delivery while asking questions.

Alternatively, a focus group is a group interview, led by a moderator. Focus groups can provide more nuanced interactions than individual interviews, but their small sample size means that external validity is low.

Primary research can often be quite simple to pursue yourself. Here are a few examples of different research methods you can use to explore different topics.

Primary research is a great choice for many research projects, but it has distinct advantages and disadvantages.

Advantages of primary research

Advantages include:

• The ability to conduct really tailored, thorough research, down to the “nitty-gritty” of your topic . You decide what you want to study or observe and how to go about doing that.
• You maintain control over the quality of the data collected, and can ensure firsthand that it is objective, reliable , and valid .
• The ensuing results are yours, for you to disseminate as you see fit. You maintain proprietary control over what you find out, allowing you to share your findings with like-minded individuals or those conducting related research that interests you for replication or discussion purposes.

Disadvantages of primary research

Disadvantages include:

• In order to be done well, primary research can be very expensive and time consuming. If you are constrained in terms of time or funding, it can be very difficult to conduct your own high-quality primary research.
• Primary research is often insufficient as a standalone research method, requiring secondary research to bolster it.
• Primary research can be prone to various types of research bias . Bias can manifest on the part of the researcher as observer bias , Pygmalion effect , or demand characteristics . It can occur on the part of participants as a Hawthorne effect or social desirability bias .

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

• Chi square 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

The 3 main types of primary research are:

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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