layout of research methodology

How it works

Published by Nicolas at March 21st, 2024 , Revised On March 12, 2024

The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one.

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount.

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions.

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
Interviews: In-depth conversations with participants, offering qualitative insights.
Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

The research paper we write have:

Precision and Clarity
Zero Plagiarism
High-level Encryption
Authentic Sources

Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions.

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
Random Assignment: Participants are randomly assigned to different experimental conditions.
Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
Large Sample Size: Surveys often target a large and diverse group of participants.
Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

Observation and Data Collection: This involves observing and documenting without manipulating variables.
Objective Description: Aim to provide an unbiased and factual account of the subject.
Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
Contextual Understanding: Aims to understand the context and unique characteristics of the case.
Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

Participant Observation: Researchers actively participate in the community or setting.
Holistic Perspective: Focuses on the interconnectedness of cultural elements.
Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

Constant Comparison: Data is continually compared and analyzed during the research process.
Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

Flexibility: Allows for flexibility in data collection and analysis.
Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

Structured Data Collection: Involves systematic and structured data collection methods.
Objective Presentation: Aims to provide an unbiased and factual account of the subject.
Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

Causal Relationships: Seeks to establish causal relationships between variables.
Controlled Variables : Often involves controlling certain variables to isolate causal factors.
Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

Snapshot View: Provides a snapshot of a population at a specific moment.
Efficiency: More efficient in terms of time and resources.
Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

Temporal Sequence: Allows for the examination of changes over time.
Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

Causality Inference: Enables the inference of cause-and-effect relationships.
Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

Natural Settings: Data is often collected in natural settings without intervention.
Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour.

Designing Effective Surveys

Objective Design:

Clearly define the research objectives to guide the survey design.
Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

Use a structured format with standardized questions for consistency.
Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

Conduct pilot tests to identify and rectify potential issues with survey design.
Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

Develop a robust sampling strategy to ensure a representative participant group.
Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

Build rapport with participants to create a comfortable and open environment.
Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

Frame open-ended questions to encourage detailed responses.
Allow participants to express their thoughts and perspectives freely.

Active Listening:

Practice active listening to understand areas and gather rich data.
Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

Obtain informed consent and assure participants of confidentiality.
Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

Actively immerse yourself in the setting or group being observed.
Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

Maintain detailed and reflective field notes during observations.
Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

Be conscious of ethical considerations, ensuring respect for participants.
Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

Maintain a more detached and objective stance during non-participant observation.
Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

Enhance the reliability of data by reducing observer bias.
Develop clear observation protocols and guidelines.

Contextual Understanding:

Strive for a thorough understanding of the observed context.
Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

Locate and access archives relevant to the research topic.
Collaborate with institutions or repositories holding valuable data.

Data Verification:

Verify the accuracy and reliability of archived data.
Cross-reference with other sources to ensure data integrity.

Ethical Use:

Adhere to ethical guidelines when using existing data.
Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

Address the possibility of incomplete or inaccurate archival records.
Acknowledge limitations and uncertainties in the data.

Temporal Bias:

Recognize potential temporal biases in archived data.
Consider the historical context and changes that may impact interpretation.

Access Limitations:

Address potential limitations in accessing certain archives.
Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

Limited timeframes can compromise the depth and thoroughness of the research process.
Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

Inadequate resources, whether financial or human, can impede the execution of research activities.
Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

Selecting participants in a way that systematically skews the sample can introduce selection bias.
Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

USEFUL LINKS

Learning resources, company details.

How It Works

Automated page speed optimizations for fast site performance

PRO Courses Guides New Tech Help Pro Expert Videos About wikiHow Pro Upgrade Sign In
EDIT Edit this Article
EXPLORE Tech Help Pro About Us Random Article Quizzes Request a New Article Community Dashboard This Or That Game Popular Categories Arts and Entertainment Artwork Books Movies Computers and Electronics Computers Phone Skills Technology Hacks Health Men's Health Mental Health Women's Health Relationships Dating Love Relationship Issues Hobbies and Crafts Crafts Drawing Games Education & Communication Communication Skills Personal Development Studying Personal Care and Style Fashion Hair Care Personal Hygiene Youth Personal Care School Stuff Dating All Categories Arts and Entertainment Finance and Business Home and Garden Relationship Quizzes Cars & Other Vehicles Food and Entertaining Personal Care and Style Sports and Fitness Computers and Electronics Health Pets and Animals Travel Education & Communication Hobbies and Crafts Philosophy and Religion Work World Family Life Holidays and Traditions Relationships Youth
Browse Articles
Learn Something New
Quizzes Hot
This Or That Game
Train Your Brain
Explore More
Support wikiHow
About wikiHow
Log in / Sign up
Education and Communications
College University and Postgraduate
Academic Writing

How to Write Research Methodology

Last Updated: May 27, 2024 Approved

This article was co-authored by Alexander Ruiz, M.Ed. and by wikiHow staff writer, Jennifer Mueller, JD . Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of experience in the education industry, Alexander coaches students to increase their self-awareness and emotional intelligence while achieving skills and the goal of achieving skills and higher education. He holds a BA in Psychology from Florida International University and an MA in Education from Georgia Southern University. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, several readers have written to tell us that this article was helpful to them, earning it our reader-approved status. This article has been viewed 522,885 times.

The research methodology section of any academic research paper gives you the opportunity to convince your readers that your research is useful and will contribute to your field of study. An effective research methodology is grounded in your overall approach – whether qualitative or quantitative – and adequately describes the methods you used. Justify why you chose those methods over others, then explain how those methods will provide answers to your research questions. [1] X Research source

Describing Your Methods

In your restatement, include any underlying assumptions that you're making or conditions that you're taking for granted. These assumptions will also inform the research methods you've chosen.
Generally, state the variables you'll test and the other conditions you're controlling or assuming are equal.

Step 2 Establish your overall methodological approach.

If you want to research and document measurable social trends, or evaluate the impact of a particular policy on various variables, use a quantitative approach focused on data collection and statistical analysis.
If you want to evaluate people's views or understanding of a particular issue, choose a more qualitative approach.
You can also combine the two. For example, you might look primarily at a measurable social trend, but also interview people and get their opinions on how that trend is affecting their lives.

Step 3 Define how you collected or generated data.

For example, if you conducted a survey, you would describe the questions included in the survey, where and how the survey was conducted (such as in person, online, over the phone), how many surveys were distributed, and how long your respondents had to complete the survey.
Include enough detail that your study can be replicated by others in your field, even if they may not get the same results you did. [4] X Research source

Step 4 Provide background for uncommon methods.

Qualitative research methods typically require more detailed explanation than quantitative methods.
Basic investigative procedures don't need to be explained in detail. Generally, you can assume that your readers have a general understanding of common research methods that social scientists use, such as surveys or focus groups.

Step 5 Cite any sources that contributed to your choice of methodology.

For example, suppose you conducted a survey and used a couple of other research papers to help construct the questions on your survey. You would mention those as contributing sources.

Justifying Your Choice of Methods

Step 1 Explain your selection criteria for data collection.

Describe study participants specifically, and list any inclusion or exclusion criteria you used when forming your group of participants.
Justify the size of your sample, if applicable, and describe how this affects whether your study can be generalized to larger populations. For example, if you conducted a survey of 30 percent of the student population of a university, you could potentially apply those results to the student body as a whole, but maybe not to students at other universities.

Step 2 Distinguish your research from any weaknesses in your methods.

Reading other research papers is a good way to identify potential problems that commonly arise with various methods. State whether you actually encountered any of these common problems during your research.

Step 3 Describe how you overcame obstacles.

If you encountered any problems as you collected data, explain clearly the steps you took to minimize the effect that problem would have on your results.

Step 4 Evaluate other methods you could have used.

In some cases, this may be as simple as stating that while there were numerous studies using one method, there weren't any using your method, which caused a gap in understanding of the issue.
For example, there may be multiple papers providing quantitative analysis of a particular social trend. However, none of these papers looked closely at how this trend was affecting the lives of people.

Connecting Your Methods to Your Research Goals

Step 1 Describe how you analyzed your results.

Depending on your research questions, you may be mixing quantitative and qualitative analysis – just as you could potentially use both approaches. For example, you might do a statistical analysis, and then interpret those statistics through a particular theoretical lens.

Step 2 Explain how your analysis suits your research goals.

For example, suppose you're researching the effect of college education on family farms in rural America. While you could do interviews of college-educated people who grew up on a family farm, that would not give you a picture of the overall effect. A quantitative approach and statistical analysis would give you a bigger picture.

Step 3 Identify how your analysis answers your research questions.

If in answering your research questions, your findings have raised other questions that may require further research, state these briefly.
You can also include here any limitations to your methods, or questions that weren't answered through your research.

Step 4 Assess whether your findings can be transferred or generalized.

Generalization is more typically used in quantitative research. If you have a well-designed sample, you can statistically apply your results to the larger population your sample belongs to.

Template to Write Research Methodology

Community Q&A

Organize your methodology section chronologically, starting with how you prepared to conduct your research methods, how you gathered data, and how you analyzed that data. [13] X Research source Thanks Helpful 0 Not Helpful 0
Write your research methodology section in past tense, unless you're submitting the methodology section before the research described has been carried out. [14] X Research source Thanks Helpful 0 Not Helpful 0
Discuss your plans in detail with your advisor or supervisor before committing to a particular methodology. They can help identify possible flaws in your study. [15] X Research source Thanks Helpful 0 Not Helpful 0

↑ http://expertjournals.com/how-to-write-a-research-methodology-for-your-academic-article/
↑ http://libguides.usc.edu/writingguide/methodology
↑ https://www.skillsyouneed.com/learn/dissertation-methodology.html
↑ https://uir.unisa.ac.za/bitstream/handle/10500/4245/05Chap%204_Research%20methodology%20and%20design.pdf
↑ https://elc.polyu.edu.hk/FYP/html/method.htm

About This Article

To write a research methodology, start with a section that outlines the problems or questions you'll be studying, including your hypotheses or whatever it is you're setting out to prove. Then, briefly explain why you chose to use either a qualitative or quantitative approach for your study. Next, go over when and where you conducted your research and what parameters you used to ensure you were objective. Finally, cite any sources you used to decide on the methodology for your research. To learn how to justify your choice of methods in your research methodology, scroll down! Did this summary help you? Yes No

Send fan mail to authors

Reader Success Stories

Prof. Dr. Ahmed Askar

Apr 18, 2020

Did this article help you?

M. Mahmood Shah Khan

Mar 17, 2020

Shimola Makondo

Jul 20, 2019

Zain Sharif Mohammed Alnadhery

Jan 7, 2019

Lundi Dukashe

Feb 17, 2020

Featured Articles

How to Be a Better Person: A Guide to Self-Improvement

Watch Articles

Terms of Use
Privacy Policy
Do Not Sell or Share My Info
Not Selling Info

Get all the best how-tos!

Have a language expert improve your writing

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

Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

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

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

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

Second, decide how you will analyse the data .

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

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

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

Qualitative vs quantitative data

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

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

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

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

Primary vs secondary data

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

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

Descriptive vs experimental data

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

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

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

Prevent plagiarism, run a free check.

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

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

Qualitative analysis methods

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

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

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

Quantitative analysis methods

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

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

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

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.

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

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

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

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.

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

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

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

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

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

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

Is this article helpful?

What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
You can easily answer any questions about your research if they arise at a later stage.
A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
It also helps ensure that ethical guidelines are followed while conducting research.
A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

Sampling: The most common type is probability sampling.
Interviews: Commonly telephonic or done in-person.
Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review: Reviewing existing research or documents to collect evidence for supporting the research.
Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

Let Paperpal help you write the perfect research methods section. Start now!

What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

Measures of frequency (count, percent, frequency)
Measures of central tendency (mean, median, mode)
Measures of dispersion or variation (range, variance, standard deviation)
Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

Correlation: To understand the relationship between two or more variables.
Cross-tabulation: Analyze the relationship between multiple variables.
Regression analysis: Study the impact of independent variables on the dependent variable.
Frequency tables: To understand the frequency of data.
Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

Research objectives, aims, and questions —these would help structure the research design.
Review existing literature to identify any gaps in knowledge.
Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

Got writer’s block? Kickstart your research paper writing with Paperpal now!

How to write a research methodology .

A research methodology should include the following components: 3,9

Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
Research method —this can be quantitative, qualitative, or mixed-method.
Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
Sampling —this involves selecting a representative subset of the population being studied.
Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
Data analysis —describe the data analysis methods you will use once you’ve collected the data.
Research limitations —mention any limitations you foresee while conducting your research.
Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

Streamline Your Research Paper Writing Process with Paperpal

The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.

With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.

Generate an outline: Input some details about your research to instantly generate an outline for your methods section
Develop the section: Use the outline and suggested sentence templates to expand your ideas and develop the first draft.
P araph ras e and trim : Get clear, concise academic text with paraphrasing that conveys your work effectively and word reduction to fix redundancies.
Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.
Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .

You can repeat this process to develop each section of your research manuscript, including the title, abstract and keywords. Ready to write your research papers faster, better, and without the stress? Sign up for Paperpal and start writing today!

Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

Research design
Data collection procedures
Data analysis methods
Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

Participants should not be subjected to harm.
Respect for the dignity of participants should be prioritized.
Full consent should be obtained from participants before the study.
Participants’ privacy should be ensured.
Confidentiality of the research data should be ensured.
Anonymity of individuals and organizations participating in the research should be maintained.
The aims and objectives of the research should not be exaggerated.
Affiliations, sources of funding, and any possible conflicts of interest should be declared.
Communication in relation to the research should be honest and transparent.
Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

Accelerate your research paper writing with Paperpal. Try for free now!

Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.

Experience the future of academic writing – Sign up to Paperpal and start writing for free!

Language and Grammar Rules for Academic Writing

Climatic vs. climactic: difference and examples, you may also like, mla works cited page: format, template & examples, how to ace grant writing for research funding..., powerful academic phrases to improve your essay writing , how to write a high-quality conference paper, how paperpal is enhancing academic productivity and accelerating..., academic editing: how to self-edit academic text with..., 4 ways paperpal encourages responsible writing with ai, what are scholarly sources and where can you..., how to write a hypothesis types and examples , what is academic writing: tips for students.

Reference management. Clean and simple.

What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

Your research method. You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
Explain your instruments. This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
What will you do with your results? How are you going to analyze the data once you have gathered it?
Advise your reader. If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
How will your sampling process go? What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
Any practical limitations? You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
Focus Groups. Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
Observations. This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️ How to do a content analysis

➡️ How to do a thematic analysis

➡️ How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

University Libraries
Research Guides
Topic Guides
Research Methods Guide
Research Design & Method

Research Methods Guide: Research Design & Method

Introduction
Survey Research
Interview Research
Data Analysis
Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

<< Previous: Introduction
Next: Survey Research >>
Last Updated: Aug 21, 2023 10:42 AM

Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

How to Cite Research Paper – All Formats and...

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Process – Steps, Examples and Tips

Institutional Review Board – Application Sample...

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!

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

You Might Also Like:

199 Comments

Thank you for this simple yet comprehensive and easy to digest presentation. God Bless!

You’re most welcome, Leo. Best of luck with your research!

I found it very useful. many thanks

This is really directional. A make-easy research knowledge.

Thank you for this, I think will help my research proposal

Thanks for good interpretation,well understood.

Good morning sorry I want to the search topic

Thank u more

Thank you, your explanation is simple and very helpful.

Very educative a.nd exciting platform. A bigger thank you and I’ll like to always be with you

That’s the best analysis

So simple yet so insightful. Thank you.

This really easy to read as it is self-explanatory. Very much appreciated…

Thanks for this. It’s so helpful and explicit. For those elements highlighted in orange, they were good sources of referrals for concepts I didn’t understand. A million thanks for this.

Good morning, I have been reading your research lessons through out a period of times. They are important, impressive and clear. Want to subscribe and be and be active with you.

Thankyou So much Sir Derek…

Good morning thanks so much for the on line lectures am a student of university of Makeni.select a research topic and deliberate on it so that we’ll continue to understand more.sorry that’s a suggestion.

Beautiful presentation. I love it.

please provide a research mehodology example for zoology

It’s very educative and well explained

Thanks for the concise and informative data.

This is really good for students to be safe and well understand that research is all about

Thank you so much Derek sir🖤🙏🤗

Very simple and reliable

This is really helpful. Thanks alot. God bless you.

very useful, Thank you very much..

thanks a lot its really useful

in a nutshell..thank you!

Thanks for updating my understanding on this aspect of my Thesis writing.

thank you so much my through this video am competently going to do a good job my thesis

Thanks a lot. Very simple to understand. I appreciate 🙏

Very simple but yet insightful Thank you

This has been an eye opening experience. Thank you grad coach team.

Very useful message for research scholars

Really very helpful thank you

yes you are right and i’m left

Research methodology with a simplest way i have never seen before this article.

wow thank u so much

Good morning thanks so much for the on line lectures am a student of university of Makeni.select a research topic and deliberate on is so that we will continue to understand more.sorry that’s a suggestion.

Very precise and informative.

Thanks for simplifying these terms for us, really appreciate it.

Thanks this has really helped me. It is very easy to understand.

I found the notes and the presentation assisting and opening my understanding on research methodology

Good presentation

Im so glad you clarified my misconceptions. Im now ready to fry my onions. Thank you so much. God bless

Thank you a lot.

thanks for the easy way of learning and desirable presentation.

Thanks a lot. I am inspired

Well written

I am writing a APA Format paper . I using questionnaire with 120 STDs teacher for my participant. Can you write me mthology for this research. Send it through email sent. Just need a sample as an example please. My topic is ” impacts of overcrowding on students learning

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.

Thank you so much for this!! God Bless

Thank you. Explicit explanation

Thank you, Derek and Kerryn, for making this simple to understand. I’m currently at the inception stage of my research.

Thnks a lot , this was very usefull on my assignment

excellent explanation

I’m currently working on my master’s thesis, thanks for this! I’m certain that I will use Qualitative methodology.

Thanks a lot for this concise piece, it was quite relieving and helpful. God bless you BIG…

I am currently doing my dissertation proposal and I am sure that I will do quantitative research. Thank you very much it was extremely helpful.

Very interesting and informative yet I would like to know about examples of Research Questions as well, if possible.

I’m about to submit a research presentation, I have come to understand from your simplification on understanding research methodology. My research will be mixed methodology, qualitative as well as quantitative. So aim and objective of mixed method would be both exploratory and confirmatory. Thanks you very much for your guidance.

OMG thanks for that, you’re a life saver. You covered all the points I needed. Thank you so much ❤️ ❤️ ❤️

Thank you immensely for this simple, easy to comprehend explanation of data collection methods. I have been stuck here for months 😩. Glad I found your piece. Super insightful.

I’m going to write synopsis which will be quantitative research method and I don’t know how to frame my topic, can I kindly get some ideas..

Thanks for this, I was really struggling.

This was really informative I was struggling but this helped me.

Thanks a lot for this information, simple and straightforward. I’m a last year student from the University of South Africa UNISA South Africa.

its very much informative and understandable. I have enlightened.

An interesting nice exploration of a topic.

Thank you. Accurate and simple🥰

This article was really helpful, it helped me understanding the basic concepts of the topic Research Methodology. The examples were very clear, and easy to understand. I would like to visit this website again. Thank you so much for such a great explanation of the subject.

Thanks dude

Thank you Doctor Derek for this wonderful piece, please help to provide your details for reference purpose. God bless.

Many compliments to you

Great work , thank you very much for the simple explanation

Thank you. I had to give a presentation on this topic. I have looked everywhere on the internet but this is the best and simple explanation.

thank you, its very informative.

Well explained. Now I know my research methodology will be qualitative and exploratory. Thank you so much, keep up the good work

Well explained, thank you very much.

This is good explanation, I have understood the different methods of research. Thanks a lot.

Great work…very well explanation

Thanks Derek. Kerryn was just fantastic!

Great to hear that, Hyacinth. Best of luck with your research!

Its a good templates very attractive and important to PhD students and lectuter

Thanks for the feedback, Matobela. Good luck with your research methodology.

Thank you. This is really helpful.

You’re very welcome, Elie. Good luck with your research methodology.

Well explained thanks

This is a very helpful site especially for young researchers at college. It provides sufficient information to guide students and equip them with the necessary foundation to ask any other questions aimed at deepening their understanding.

Thanks for the kind words, Edward. Good luck with your research!

Thank you. I have learned a lot.

Great to hear that, Ngwisa. Good luck with your research methodology!

Thank you for keeping your presentation simples and short and covering key information for research methodology. My key takeaway: Start with defining your research objective the other will depend on the aims of your research question.

My name is Zanele I would like to be assisted with my research , and the topic is shortage of nursing staff globally want are the causes , effects on health, patients and community and also globally

Thanks for making it simple and clear. It greatly helped in understanding research methodology. Regards.

This is well simplified and straight to the point

Thank you Dr

I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

Thanks a lot I am relieved of a heavy burden.keep up with the good work

I’m very much grateful Dr Derek. I’m planning to pursue one of the careers that really needs one to be very much eager to know. There’s a lot of research to do and everything, but since I’ve gotten this information I will use it to the best of my potential.

Thank you so much, words are not enough to explain how helpful this session has been for me!

Thanks this has thought me alot.

Very concise and helpful. Thanks a lot

Thank Derek. This is very helpful. Your step by step explanation has made it easier for me to understand different concepts. Now i can get on with my research.

I wish i had come across this sooner. So simple but yet insightful

really nice explanation thank you so much

I’m so grateful finding this site, it’s really helpful…….every term well explained and provide accurate understanding especially to student going into an in-depth research for the very first time, even though my lecturer already explained this topic to the class, I think I got the clear and efficient explanation here, much thanks to the author.

It is very helpful material

I would like to be assisted with my research topic : Literature Review and research methodologies. My topic is : what is the relationship between unemployment and economic growth?

Its really nice and good for us.

THANKS SO MUCH FOR EXPLANATION, ITS VERY CLEAR TO ME WHAT I WILL BE DOING FROM NOW .GREAT READS.

Short but sweet.Thank you

Informative article. Thanks for your detailed information.

I’m currently working on my Ph.D. thesis. Thanks a lot, Derek and Kerryn, Well-organized sequences, facilitate the readers’ following.

great article for someone who does not have any background can even understand

I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

concise and informative.

Thank you very much

How can we site this article is Harvard style?

Very well written piece that afforded better understanding of the concept. Thank you!

Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

Thank too much

Thank you very much for your comprehensive explanation about research methodology so I like to thank you again for giving us such great things.

Good very well explained.Thanks for sharing it.

Thank u sir, it is really a good guideline.

so helpful thank you very much.

Thanks for the video it was very explanatory and detailed, easy to comprehend and follow up. please, keep it up the good work

It was very helpful, a well-written document with precise information.

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/

Your explanation is easily understood. Thank you

Very help article. Now I can go my methodology chapter in my thesis with ease

I feel guided ,Thank you

This simplification is very helpful. It is simple but very educative, thanks ever so much

The write up is informative and educative. It is an academic intellectual representation that every good researcher can find useful. Thanks

Wow, this is wonderful long live.

Nice initiative

thank you the video was helpful to me.

Thank you very much for your simple and clear explanations I’m really satisfied by the way you did it By now, I think I can realize a very good article by following your fastidious indications May God bless you

Thanks very much, it was very concise and informational for a beginner like me to gain an insight into what i am about to undertake. I really appreciate.

very informative sir, it is amazing to understand the meaning of question hidden behind that, and simple language is used other than legislature to understand easily. stay happy.

This one is really amazing. All content in your youtube channel is a very helpful guide for doing research. Thanks, GradCoach.

research methodologies

Please send me more information concerning dissertation research.

Nice piece of knowledge shared….. #Thump_UP

This is amazing, it has said it all. Thanks to Gradcoach

This is wonderful,very elaborate and clear.I hope to reach out for your assistance in my research very soon.

This is the answer I am searching about…

realy thanks a lot

Thank you very much for this awesome, to the point and inclusive article.

Thank you very much I need validity and reliability explanation I have exams

Thank you for a well explained piece. This will help me going forward.

Very simple and well detailed Many thanks

This is so very simple yet so very effective and comprehensive. An Excellent piece of work.

I wish I saw this earlier on! Great insights for a beginner(researcher) like me. Thanks a mil!

Thank you very much, for such a simplified, clear and practical step by step both for academic students and general research work. Holistic, effective to use and easy to read step by step. One can easily apply the steps in practical terms and produce a quality document/up-to standard

Thanks for simplifying these terms for us, really appreciated.

Thanks for a great work. well understood .

This was very helpful. It was simple but profound and very easy to understand. Thank you so much!

Great and amazing research guidelines. Best site for learning research

hello sir/ma’am, i didn’t find yet that what type of research methodology i am using. because i am writing my report on CSR and collect all my data from websites and articles so which type of methodology i should write in dissertation report. please help me. i am from India.

how does this really work?

perfect content, thanks a lot

As a researcher, I commend you for the detailed and simplified information on the topic in question. I would like to remain in touch for the sharing of research ideas on other topics. Thank you

Impressive. Thank you, Grad Coach 😍

Thank you Grad Coach for this piece of information. I have at least learned about the different types of research methodologies.

Very useful content with easy way

Thank you very much for the presentation. I am an MPH student with the Adventist University of Africa. I have successfully completed my theory and starting on my research this July. My topic is “Factors associated with Dental Caries in (one District) in Botswana. I need help on how to go about this quantitative research

I am so grateful to run across something that was sooo helpful. I have been on my doctorate journey for quite some time. Your breakdown on methodology helped me to refresh my intent. Thank you.

thanks so much for this good lecture. student from university of science and technology, Wudil. Kano Nigeria.

It’s profound easy to understand I appreciate

Thanks a lot for sharing superb information in a detailed but concise manner. It was really helpful and helped a lot in getting into my own research methodology.

Comment * thanks very much

This was sooo helpful for me thank you so much i didn’t even know what i had to write thank you!

You’re most welcome 🙂

Simple and good. Very much helpful. Thank you so much.

This is very good work. I have benefited.

Thank you so much for sharing

This is powerful thank you so much guys

I am nkasa lizwi doing my research proposal on honors with the university of Walter Sisulu Komani I m on part 3 now can you assist me.my topic is: transitional challenges faced by educators in intermediate phase in the Alfred Nzo District.

Appreciate the presentation. Very useful step-by-step guidelines to follow.

I appreciate sir

wow! This is super insightful for me. Thank you!

Indeed this material is very helpful! Kudos writers/authors.

I want to say thank you very much, I got a lot of info and knowledge. Be blessed.

I want present a seminar paper on Optimisation of Deep learning-based models on vulnerability detection in digital transactions.

Need assistance

Dear Sir, I want to be assisted on my research on Sanitation and Water management in emergencies areas.

I am deeply grateful for the knowledge gained. I will be getting in touch shortly as I want to be assisted in my ongoing research.

The information shared is informative, crisp and clear. Kudos Team! And thanks a lot!

hello i want to study

Hello!! Grad coach teams. I am extremely happy in your tutorial or consultation. i am really benefited all material and briefing. Thank you very much for your generous helps. Please keep it up. If you add in your briefing, references for further reading, it will be very nice.

All I have to say is, thank u gyz.

Good, l thanks

thank you, it is very useful

Trackbacks/Pingbacks

What Is A Literature Review (In A Dissertation Or Thesis) - Grad Coach - […] the literature review is to inform the choice of methodology for your own research. As we’ve discussed on the Grad Coach blog,…
Free Download: Research Proposal Template (With Examples) - Grad Coach - […] Research design (methodology) […]
Dissertation vs Thesis: What's the difference? - Grad Coach - […] and thesis writing on a daily basis – everything from how to find a good research topic to which…

Submit a Comment Cancel reply

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

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

Print Friendly

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

Free Account
For Digital
For Customer Care
For Human Resources
For Researchers
Financial Services
All Industries

Popular Use Cases

Customer Experience
Employee Experience
Net Promoter Score
Voice of Customer
Customer Success Hub
Product Documentation
Training & Certification
XM Institute
Popular Resources
Customer Stories
Artificial Intelligence

Market Research

Partnerships
Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

English/AU & NZ
Español/Europa
Español/América Latina
Português Brasileiro
REQUEST DEMO

What is design research methodology and why is it important?

What is design research.

Design research is the process of gathering, analyzing and interpreting data and insights to inspire, guide and provide context for designs. It’s a research discipline that applies both quantitative and qualitative research methods to help make well-informed design decisions.

Not to be confused with user experience research – focused on the usability of primarily digital products and experiences – design research is a broader discipline that informs the entire design process across various design fields. Beyond focusing solely on researching with users, design research can also explore aesthetics, cultural trends, historical context and more.

Design research has become more important in business, as brands place greater emphasis on building high-quality customer experiences as a point of differentiation.

Elevate Your Brand's Potential with Qualtrics

Design research vs. market research

The two may seem like the same thing at face value, but really they use different methods, serve different purposes and produce different insights.

Design research focuses on understanding user needs, behaviors and experiences to inform and improve product or service design. Market research , on the other hand, is more concerned with the broader market dynamics, identifying opportunities, and maximizing sales and profitability.

Both are essential for the success of a product or service, but cater to different aspects of its lifecycle.

Design research in action: A mini mock case study

A popular furniture brand, known for its sleek and simple designs, faced an unexpected challenge: dropping sales in some overseas markets. To address this, they turned to design research – using quantitative and qualitative methods – to build a holistic view of the issue.

Company researchers visited homes in these areas to interview members of their target audience and understand local living spaces and preferences. Through these visits, they realized that while the local customers appreciated quality, their choices in furniture were heavily influenced by traditions and regional aesthetics, which the company's portfolio wasn’t addressing.

To further their understanding, the company rolled out surveys, asking people about their favorite materials, colors and furniture functionalities. They discovered a consistent desire for versatile furniture pieces that could serve multiple purposes. Additionally, the preference leaned towards certain regional colors and patterns that echoed local culture.

Armed with these insights, the company took to the drawing board. They worked on combining their minimalist style with the elements people in those markets valued. The result was a refreshed furniture line that seamlessly blended the brand's signature simplicity with local tastes. As this new line hit the market, it resonated deeply with customers in the markets, leading to a notable recovery in sales and even attracting new buyers.

When to use design research

Like most forms of research, design research should be used whenever there are gaps in your understanding of your audience’s needs, behaviors or preferences. It’s most valuable when used throughout the product development and design process.

When differing opinions within a team can derail a design process, design research provides concrete data and evidence-based insights, preventing decisions based on assumptions.

Design research brings value to any product development and design process, but it’s especially important in larger, resource intensive projects to minimize risk and create better outcomes for all.

The benefits of design research

Design research may be perceived as time-consuming, but in reality it’s often a time – and money – saver that can. easily prove to be the difference between strong product-market fit and a product with no real audience.

Deeper customer knowledge

Understanding your audience on a granular level is paramount – without tapping into the nuances of their desires, preferences and pain points, you run the risk of misalignment.

Design research dives deep into these intricacies, ensuring that products and services don't just meet surface level demands. Instead, they can resonate and foster a bond between the user and the brand, building foundations for lasting loyalty .

Efficiency and cost savings

More often than not, designing products or services based on assumptions or gut feelings leads to costly revisions, underwhelming market reception and wasted resources.

Design research offers a safeguard against these pitfalls by grounding decisions in real, tangible insights directly from the target market – streamlining the development process and ensuring that every dollar spent yields maximum value.

New opportunities

Design research often brings to light overlooked customer needs and emerging trends. The insights generated can shift the trajectory of product development, open doors to new and novel solutions, and carve out fresh market niches.

Sometimes it's not just about avoiding mistakes – it can be about illuminating new paths of innovation.

Enhanced competitive edge

In today’s world, one of the most powerful ways to stand out as a business is to be relentlessly user focused. By ensuring that products and services are continuously refined based on user feedback, businesses can maintain a step ahead of competitors.

Whether it’s addressing pain points competitors might overlook, or creating user experiences that are not just satisfactory but delightful, design research can be the foundations for a sharpened competitive edge.

Design research methods

The broad scope of design research means it demands a variety of research tools, with both numbers-driven and people-driven methods coming into play. There are many methods to choose from, so we’ve outlined those that are most common and can have the biggest impact.

This stage is about gathering initial insights to set a clear direction.

Literature review

Simply put, this research method involves investigating existing secondary research, like studies and articles, in your design area. It's a foundational method that helps you understand current knowledge and identify any gaps – think of it like surveying the landscape before navigating through it.

Field observations

By observing people's interactions in real-world settings, we gather genuine insights. Field observations are about connecting the dots between observed behaviors and your design's intended purpose. This method proves invaluable as it can reveal how design choices can impact everyday experiences.

Stakeholder interviews

Talking to those invested in the design's outcome, be it users or experts, is key. These discussions provide first-hand feedback that can clarify user expectations and illuminate the path towards a design that resonates with its audience.

This stage is about delving deeper and starting to shape your design concepts based on what you’ve already discovered.

Design review

This is a closer look at existing designs in the market or other related areas. Design reviews are very valuable because they can provide an understanding of current design trends and standards – helping you see where there's room for innovation or improvement.

Without a design review, you could be at risk of reinventing the wheel.

Persona building

This involves creating detailed profiles representing different groups in your target audience using real data and insights.

Personas help bring to life potential users, ensuring your designs address actual needs and scenarios. By having these "stand-in" users, you can make more informed design choices tailored to specific user experiences.

Putting your evolving design ideas to the test and gauging their effectiveness in the real world.

Usability testing

This is about seeing how real users interact with a design.

In usability testing you observe this process, note where they face difficulties and moments of satisfaction. It's a hands-on way to ensure that the design is intuitive and meets user needs.

Benchmark testing

Benchmark testing is about comparing your design's performance against set standards or competitor products.

Doing this gives a clearer idea of where your design stands in the broader context and highlights areas for improvement or differentiation. With these insights you can make informed decisions to either meet or exceed those benchmarks.

This final stage is about gathering feedback once your design is out in the world, ensuring it stays relevant and effective.

Feedback surveys

After users have interacted with the design for some time, use feedback surveys to gather their thoughts. The results of these surveys will help to ensure that you have your finger on the pulse of user sentiment – enabling iterative improvements.

Remember, simple questions can reveal a lot about what's working and where improvements might be needed.

Focus groups

These are structured, moderator-led discussions with a small group of users . The aim is for the conversation to dive deep into their experiences with the design and extract rich insights – not only capturing what users think but also why.

Start your free 30-day trial of DesignXM® today

Understanding what your market wants before they even know it can set your business apart in a saturated market. That's where DesignXM by Qualtrics® comes in – offering a top-tier platform designed for those who want to lead, not just follow.

Why dive into DesignXM?

Quick insights: Get to the heart of the matter faster and make informed decisions swiftly
Cost-effective research: Cut back on outsourced studies and get more bang for your buck, all while ensuring top-notch quality
Premium quality: Stand shoulder to shoulder with leading brands, using best-in-class research methods

Qualtrics // Experience Management

Qualtrics, the leader and creator of the experience management category, is a cloud-native software platform that empowers organizations to deliver exceptional experiences and build deep relationships with their customers and employees.

With insights from Qualtrics, organizations can identify and resolve the greatest friction points in their business, retain and engage top talent, and bring the right products and services to market. Nearly 20,000 organizations around the world use Qualtrics’ advanced AI to listen, understand, and take action. Qualtrics uses its vast universe of experience data to form the largest database of human sentiment in the world. Qualtrics is co-headquartered in Provo, Utah and Seattle.

December 20, 2023

Top market research analyst skills for 2024

November 7, 2023

Brand Experience

The 4 market research trends redefining insights in 2024

September 14, 2023

How BMG and Loop use data to make critical decisions

August 21, 2023

Designing for safety: Making user consent and trust an organizational asset

June 27, 2023

The fresh insights people: Scaling research at Woolworths Group

June 20, 2023

Bank less, delight more: How Bankwest built an engine room for customer obsession

June 16, 2023

How Qualtrics Helps Three Local Governments Drive Better Outcomes Through Data Insights

April 1, 2023

Academic Experience

How to write great survey questions (with examples)

Stay up to date with the latest xm thought leadership, tips and news., request demo.

Ready to learn more about Qualtrics?

The Complete Guide to UX Research Methods

UX research provides invaluable insight into product users and what they need and value. Not only will research reduce the risk of a miscalculated guess, it will uncover new opportunities for innovation.

By Miklos Philips

Miklos is a UX designer, product design strategist, author, and speaker with more than 18 years of experience in the design field.

PREVIOUSLY AT

“Empathy is at the heart of design. Without the understanding of what others see, feel, and experience, design is a pointless task.” —Tim Brown, CEO of the innovation and design firm IDEO

User experience (UX) design is the process of designing products that are useful, easy to use, and a pleasure to engage. It’s about enhancing the entire experience people have while interacting with a product and making sure they find value, satisfaction, and delight. If a mountain peak represents that goal, employing various types of UX research is the path UX designers use to get to the top of the mountain.

User experience research is one of the most misunderstood yet critical steps in UX design. Sometimes treated as an afterthought or an unaffordable luxury, UX research, and user testing should inform every design decision.

Every product, service, or user interface designers create in the safety and comfort of their workplaces has to survive and prosper in the real world. Countless people will engage our creations in an unpredictable environment over which designers have no control. UX research is the key to grounding ideas in reality and improving the odds of success, but research can be a scary word. It may sound like money we don’t have, time we can’t spare, and expertise we have to seek.

In order to do UX research effectively—to get a clear picture of what users think and why they do what they do—e.g., to “walk a mile in the user’s shoes” as a favorite UX maxim goes, it is essential that user experience designers and product teams conduct user research often and regularly. Contingent upon time, resources, and budget, the deeper they can dive the better.

Website and mobile app UX research methods and techniques.

What Is UX Research?

There is a long, comprehensive list of UX design research methods employed by user researchers , but at its center is the user and how they think and behave —their needs and motivations. Typically, UX research does this through observation techniques, task analysis, and other feedback methodologies.

There are two main types of user research: quantitative (statistics: can be calculated and computed; focuses on numbers and mathematical calculations) and qualitative (insights: concerned with descriptions, which can be observed but cannot be computed).

Quantitative research is primarily exploratory research and is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics. Some common data collection methods include various forms of surveys – online surveys , paper surveys , mobile surveys and kiosk surveys , longitudinal studies, website interceptors, online polls, and systematic observations.

This user research method may also include analytics, such as Google Analytics .

Google Analytics is part of a suite of interconnected tools that help interpret data on your site’s visitors including Data Studio , a powerful data-visualization tool, and Google Optimize, for running and analyzing dynamic A/B testing.

Quantitative data from analytics platforms should ideally be balanced with qualitative insights gathered from other UX testing methods , such as focus groups or usability testing. The analytical data will show patterns that may be useful for deciding what assumptions to test further.

Qualitative user research is a direct assessment of behavior based on observation. It’s about understanding people’s beliefs and practices on their terms. It can involve several different methods including contextual observation, ethnographic studies, interviews, field studies, and moderated usability tests.

Jakob Nielsen of the Nielsen Norman Group feels that in the case of UX research, it is better to emphasize insights (qualitative research) and that although quant has some advantages, qualitative research breaks down complicated information so it’s easy to understand, and overall delivers better results more cost effectively—in other words, it is much cheaper to find and fix problems during the design phase before you start to build. Often the most important information is not quantifiable, and he goes on to suggest that “quantitative studies are often too narrow to be useful and are sometimes directly misleading.”

Not everything that can be counted counts, and not everything that counts can be counted. William Bruce Cameron

Design research is not typical of traditional science with ethnography being its closest equivalent—effective usability is contextual and depends on a broad understanding of human behavior if it is going to work.

Nevertheless, the types of user research you can or should perform will depend on the type of site, system or app you are developing, your timeline, and your environment.

Top UX Research Methods and When to Use Them

Here are some examples of the types of user research performed at each phase of a project.

Card Sorting : Allows users to group and sort a site’s information into a logical structure that will typically drive navigation and the site’s information architecture. This helps ensure that the site structure matches the way users think.

Contextual Interviews : Enables the observation of users in their natural environment, giving you a better understanding of the way users work.

First Click Testing : A testing method focused on navigation, which can be performed on a functioning website, a prototype, or a wireframe.

Focus Groups : Moderated discussion with a group of users, allowing insight into user attitudes, ideas, and desires.

Heuristic Evaluation/Expert Review : A group of usability experts evaluating a website against a list of established guidelines .

Interviews : One-on-one discussions with users show how a particular user works. They enable you to get detailed information about a user’s attitudes, desires, and experiences.

Parallel Design : A design methodology that involves several designers pursuing the same effort simultaneously but independently, with the intention to combine the best aspects of each for the ultimate solution.

Personas : The creation of a representative user based on available data and user interviews. Though the personal details of the persona may be fictional, the information used to create the user type is not.

Prototyping : Allows the design team to explore ideas before implementing them by creating a mock-up of the site. A prototype can range from a paper mock-up to interactive HTML pages.

Surveys : A series of questions asked to multiple users of your website that help you learn about the people who visit your site.

System Usability Scale (SUS) : SUS is a technology-independent ten-item scale for subjective evaluation of the usability.

Task Analysis : Involves learning about user goals, including what users want to do on your website, and helps you understand the tasks that users will perform on your site.

Usability Testing : Identifies user frustrations and problems with a site through one-on-one sessions where a “real-life” user performs tasks on the site being studied.

Use Cases : Provide a description of how users use a particular feature of your website. They provide a detailed look at how users interact with the site, including the steps users take to accomplish each task.

US-based full-time freelance UX designers wanted

You can do user research at all stages or whatever stage you are in currently. However, the Nielsen Norman Group advises that most of it be done during the earlier phases when it will have the biggest impact. They also suggest it’s a good idea to save some of your budget for additional research that may become necessary (or helpful) later in the project.

Here is a diagram listing recommended options that can be done as a project moves through the design stages. The process will vary, and may only include a few things on the list during each phase. The most frequently used methods are shown in bold.

UX research methodologies in the product and service design lifecycle.

Reasons for Doing UX Research

Here are three great reasons for doing user research :

To create a product that is truly relevant to users

If you don’t have a clear understanding of your users and their mental models, you have no way of knowing whether your design will be relevant. A design that is not relevant to its target audience will never be a success.

To create a product that is easy and pleasurable to use

A favorite quote from Steve Jobs: “ If the user is having a problem, it’s our problem .” If your user experience is not optimal, chances are that people will move on to another product.

To have the return on investment (ROI) of user experience design validated and be able to show:

An improvement in performance and credibility
Increased exposure and sales—growth in customer base
A reduced burden on resources—more efficient work processes

Aside from the reasons mentioned above, doing user research gives insight into which features to prioritize, and in general, helps develop clarity around a project.

What is UX research: using analytics data for quantitative research study.

What Results Can I Expect from UX Research?

In the words of Mike Kuniaysky, user research is “ the process of understanding the impact of design on an audience. ”

User research has been essential to the success of behemoths like USAA and Amazon ; Joe Gebbia, CEO of Airbnb is an enthusiastic proponent, testifying that its implementation helped turn things around for the company when it was floundering as an early startup.

Some of the results generated through UX research confirm that improving the usability of a site or app will:

Increase conversion rates
Increase sign-ups
Increase NPS (net promoter score)
Increase customer satisfaction
Increase purchase rates
Boost loyalty to the brand
Reduce customer service calls

Additionally, and aside from benefiting the overall user experience, the integration of UX research into the development process can:

Minimize development time
Reduce production costs
Uncover valuable insights about your audience
Give an in-depth view into users’ mental models, pain points, and goals

User research is at the core of every exceptional user experience. As the name suggests, UX is subjective—the experience that a person goes through while using a product. Therefore, it is necessary to understand the needs and goals of potential users, the context, and their tasks which are unique for each product. By selecting appropriate UX research methods and applying them rigorously, designers can shape a product’s design and can come up with products that serve both customers and businesses more effectively.

Understanding the basics

How do you do user research in ux.

UX research includes two main types: quantitative (statistical data) and qualitative (insights that can be observed but not computed), done through observation techniques, task analysis, and other feedback methodologies. The UX research methods used depend on the type of site, system, or app being developed.

What are UX methods?

There is a long list of methods employed by user research, but at its center is the user and how they think, behave—their needs and motivations. Typically, UX research does this through observation techniques, task analysis, and other UX methodologies.

What is the best research methodology for user experience design?

The type of UX methodology depends on the type of site, system or app being developed, its timeline, and environment. There are 2 main types: quantitative (statistics) and qualitative (insights).

What does a UX researcher do?

A user researcher removes the need for false assumptions and guesswork by using observation techniques, task analysis, and other feedback methodologies to understand a user’s motivation, behavior, and needs.

Why is UX research important?

UX research will help create a product that is relevant to users and is easy and pleasurable to use while boosting a product’s ROI. Aside from these reasons, user research gives insight into which features to prioritize, and in general, helps develop clarity around a project.

UserResearch

Miklos Philips

London, United Kingdom

Member since May 20, 2016

About the author

World-class articles, delivered weekly.

By entering your email, you are agreeing to our privacy policy .

Toptal Designers

Adobe Creative Suite Experts
Agile Designers
AI Designers
Art Direction Experts
Augmented Reality Designers
Axure Experts
Brand Designers
Creative Directors
Dashboard Designers
Digital Product Designers
E-commerce Website Designers
Full-Stack Designers
Information Architecture Experts
Interactive Designers
Mobile App Designers
Mockup Designers
Presentation Designers
Prototype Designers
SaaS Designers
Sketch Experts
Squarespace Designers
User Flow Designers
User Research Designers
Virtual Reality Designers
Visual Designers
Wireframing Experts
View More Freelance Designers

Join the Toptal ® community.

MBA Knowledge Base

Business • Management • Technology

Home » Research Methodology » Contents and Layout of Research Report

Contents and Layout of Research Report

Contents of research report.

The researcher must keep in mind that his research report must contain following aspects:

Purpose of study
Significance of his study or statement of the problem
Review of literature
Methodology
Interpretation of data
Conclusions and suggestions
Bibliography

These can be discussed in detail as under:

(1) Purpose of study:

Research is one direction oriented study. He should discuss the problem of his study. He must give background of the problem. He must lay down his hypothesis of the study. Hypothesis is the statement indicating the nature of the problem. He should be able to collect data, analyze it and prove the hypothesis . The importance of the problem for the advancement of knowledge or removed of some evil may also be explained. He must use review of literature or the data from secondary source for explaining the statement of the problems.

(2) Significance of study:

Research is re-search and hence the researcher may highlight the earlier research in new manner or establish new theory. He must refer earlier research work and distinguish his own research from earlier work. He must explain how his research is different and how his research topic is different and how his research topic is important. In a statement of his problem, he must be able to explain in brief the historical account of the topic and way in which he can make and attempt. In his study to conduct the research on his topic.

(3) Review of Literature :

Research is a continuous process. He cannot avoid earlier research work. He must start with earlier work. He should note down all such research work, published in books, journals or unpublished thesis. He will get guidelines for his research from taking a review of literature . He should collect information in respect of earlier research work. He should enlist them in the given below:

Author/researcher
Title of research /Name of book
Year of publication
Objectives of his study
Conclusion/suggestions

Then he can compare this information with his study to show separate identity of his study. He must be honest to point out similarities and differences of his study from earlier research work.

(4) Methodology:

It is related to collection of data. There are two sources for collecting data; primary and secondary. Primary data is original and collected in field work, either through questionnaire interviews. The secondary data relied on library work. Such primary data are collected by sampling method . The procedure for selecting the sample must be mentioned. The methodology must give various aspects of the problem that are studied for valid generalization about the phenomena. The scales of measurement must be explained along with different concepts used in the study.

While conducting a research based on field work, the procedural things like definition of universe, preparation of source list must be given. We use case study method , historical research etc. He must make it clear as to which method is used in his research work. When questionnaire is prepared, a copy of it must be given in appendix.

(5) Interpretation of data :

Mainly the data collected from primary source need to be interpreted in systematic manner. The tabulation must be completed to draw conclusions. All the questions are not useful for report writing . One has to select them or club them according to hypothesis or objectives of study .

(6) Conclusions/suggestions:

Data analysis forms the crux of the research problem . The information collected in field work is useful to draw conclusions of study. In relation with the objectives of study the analysis of data may lead the researcher to pin point his suggestions. This is the most important part of study. The conclusions must be based on logical and statistical reasoning. The report should contain not only the generalization of inference but also the basis on which the inferences are drawn. All sorts of proofs, numerical and logical, must be given in support of any theory that has been advanced. He should point out the limitations of his study.

(7) Bibliography:

The list of references must be arranged in alphabetical order and be presented in appendix. The books should be given in first section and articles are in second section and research projects in the third. The pattern of bibliography is considered convenient and satisfactory from the point of view of reader.

(8) Appendices:

The general information in tabular form which is not directly used in the analysis of data but which is useful to understand the background of study can be given in appendix.

Layout of the Research Report

There is scientific method for the layout of research report . The layout of research report means as to what the research report should contain. The contents of the research report are noted below:

Preliminary Page

(1) Preliminary Pages:

These must be title of the research topic and data. There must be preface of foreword to the research work. It should be followed by table of contents. The list of tables, maps should be given.

(2) Main Text:

It provides the complete outline of research report along with all details. The title page is reported in the main text. Details of text are given continuously as divided in different chapters.

(a) Introduction
(b) Statement of the problem
(c) The analysis of data
(d) The implications drawn from the results
(e) The summary

(a) Introduction :

Its purpose is to introduce the research topic to readers. It must cover statement of the research problem , hypotheses, objectives of study, review of literature, and the methodology to cover primary and secondary data, limitations of study and chapter scheme. Some may give in brief in the first chapter the introduction of the research project highlighting the importance of study. This is followed by research methodology in separate chapter.

The methodology should point out the method of study, the research design and method of data collection.

(b) Statement of the problem :

This is crux of his research. It highlights main theme of his study. It must be in nontechnical language. It should be in simple manner so ordinary reader may follow it. The social research must be made available to common man. The research in agricultural problems must be easy for farmers to read it.

Data so collected should be presented in systematic manner and with its help, conclusions can be drawn. This helps to test the hypothesis . Data analysis must be made to confirm the objectives of the study.

(d) Implications of Data :

The results based on the analysis of data must be valid. This is the main body of research. It contains statistical summaries and analysis of data. There should be logical sequence in the analysis of data. The primary data may lead to establish the results. He must have separate chapter on conclusions and recommendations. The conclusions must be based on data analysis. The conclusions must be such which may lead to generalization and its applicability in similar circumstances. The conditions of research work limiting its scope for generalization must be made clear by the researcher.

(e) Summary :

This is conclusive part of study. It makes the reader to understand by reading summary the knowledge of the research work. This is also a synopsis of study.

(3) End Matter:

It covers relevant appendices covering general information, the concepts and bibliography. The index may also be added to the report.

Writing the Research Report
Preparing a Research Report
Referencing a Research Report
Steps Involved in Drafting a Research Report
The Research Problem
The Purpose of Research
Significance of Research
The Basic Types of Research
Classification and Tabulation of Data in Research
Documentary Sources of Information in Research

One thought on “ Contents and Layout of Research Report ”

Any more research’s is welcome….

Understanding the use of co-design methods for research involving older adults living with HIV: A scoping review protocol

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft

Affiliations Division of Geriatric Medicine, Department of Medicine, Sinai Health System and University Health Network, Toronto, ON, Canada, Undergraduate Medical Education, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

Affiliations Department of Occupational Science and Occupational Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada, KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada, Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Roles Writing – original draft, Writing – review & editing

Affiliation Division of Geriatric Medicine, Department of Medicine, Sinai Health System and University Health Network, Toronto, ON, Canada

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing

Roles Conceptualization, Data curation, Methodology, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Writing – review & editing

Affiliations Faculty of Social Work–Saskatoon Campus, University of Regina, Regina, SK, Canada, Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – review & editing

Affiliations Infectious Diseases, Department of Medicine, University Health Network, Toronto, ON, Canada, Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada, CIHR Canadian HIV Trials Network, Vancouver, BC, Canada

Roles Conceptualization, Writing – review & editing

* E-mail: [email protected]

Affiliations Division of Geriatric Medicine, Department of Medicine, Sinai Health System and University Health Network, Toronto, ON, Canada, Department of Occupational Science and Occupational Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Paige Brown,
Hardeep Singh,
Esther Su,
Luxey Sirisegaram,
Sarah E. P. Munce,
Andrew D. Eaton,
Alice Zhabokritsky,
Stuart McKinlay,
Kristina M. Kokorelias

Published: May 30, 2024
https://doi.org/10.1371/journal.pone.0303580
Reader Comments

There is a growing population of adults aged 50 years or older living with HIV, facing unique challenges in care due to age, minority status, and stigma. Co-design methodologies, aligning with patient-centered care, have potential for informing interventions addressing the complex needs of older adults with HIV. Despite challenges, co-design has shown promise in empowering older individuals to actively participate in shaping their care experiences. The scoping review outlined here aims to identify gaps in existing co-design work with this population, emphasizing the importance of inclusivity based on PROGRESS-Plus characteristics for future patient-oriented research. This scoping review protocol is informed by the Joanna Briggs Institute Manual to explore co-design methods in geriatric HIV care literature. The methodology encompasses six stages: 1) developing research questions, 2) creating a search strategy, 3) screening and selecting evidence, 4) data extraction, 5) data analysis using content analysis, and 6) consultation with key stakeholders, including community partners and individuals with lived experience. The review will involve a comprehensive literature search, including peer-reviewed databases and gray literature, to identify relevant studies conducted in the past 20 years. The inclusive criteria focus on empirical data related to co-design methods in HIV care for individuals aged 50 or older, aiming to inform future research and co-design studies in geriatric HIV care. The study will be limited by the exclusion of papers not published or translated to English. Additionally, the varied terminology used to describe co-design across different research may result in the exclusion of articles using alternative terms. The consultation with key stakeholders will be crucial for translating insights into meaningful co-design solutions for virtual HIV care, aiming to provide a comprehensive synthesis that informs evidence-based strategies and addresses disparities in geriatric HIV care.

Citation: Brown P, Singh H, Su E, Sirisegaram L, Munce SEP, Eaton AD, et al. (2024) Understanding the use of co-design methods for research involving older adults living with HIV: A scoping review protocol. PLoS ONE 19(5): e0303580. https://doi.org/10.1371/journal.pone.0303580

Editor: Graeme Hoddinott, Stellenbosch University, SOUTH AFRICA

Received: January 12, 2024; Accepted: April 25, 2024; Published: May 30, 2024

Copyright: © 2024 Brown et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: No datasets were generated or analysed during the current study protocol. All relevant data from this study will be made available upon study completion.

Funding: This research was supported by the AGE-WELL Network of Centres of Excellent (NCE) (AW-CAT-2023-03) Inc and the Canadian Frailty Network’s (CFN) Catalyst Funding Program in Healthy Aging. The AGE-WELL NCE and CFN are funded by the Government of Canada through the Networks of Centres of Excellence program.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The population of adults aged 50 years or older living with human immunodeficiency virus (HIV), a chronic infection, is continuing to grow as advancements in HIV care, including antiretroviral therapy (ART), have become available. In 2021, approximately 63,000 individuals in Canada and 38.3 million individuals worldwide were living with HIV [ 1 , 2 ]. It is further estimated that 50% of individuals living with HIV are aged 50 years or older in Canada. Globally, other countries are identifying similar trends; for example, in the Netherlands, 73% of individuals living with HIV will be aged 50 years or older by 2030 [ 3 , 4 ]. Further complicating the care provided to older adults living with HIV are the barriers to care faced by this population group. Older adults living with HIV are more likely to be from underserved populations, exemplified by data showing that 49% of older adults with HIV belong to minority groups. These findings may present challenges to receiving culturally appropriate care [ 5 ]. Moreover, this patient group may face discrimination and stigma due to their HIV-positive status, leading to higher levels of loneliness and smaller social support networks. Collectively, these barriers to care, in addition to ageism, may contribute to worse health outcomes and decreased quality of life [ 4 , 6 , 7 ].

Co-design approaches have become increasingly prominent in healthcare research as they align with the emerging priority of patient-centred care. Furthermore, co-design approaches may also hold benefits in research design methods for work with older adults with HIV. Co-design and related terms, including co-production or co-creation, refer to research approaches to design new care interventions or care improvements that engage end-users, health care staff, and related advocacy groups in an equal collaboration [ 8 ]. Originating from community-based participatory design, co-design allows for immersive, reflective feedback from the user throughout the entirety of a project, from conceptualization to consultation [ 9 ]. The aim of engaging end-users in research is to ensure that research results can be meaningful, relevant, and useful to the population they intend to benefit [ 10 , 11 ].

Although challenges exist for co-design methods involving older people, it has been noted that older people are interested in participating in the design of their own care [ 12 ]. One study used co-design in the creation of cultural mental health interventions for older adults living in Hong Kong, finding that there was value in an active participatory process and that the approach was perceived by participants to be “empowering” [ 13 ]. Historically, in HIV research, community-based participatory research methods have been conducted to address HIV prevention, care, and treatment efforts. Engagement with individuals living with HIV as partners in research design, rather than the population of interest, [ 14 , 15 ] and the development of co-design and community-based research methods in response to the HIV epidemic, has been fundamental to community engagement [ 16 ]. In the context of HIV care, co-design has been used in several studies to engage relevant stakeholders, including individuals with HIV and patient partners [ 17 – 19 ]. Consequently, while traditional approaches may not fully address the complex needs of older adults living with HIV, co-design methods have the potential to tailor interventions to meet this group’s unique needs and empower participants to have a voice in shaping their own care experiences. The inclusion of marginalized or underrepresented perspectives will be particularly important for tailoring culturally responsive care models that meet the specific needs of older adults living with HIV. Co-designed interventions have the potential to enhance patient-provider communication and overall healthcare experiences in a culturally responsive manner. This scoping review aims to understand the gaps existing in co-design work with older adults with HIV, as no known synthesis of information exists at present. Specifically, understanding what groups of individuals may be excluded from co-design methods in relation to PROGRESS-Plus characteristics (eg, place of residence, race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, social capital) [ 20 ] will inform future co-design research with older adults with HIV to emphasize patient-oriented research [ 21 ].

To better understand co-design methods used in the context of research methods and interventions for older adults living with HIV, we plan to complete a scoping review to examine the geriatric HIV care literature for older adults living with HIV. A scoping review methodology is appropriate due to the broad nature of co-design and allows exploration of knowledge across study designs [ 22 , 23 ]. The modified Arksey and O’Malley [ 23 , 24 ] scoping review frameworks and Joanna Briggs Institute (JBI) Manual for scoping review studies framework [ 25 ] will be followed. The proposed framework includes the following 1) developing a research question, 2) developing a search strategy, 3) evidence screening and study selection, 4) data extraction, 5) data analysis; and 6) consultation [ 23 – 25 ]. The review reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-analysis for Protocols (PRISMA-P) [ 26 ] and the PRISMA extension for scoping reviews (PRISMA-ScR) [ 27 ] guidelines. The preliminary literature search will begin in April 2024, and we anticipate this review will be completed in late 2024.

Stage 1: Developing a research question

The research team consisting of researchers and a patient partner were consulted to develop and clarify the research questions. This scoping review aims to understand various co-design and community-based research methods implemented within the geriatric HIV care literature to inform future methodologies in geriatric HIV research and co-design studies. This scoping review outlined here will address the following research questions:

What is the extent, range, and nature of geriatric-HIV research methods that have used co-design methods?
What co-design methods have been used to develop HIV interventions for individuals aged 50 or older?
Who is being excluded from existing co-design studies for older adults living with HIV (as defined by the PROGRESS+ characteristics)?

Stage 2: Developing a search strategy

Literature will be found using a search strategy created and drafted by an Information Specialist and Health Science Librarian (TBD) in consultation with the review team. The following text words and subject headings will be included in the search, as they pertain to concepts addressed by the research questions: ‘older adults’ ‘HIV’ ‘co-design’ ‘co-creation’ ‘co-production’ ‘community-based research’ and ‘geriatric-HIV interventions’. To ensure a breadth of understanding, we will define older adults in the context of HIV care as aged 50 years or older [ 28 ]. The search will be limited to articles written or translated to English. Papers published prior to the 2003 will not be considered within the search to find the most up-to-date literature informing future research, due to resource constraints. To minimize search errors and enhance comprehensiveness, the search will be peer-reviewed using the Peer Review of Electronic Search Strategies [ 29 ]. After the search strategy is refined and finalized, the search will be conducted by the librarian in MEDLINE(R) ALL (in Ovid, including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily) and then translated it into NLM’s PubMed OVID Embase + Embase Classic, EBSCO’s CINAHL Complete, Clarivate’s Web of Science Core Collection, and Elsevier’s Scopus.

Relevant gray literature will be found by handsearching using similar search terms as the scientific search through Google Scholar, Open Grey, open Google searches and relevant websites, such as WHO, UK National Research Register, CADTH’s “Grey Matters”, New York Academy of Medicine’s Grey Literature Report, the Canadian Medical Association InfoBase and the National Institute for Heath and Care Excellence–Guidance.

Due to the broad nature of this search, included studies’ reference lists will also be searched to identify any missed studies in the search. Forward citation searching, consisting of a citation index that cites eligible studies in the scoping review [ 30 ], will also be conducted to identify any studies missed in the search.

Stage 3: Evidence screening and study selection

Endnote will be used to remove duplicate articles and Covidence will be used to complete screening of articles [ 31 , 32 ]. Two reviewers will independently screen and review articles (PB and KMK), first completing title/abstract screening (level 1-screening) and then full-text article screening (level 2-screening). Any discrepancies in screening will be discussed by the reviewers and resolved by team-based discussion. Articles will be included and excluded based on criteria listed in Table 1 .

PPT PowerPoint slide
PNG larger image
TIFF original image

https://doi.org/10.1371/journal.pone.0303580.t001

Stage 4: Data extraction

Data extraction will be completed by two independent reviewers using a data extraction form in Covidence. Similar to screening, the data extraction will be an iterative process, with final categories decided upon as reviewers become more familiar with data and studies reviewed [ 24 ]. Data extracted will include, but is not limited to, author last name, year, study type, setting, country, methods, methodology, characteristics of intervention, delivery method (i.e., virtual, in-person, telephone), participant characteristics, provider characteristics, results, and key conclusions. First, each reviewer will extract data from a random sample of five included studies. Once >75% agreement is reached between the two reviewers, then half of the included studies will be screened. Reviewers will then meet to discuss discrepancies and if poor agreement is found, the data abstraction form will be clarified, and conflicts will be resolved by a third reviewer. Following this, the senior responsible author will check all data extraction to ensure agreement. Appraisal for risk of bias and quality of the studies will not be performed, as per the Joanna Briggs Institute Manual [ 22 , 33 ].

Stage 5: Data analysis

Data extracted will be analyzed using a content analysis as recommended for scoping reviews [ 34 ] Explicit rules of coding will be used to create content categories. The data will be coded manually by the research team and will be grouped based on the main components of the studies extracted including main components of the co-design model, methodology, framework used, population, location of study, population age, associated methods (eg, one-on-one interviews, workshops, etc.), and other PROGRESS-Plus characteristics (eg, race, ethnicity, language, culture, level of education) [ 20 ].

Stage 6: Consultation

The findings of this scoping review are intended to guide co-design with older persons living with HIV. As such, through co-design, we will consult with several knowledge users including community partners and diverse individuals with lived experience with HIV aged 50 years or older. Prior to data extraction, we will invite key stakeholders, including HIV care specialists, advisory committee members including older adults living with HIV, and HIV community organizations (i.e., healthcare organizations, social service agencies, housing support and shelters) to share feedback on the data extraction table to ensure the data adequately reflects the community-based perspective. Our advisory committee consists of individuals involved in geriatric HIV policymaking, research, advocacy, and individuals with lived experience with HIV and aging. Consulting community partners will provide insights beyond the literature reviewed to gain deeper understanding on methodology and methods used and potential areas for improvement with co-design methods. Going forward, insights on co-design methodology from community partners will be used to guide meaningful co-design of solutions to HIV care for older adults.

Limitations

Only papers published or translated to English will be included in the study search criteria, thus articles not published in English or translated to English that are relevant to co-design may not be included. Co-design methods have been defined in various terms by different research, including but not limited to, co-production and co-creation, [ 35 ] and thus articles using different terminology regarding co-design, such as co-build, or human-centred design, may be excluded from the search.

Our upcoming scoping review focuses on synthesizing the use of co-design methods for research involving older adults living with HIV. Co-design methods empower older individuals living with HIV to actively participate in shaping their own care experiences, thereby enhancing patient-centered care [ 36 – 38 ]. The objectives, methods, and data extraction process are outlined to systematically explore the extent, range, and nature of geriatric HIV research that has employed methods with co-design approaches, and will identify who may be excluded from these studies, keeping in mind PROGRESS-Plus characteristics [ 20 ]. The importance of consultation with key stakeholders, including community partners and individuals with lived experience, is emphasized to ensure that the insights gained from the review are translated into meaningful co-design of virtual solutions for HIV care for older adults. This review will result in a comprehensive synthesis of the existing literature, offering researchers and healthcare professionals a deeper understanding of how co-design methods have been used to address the unique needs of older adults living with HIV. By identifying gaps in current research and highlighting successful approaches to co-design with older adults with HIV, this review can inform the development of evidence-based strategies for improving the quality of care and support for this older adult population. Successful approaches will emphasize principles from the SPOR Patient Engagement consultation [ 21 ] leading to meaningful engagement in co-design research. Additionally, the review’s focus on inclusivity and cultural sensitivity underscores its potential to promote more equitable and person-centered healthcare, addressing disparities faced by older adults living with HIV from diverse backgrounds. Ultimately, this scoping review serves as a crucial step in shaping the future of geriatric HIV care, where approaches to co-design can play a pivotal role in enhancing healthcare delivery, to improve overall well-being.

Supporting information

S1 checklist. prisma-p (preferred reporting items for systematic review and meta-analysis protocols) 2015 checklist: recommended items to address in a systematic review protocol*..

https://doi.org/10.1371/journal.pone.0303580.s001

1. Global HIV. AIDS statistics—Fact sheet [Internet [Internet]. Available from: https://www.unaids.org/en/resources/fact-sheet .
2. U.N.A.I.D.S. HIV estimates with uncertainty bounds 1990-Present [Internet [Internet]. Available from: https://www.unaids.org/en/resources/fact-sheet .
View Article
PubMed/NCBI
Google Scholar
21. Strategy for Patient-Oriented Research—Patient Engagement Framework—CIHR [Internet]. [cited 2023 Dec 3]. Available from: https://cihr-irsc.gc.ca/e/48413.html .
33. Peters M, Godfrey C, McInerney P, Soares C, Khalil H, Parker D. The Joanna Briggs Institute reviewers’ manual 2015: methodology for JBI scoping reviews.

Open access
Published: 31 May 2024

Modifications of the readiness assessment for pragmatic trials tool for appropriate use with Indigenous populations

Joanna Hikaka 1 ,
Ellen M. McCreedy 2 , 3 ,
Eric Jutkowitz 3 , 4 ,
Ellen P. McCarthy 5 , 6 &
Rosa R. Baier 2 , 3

BMC Medical Research Methodology volume 24 , Article number: 121 ( 2024 ) Cite this article

Metrics details

Inequities in health access and outcomes exist between Indigenous and non-Indigenous populations. Embedded pragmatic randomized, controlled trials (ePCTs) can test the real-world effectiveness of health care interventions. Assessing readiness for ePCT, with tools such as the Readiness Assessment for Pragmatic Trials (RAPT) model, is an important component. Although equity must be explicitly incorporated in the design, testing, and widespread implementation of any health care intervention to achieve equity, RAPT does not explicitly consider equity. This study aimed to identify adaptions necessary for the application of the ‘Readiness Assessment for Pragmatic Trials’ (RAPT) tool in embedded pragmatic randomized, controlled trials (ePCTs) with Indigenous communities.

We surveyed and interviewed participants (researchers with experience in research involving Indigenous communities) over three phases (July-December 2022) in this mixed-methods study to explore the appropriateness and recommended adaptions of current RAPT domains and to identify new domains that would be appropriate to include. We thematically analyzed responses and used an iterative process to modify RAPT.

The 21 participants identified that RAPT needed to be modified to strengthen readiness assessment in Indigenous research. In addition, five new domains were proposed to support Indigenous communities’ power within the research processes: Indigenous Data Sovereignty; Acceptability – Indigenous Communities; Risk of Research; Research Team Experience; Established Partnership). We propose a modified tool, RAPT-Indigenous (RAPT-I) for use in research with Indigenous communities to increase the robustness and cultural appropriateness of readiness assessment for ePCT. In addition to producing a tool for use, it outlines a methodological approach to adopting research tools for use in and with Indigenous communities by drawing on the experience of researchers who are part of, and/or working with, Indigenous communities to undertake interventional research, as well as those with expertise in health equity, implementation science, and public health.

RAPT-I has the potential to provide a useful framework for readiness assessment prior to ePCT in Indigenous communities. RAPT-I also has potential use by bodies charged with critically reviewing proposed pragmatic research including funding and ethics review boards.

Peer Review reports

The World Health Organization defines health equity as ‘the absence of unfair, avoidable or remediable differences among groups of people’ and states ‘health equity is achieved when everyone can attain their full potential for health and wellbeing’ [ 1 ]. Healthcare access and outcomes differ between Indigenous and non-Indigenous populations across the globe, are unfair and unjust, and are therefore defined as health inequities [ 2 ]. These inequities are mediated by colonization and structural racism, which reduce Indigenous peoples’ access to the wider determinants of health, such as education, employment, and healthcare access, further affecting the barriers and enablers of high-quality health care [ 3 ]. To achieve Indigenous health equity [ 4 (p2)] equity must be explicitly incorporated in the design, testing, and widespread implementation of any intervention [ 5 , 6 , 7 , 8 , 9 ]. In practice, this requires researchers to work together with Indigenous communities to understand local contexts and support the achievement of equity by involving Indigenous people as leaders in research, understanding Indigenous priorities, aspirations, and appropriate measures of success [ 5 , 6 , 10 ]. A recent publication of an equity-focused implementation framework provides practical guidance on how to incorporate equity [ 11 ]. The framework is founded on Indigenous rights as set out in New Zealand’s (NZ’s) founding legislative document and includes steps such as defining resources required for equitable implementation [ 11 ].

Centuries of colonial research and inquiry involving subjugation of Indigenous peoples by powerful ‘others’ provides a lineage to contemporary research practices which further exclude and marginalize Indigenous populations [ 7 ]. This exclusion and marginalization is seen in health intervention research. Indigenous populations may be ‘unseen’ through non-reporting of participants’ ethnicity, or under-represented through low Indigenous recruitment [ 12 ]. The design of the trial, outcome measures, or the intervention itself, may be culturally inappropriate or not reflect Indigenous priorities [ 10 , 13 ]. Findings may also be inappropriately framed to focus on individual or cultural deficits rather than service or systematic factors contributing to differences in outcomes between Indigenous and non-Indigenous populations [ 14 , 15 ]. As a result, interventional research that demonstrates benefit in predominately White populations may not be effective, feasible, or acceptable in other cultural settings and research tools developed in non-Indigenous settings have the potential to widen inequities [ 16 ] and lead to unethical research practices in Indigenous populations [ 17 ].

Explicitly designing for equitable health access and outcomes at the outset facilitates pro-equity research. Indigenous pro-equity research may be supported using embedded pragmatic randomized, controlled trials (ePCTs). ePCTs are effectiveness trials that reflect real-world considerations [ 18 ], including ensuring research is appropriate to the targeted communities and settings [ 7 , 19 ]. Further preparatory work is likely required to prepare interventions shown to be effective in predominately non-Indigenous populations for ePCTs in Indigenous populations. Previous Indigenous health intervention research undertaken in Australia, Canada, the United States (US) and NZ has identified processes for investigating how to co-design, implement and evaluate interventions in Indigenous settings [ 20 ], adapting interventions prior to ePCT [ 21 ], how to ensure low resource environments are ready to implement an intervention within a ePCT [ 22 ], as well as targeting specific research processes, such as recruitment [ 23 ].

An intervention must be sufficiently ‘ready’ for an ePCT to ensure it will be feasible to conduct and possible to draw appropriate conclusions from the findings [ 24 ]. The Readiness Assessment for Pragmatic Trials (RAPT) model is an implementation science tool to help researchers qualitatively assess an intervention’s ‘readiness’ (low to high) in the context the intervention’s current state and likelihood of intervention adoption if proven effective in ePCT [ 25 ]. There are nine domains with accompanying questions and scoring criteria: [ 25 ]

Implementation protocol

Is there an implementation protocol that is sufficiently detailed to enable replication?

What is the extent of evidence to support intervention efficacy?

Is the safety of the intervention known?

Feasibility

To what extent can the intervention be implemented within the current environment?

Measurement

To what extent can the intervention effectiveness be measured, ideally using pragmatic outcome measures?

Is the intervention likely to be economically viable?

Acceptability

How likely is it that providers will adopt the intervention?

To what extent is the intervention in alignment with stakeholders’ priorities?

How likely is it that the results for the ePCT will inform clinical practice and/or policy?

RAPT’s readiness domains were defined based on discussion amongst experts at a US National Institute on Aging workshop. However, the resulting model does not explicitly include health equity [ 26 ] and has not been applied to pro-equity Indigenous health intervention research. If adapted to include Indigenous equity considerations, RAPT may inform such efforts. This study aimed to identify adaptions necessary for RAPT’s application to ePCTs with Indigenous communities.

Study design

This mixed-methods study used an online questionnaire and semi-structured interviews. This study was approved by the Auckland Health Research Ethics Committee (AH24242).

This research was led by JH, an Indigenous health services researcher from NZ with experience in Indigenous research methodology and qualitative research, including inductive thematic analysis in Indigenous research underpinned by Indigenous theory. She was working at a university in the US at the time this research was undertaken and worked in collaboration with the rest of the research team who are the lead authors of the RAPT model. Our research team had expertise in qualitative research, co-design and co-creation, public health, health equity, survey methodology, quality improvement, and clinical care in older adult settings. The researchers recognise the right of Indigenous peoples, and the right of people living with dementia, to experience equitable health outcomes.

Recruitment and consent

Eligibility.

Participants were eligible if they were 18 years or older and had been involved as a researcher (self-identified, no formal qualifications required) in research relating to non-pharmacological dementia care interventions in Indigenous communities in NZ (Māori) or the United States (US; American Indian, Native Alaskan, and Kānaka Maoli/Native Hawaiian peoples). We focused on dementia interventions because RAPT, although since applied more broadly [ 27 ], was initially developed to assess dementia interventions [ 25 ] and because this work was partially conducted in partnership with the US National Institute on Aging (NIA) IMPACT Collaboratory, which focuses on dementia interventions. NZ and the US were the countries of interest as the lead author is an Indigenous researcher from NZ and was a visiting scholar, collaborating with the US authors of RAPT.

Recruitment

We first conducted a literature search to identify peer-reviewed publications relating to non-pharmacological dementia interventions (any study design) that included Indigenous populations in the US or NZ and were published from 2011 to 2022. We then emailed invitations to all identified authors for whom we could obtain email addresses ( n = 77). We also emailed invitations to directors of three Indigenous ageing research centers and the International Indigenous Dementia Research Network. We used snowball techniques to identify additional potential study participants [ 28 ]. Participants provided informed consent using an online form immediately prior to completing the online questionnaire.

Questionnaire development and data collection

We surveyed participants in July and August 2022. We provided brief introductory material regarding RAPT. We then asked participants to complete the questionnaire ( Supplementary material ). We collected all data using Qualtrics® (Seattle, Washington US).

Demographics and research experience

The questionnaire captured respondents’ demographics, including self-identified ethnicity and research experience.

RAPT domain questionnaire

We asked participants first to reflect on their research experiences, then to rate each RAPT domain’s appropriateness for interventional research with Indigenous communities using a 4-point Likert scale (inappropriate, slightly inappropriate, slightly appropriate, appropriate). We also asked participants to indicate whether ‘to adequately incorporate health equity’ a domain needed any modifications or should be removed. If they advised modifications, we asked for specific suggestions.

Semi-structured interviews and consensus building

After modifying the existing RAPT domains and adding new domains based on participants’ questionnaire responses, we drafted a modified RAPT, termed the RAPT-Indigenous (RAPT-I). We conducted a semi-structured in-depth interview with respondents to the online questionnaire component (‘respondents’) who assented to participate in follow-up interviews (November-December 2022). Questionnaire respondents were invited to participate rather than new participants to continue development and refinement of domains, similar to the approach taken in a Delphi consensus approach [ 29 ] and to methods used in other similar implementation science research [ 22 ]. The lead author (JH) conducted all interviews using Zoom™ (San Jose, California US) and transcribed the interviews. We provided participants (interviewees) with the draft RAPT-I via email at the time of scheduling the interview, encouraging them to review draft RAPT-I ahead of the interview. During interviews, we explored interviewees perspectives about RAPT-I; any guidance that should accompany the tool; whether ePCTs in Indigenous populations should proceed with low readiness in various domains; and the modified tool’s utility with marginalized populations other than Indigenous communities. Interviewees could request a recording of their interview within two weeks of the interview. An iterative process was used to make further modifications to the draft RAPT-I based on interview responses.The lead researcher sent a second RAPT-I draft to all interviewees and invited them to review and suggest additional modifications prior to RAPT-I’s finalisation. Interviewees provided further feedback either in written form or via a video conference where notes were taken by the lead researcher.

Data analysis

We used Microsoft Excel® (Seattle, Washington US) to characterize participants using descriptive statistics. We calculated the percentage of participants who selected each Likert response when asked about each domain’s appropriateness and need for modification. The lead researcher used the current domains as a framework to group qualitative feedback from the questionnaire and interviews that related to each of the existing domains [ 30 ] and used general inductive analysis to generate new domains from free-text questionnaire responses and interview transcripts to develop new domains (Fig. 1 ). This preliminary analysis was presented to all other authors for discussion and review, with raw data being supplied as required during discussions. A general inductive approach was chosen as this method aligns with our intent to condense and summarize extensive and varied raw data and to develop a model [ 31 ], in this case a modification of RAPT. We included quotes from respondents (‘R’) in the results. We did not undertake any subgroup analysis. For each of the stages that involved iterative changes to draft versions of RAPT-I, the lead researcher made initial changes which were then discussed with all other authors for consensus building and finalization of draft versions. The lead author undertook the final iterative review process which produced a third draft that was finalized, through consultation and discussion with the full research team, for presentation in this paper.

Participant flow through study

Sample size

We targeted 30 participants to reach saturation of responses to qualitative questionnaire questions. We aimed for approximately 15 participants from each country and at least 10 who self-identified as Indigenous.

We emailed questionnaire invitations to 77 people and 21 (27·3%) responded. Research experience ranged from 5 to 40 years (median 20 years); experience focused on older adult/dementia research, 3–40 years (median: 10 years); and with Indigenous research 4–18 years (median: 6). Two-thirds of participants were from NZ ( n = 14, 66·7%). About half identified as Indigenous ( n = 10, 47·6%) or White ( n = 9, 42·9%); the remainder, non-Indigenous ethnic/racial minorities ( n = 2, 9·5%).

Seven (33·3%) questionnaire respondents participated in follow-up interviews, which lasted 26–30 min (median: 28 min). Research experience ranged from 5 to 28 years (median: 14 years). Four interviewees (57·1%) were from NZ; all but one ( n = 6, 85·7%) identified as Indigenous; one an ethnic minority; and three were female (42·9%). Following the interviews, three interviewees (42·9%) reviewed the draft RAPT-I. Saturation of ideas in response to qualitative survey questions was achieved. Saturation of interviewee responses was not sought although saturation was largely achieved after the fifth interview. Interviewees suggested further changes to the first draft RAPT-I which focused on the clarification of domain and scoring wording to convey intended meaning, highlighting the importance of Indigenous partnership, and the value of accompanying guidance to support the use of RAPT-I.

All nine domains were assessed as being appropriate or slightly appropriate by most participants (Table 1 ); however, most participants (90·5%) indicated that some modifications were needed to increase appropriateness for use with Indigenous populations. A greater proportion of respondents would use a modified version of RAPT ( n = 15, 71·4%) vs. the original ( n = 8, 38·1%).

General summary of questionnaire responses to existing domains

Although respondents felt many domains were general enough to be appropriate, most recommended including explicit guidance regarding the intent to achieve Indigenous health equity and to minimise potential risks associated with the intervention or research process. Many felt such guidance would promote culturally-safe interventions and research practices, help researchers to identify areas to strengthen before an ePCT, and even provide a framework for critical review by funders and ethics boards.

The goal of [using a tool such as RAPT] is that health equity becomes part and parcel of how we do high quality research. (R10, US, non-Indigenous ethnic minority)

At the same time, they expressed any guidance provided needed to support meaningful assessment rather than performative assessment that did not change approaches to research.

The question is, will it become another tick box exercise? (R3, NZ, Indigenous)

All respondents felt that it was appropriate to have an implementation protocol that considered equity through all aspects of implementation.

The parameters and criterion of health equity should be demonstrated. (R15, NZ, Indigenous)

However, many noted there were likely to be aspects of pragmatic research with Indigenous communities that could not be protocolised. Others noted that even if a protocol enabled replicability, replicating an intervention tested in one Indigenous community in another Indigenous community may be inappropriate.

There is a need to recognize the flexibility necessary for Indigenous research, I believe there is an option between partially documented and fully documented, for flexible documentation that is mostly (or partially) documented, that is revised during the research journey. (R4, NZ, Indigenous)

Many respondents deemed Evidence essential; however, most recommended requiring evidence with the targeted Indigenous community specifically. Several questioned the need for efficacy evidence from randomized-controlled trials, which may not be available for Indigenous communities.

The ideal is to have prior evidence, however there may not be prior evidence for Indigenous populations. Sometimes a number of less rigorous methods is good enough evidence for the intervention to be tested. (R4, NZ, Indigenous)

Some respondents also felt that it was important to modify Evidence to include evidence of access- and equity-related outcomes. One noted that one purpose for conducting research with Indigenous communities may be that interventions efficacious in other populations either do not achieve equity or worsen inequities in Indigenous populations.

In many instances of health equity research, there may not be any existing efficacy studies. I mean, a large part of the drivers of inequity are that interventions are NOT fit for purpose and it is precisely because of this that new interventions are being proposed and researched!” (R3, NZ, Indigenous).

Most participants agreed that Feasibility was important and that understanding feasibility specifically in the targeted Indigenous community was crucial, as it may differ across populations. For example, some participants shared that it may not be possible to adequately implement a new intervention with existing resources in already under-resourced communities or populations.

[Feasibility] tends to be neglected as work is moved into communities. (R9, US, non-Indigenous)

Several participants felt that additional support (e.g., human and financial resourcing) may be required to investigate a new intervention and that such needs should not be reason to withhold research opportunities from communities already experiencing inequitable resourcing.

Many participants felt that pragmatic outcome data collection could be beneficial, but potentially unachievable for Indigenous interventions, for example if structural inequities impacted the availability and use of electronic health records systems.

Rural indigenous communities do not have outcomes “routinely captured” due to lack of health care / poor health care services. (R7, US, Indigenous)

One participant questioned the ability of electronic systems to accurately capture measures, noting that ethnic minority populations are routinely misclassified and undercounted in NZ national data sets. Another suggested that measurement readiness could be expanded to include two items: one focused on exploring electronic data collection; the other, on using easily collected and entered hand-written data collection.

Most participants deemed the economic viability important for sustainability and evidence-based resource allocation. However, they felt that expertise in cost-benefit analysis in the Indigenous communities of interest would be important to appropriately account for economic costs or benefits particular to the community of interest and to consider the wider influences and impacts of inequitable resourcing in health service/system infrastructure and in the social determinants of health.

To achieve equity the costs are often higher in these populations to achieve the same level of intervention/outcome. (R6, NZ, non-Indigenous)

Equally, some participants described the need to take a broad approach to assessing benefits through an Indigenous lens, e.g., improvement in spiritual wellbeing or social connectedness.

Importantly, some participants related Cost to Evidence, noting lack of evidence in Indigenous communities would affect cost-benefit analysis calculations or considerations. Several felt that lack of cost data or low readiness should not prevent investigation of potentially beneficial interventions in “understudied, underserved, and minoritized groups”.

Some participants were unclear about the distinctions among Acceptability, Alignment, and Impact and suggested adding wording to clarify differences. As currently framed, Acceptability and Alignment domains focus on the existing relevance to internal and external stakeholders, whereas Impact domain focuses on the potential value of future ePCT findings [ 12 ]. Participants felt Impact aligned with Indigenous values by appropriately focusing on the potential for translation into practice, but that the domain needed to focus on benefit for Indigenous communities and inform or relate to equitable clinical care and policy.

Impact should be Indigenous focused. A focus group would be better able to define how this would look when considering what qualifies as meaningful “impact”.” (R7, US, Indigenous).

Further respondent quotes are shown in Table 2 .

New domains

General thematic analysis of questionnaire and interview responses led to the development of five new domains: Indigenous Data Sovereignty; Acceptability – Indigenous communities; Risk of research; Research team experience; Established partnership.

Acceptability – Indigenous communities

Several participants recognized the importance of Acceptability to ensure the intervention reflects providers’ priorities and is implemented as intended specifically in Indigenous communities. However, they raised the need to engage providers in preparatory work relating to health equity to ensure or increase acceptance and therefore the potential for intervention success, especially if intervention elements or implementation approaches differed from practices used by staff from dominant cultures in implementation sites.

Health equity research findings can be confronting to many in the mainstream who don’t believe there is a problem. (R3, NZ, Indigenous)

Most participants suggested broadening Acceptability to include the community in which the intervention will be examined, as without this acceptance the intervention is also likely to fail.

[We need to think about] how we make research attractive to Indigenous communities” (R7, US, Indigenous).

Participants felt Alignment was critical to Indigenous intervention development and implementation, like Acceptance. Many felt that the requirement for Indigenous stakeholders’ values and priorities needed to be explicitly stated. Participants mentioned the potential for stakeholders to hold competing priorities and some stated that Indigenous priorities need to be privileged above other stakeholders’. Although several participants recognised the need for some alignment between all stakeholders, including Indigenous stakeholders, they questioned what course of action to take when health systems or providers disagreed or did not value equity as a priority.

Important question, but how are community needs balanced with stakeholder needs? (R19, NZ, Indigenous)

Risk of research

Most respondents felt that understanding potential risks in Indigenous communities was essential for assessing readiness. In fact, some felt that researchers should assess risk first and not assess other domains or proceed with an ePCT if risk was unknown or there was potential for harm. Importantly, they described considering risk from the perspectives of both participants and the wider community, and not just risks associated with the intervention, but with the research process as a whole.

What is deemed as a risk? What might not be a risk for non-Indigenous peoples might be a risk for Indigenous peoples. Is the intervention culturally appropriate? Could possibly consider the benefits for Indigenous peoples too. Thinking about collective risk of intervention not just individual risk . (R20, NZ, Indigenous)

Research team experience

Several respondents felt that lack of evidence in Indigenous populations could be overcome by adapting interventions proven efficacious in other populations in partnership with Indigenous communities, without the need for further testing ahead of ePCTs. Some felt Evidence should consider the Indigenous practices in place and valued for decades or centuries, and not be limited to Western approaches to evidence. To undertake this however, it was identified that at least some members of the research team should have experience working with Indigenous communities to support these practices and that Indigenous researchers and communities should be part of the research team.

[Indigenous communities] have to be part of the research team from the start, deciding the questions, methods and protocols. (R19, NZ, Indigenous)

Respondents commented that such guidance accompanying RAPT-I would be particularly important for research teams with limited health equity experience; they felt that such researchers often want to do the right thing but lack the expertise to plan for equity.

Established partnership

Respondents discussed the importance of collaboration with Indigenous communities to assess each domain and facilitate culturally-appropriate modifications. They felt that the type and extent of preparatory work required prior to moving forward with an ePCT should be done by researchers and Indigenous communities together and that a modified RAPT could provide a useful framework for such planning and work. For example, some suggested modifying the domain to ensure the protocol be developed in partnership with Indigenous communities, explicitly consider health equity, and be written in culturally-appropriate and accessible language. Many emphasized the importance of engaging the Indigenous community to assess feasibility and recommended providing guidance to help researchers and communities identify all aspects of feasibility that should be assessed. Several participants also suggested identifying the communities’ opportunities and strengths which facilitated feasible implementation, rather than only shortcomings. Others noted that established partnerships would support the inclusion of outcome measures of most importance or relevance to Indigenous communities and that pre-work should ensure that planned outcomes are appropriate.

Importantly, respondents discussed the need to establish partnerships between researchers and Indigenous communities very early in the process.

Partnership discussions should be part of the initial engagement. (R7, US, Indigenous)

Indigenous Data Sovereignty

Interview participants highlighted the importance of Indigenous Data Sovereignty, with one respondent saying it was so important that it should be prioritized as the first domain. Respondents stated that Indigenous Data Sovereignty needed to be considered and discussed right from the outset and that these discussions were likely to be fundamental to partnership establishment and intervention implementation. Respondents felt that decisions that upheld Indigenous Data Sovereignty needed to be ongoing throughout the research process and therefore, that a shared understanding of the need for ongoing discussion was needed prior to e-PCT. Respondents also advised the framing of Indigenous Data Sovereignty guidance and scoring was important to demonstrate that research processes needed to ensure Indigenous rights to data sovereignty could be exercised.

Indigenous communities will always have sovereignty over their data, but they may not have the infrastructure in place to exercise the sovereignty over their data. (R5, US, Indigenous)

Questionnaires and interviews with researchers conducting non-pharmacological dementia care interventions with Indigenous communities in NZ and the US resulted in recommendations to modify RAPT to explicitly incorporate considerations for pro-equity research in Indigenous communities. Recommendations for RAPT-I included new guidance for existing RAPT domains and the addition of new domains focused on Indigenous rights to culturally-safe research practices and to govern and control research processes. Participants also discussed how RAPT-I could guide researchers with limited experience with equity-focused research and emphasized the importance of assessing and modifying interventions in collaboration with Indigenous communities.

Others have previously raised the need for implementation science theory and methods to adequately incorporate health equity [ 32 , 33 , 34 ]. Without doing so, traditional implementation science will likely widen disparities, moving us further away from the goal of health equity [ 33 ]. Similar to our study findings, exploring and addressing power dynamics, working in partnership with the goal of developing sustainable models of care, and examination of wider structural systems that impact on interventions and their impacts have been deemed important [ 32 ]. As in our study, methods that facilitate and provide guidance on how to effectively design for equity when implementing an intervention have been identified as crucial [ 33 , 34 ]. An example of how this is done in practice is provided by the National Institute of Aging IMPACT Collaboratory, which has produced guidance documents on ‘Best Practices for Integrating Health Equity into Embedded Pragmatic Clinical Trials for Dementia Care’ to step researchers through equity considerations at all stages of ePCT from community engagement and study design through implementation and analysis [ 35 ]. This type of tool, along with implementation frameworks addressing equity in Indigenous populations [ 11 ], could be used alongside RAPT-I, providing guidance on next steps if RAPT-I identifies low readiness in one or more of the domains.

Previous Canadian research investigating practices that support cultural safety in controlled trials with Indigenous peoples identified that effective communication and co-design between researchers and Indigenous communities and critical reflection in response to cultural ‘mistakes’ fostered success in research [ 36 ]. Indigenous peoples’ rights to control and power within research appeared to be recognised by participants who sought mechanisms within RAPT-I to protect and uphold these rights in implementation research. This included understanding the participation, and potential risk to communities, as a collective rather than solely as individuals, as well as recognising strengths and opportunities within communities. The CONSIDER statement [ 6 ] was developed to facilitate full and complete reporting of research that involves Indigenous peoples, however it also provides a framework through which to plan research that upholds Indigenous rights. Application of the CONSIDER statement would also be useful for planning for ePCT readiness in Indigenous communities.

Some of the concepts that are incorporated within RAPT-I domains have been previously described in pragmatic controlled trial literature with Indigenous populations. These include developing effective relationships which give power to Indigenous communities [ 36 ], Indigenous community endorsement of ePCT prior to initiation [ 37 ], relationships, assessing community and researcher readiness to commence the ePCT [ 22 ]. Previous work has identified ten principles of practice when undertaking health research with Indigenous Australians, although not specifically related to ePCT [ 38 ]. The adaption of interventions for ePCT with Indigenous communities has also been described, with methods for adaption including community involvement and focussing on strengths within Indigenous communities [ 39 ] and inclusion of culturally relevant values and materials [ 40 , 41 ]. A recent scoping review identified that although participatory research approaches with Indigenous communities are needed for appropriate adaption, this is done with varying authenticity and success, and authors noted that clearer guidance was needed to facilitate improved practices [ 42 ]. Our research further builds on these works and brings together considerations relating to both intervention implementation and research processes in one tool for researchers and communities to access and guide them through an explicit ePCT readiness assessment process.

It is widely acknowledged that pragmatism of a clinical trial should be viewed on a continuum [ 18 ] and participants felt RAPT-I could provide a useful framework for researchers and Indigenous communities to critically and collaboratively evaluate readiness in Indigenous and equity focused contexts. Where there was low or medium readiness in some domains, participants felt that this would not necessarily prevent progression to ePCT, but that researchers and Indigenous communities should have collaborative discussions with decisions made about the preparatory work which could increase readiness. This may include small pilot or feasibility studies to better understand some aspects of the intervention and research processes.

Where research was not feasible due to structural factors such as chronic under resourcing as seen in other studies [ 43 , 44 ], thought should be given to whether these could be corrected in the short-term. For example, those delivering the intervention could be resourced through research funding during the research contract, alongside researchers working with other stakeholders to advocate for and develop stable resourcing for sustainable service models in the long-term. This highlights the potential of researchers as advocates for structural change within health research resourcing. This includes a responsibility to monitor RAPT-I utilization to ensure it is used to strengthen research undertaken in and with Indigenous communities rather than impeding Indigenous progress.

RAPT was designed to help researchers make informed decisions about whether a particular intervention is ready to undergo real-world effectiveness testing and to identify areas that may need to be addressed prior to an ePCT. RAPT-I has the potential to also provide a useful framework for those charged with critically reviewing proposed pragmatic research, including funding and ethics review boards. Further study is warranted to pilot and refine RAPT-I within a broader context including non-dementia focused research and in Indigenous settings outside of NZ and the US. Further investigation to provide RAPT-I assessment exemplars, evaluate language accessibility, assess applicability in these additional settings and to explore how RAPT-I could be the basis for a broader health equity extension which would have applicability in the vast majority of ePCT readiness assessments would be beneficial.

Strengths and limitations

A strength of this study was that the research team, and participants, had collective expertise in Indigenous health research, health equity, intervention science methodology and ePCT study design. Fewer participants than anticipated were required to reach data saturation in the online questionnaire. We only included researchers from the UA and NZ it is likely that this work can be progressed further by including other Indigenous populations and researchers. Participants were recruited from dementia-related studies only and widening the inclusion criteria may have led to more diverse discussion. Findings therefore may not be able to generalized for other study settings. Although participants with experience with research including Indigenous populations was sought, Indigenous health services research and development, or health equity more generally, may not have been their area of expertise.

This study highlights the specific strategies to incorporate Indigenous health equity considerations into RAPT and offers RAPT-I as a proposed modified assessment. New domains have been proposed which advocate for the rights of Indigenous communities to be partners in research and maintain sovereignty over research data. RAPT-I provides a potential mechanism to increase the robustness of readiness assessment for ePCT by researchers and Indigenous communities.

Data availability

Data is not available as use by third parties was not granted in the ethics process.

Abbreviations

Embedded pragmatic randomized, controlled trials

New Zealand

Readiness Assessment for Pragmatic Trials

Readiness Assessment for Pragmatic Trials – Indigenous

United States of America

World Health Organization. Health Equity. Geneva: World Health Organization. 2024. [Cited 2024 Feb 29]. https://www.who.int/health-topics/health-equity

United Nations. State of the World’s Indigenous Peoples - Indigenous Peoples’ Access to Health Services. United Nations. 2018. [Cited 2024 Feb 29]. https://www.un.org/development/desa/indigenouspeoples/wp-content/uploads/sites/19/2018/03/The-State-of-The-Worlds-Indigenous-Peoples-WEB.pdf

Bailey ZD, Feldman JM, Bassett MT. How structural Racism Works — Racist policies as a Root Cause of U.S. Racial Health Inequities. N Engl J Med. 2021;384(8):768–73. https://doi.org/10.1056/NEJMms2025396

Article PubMed Google Scholar

World Health Organization. It’s Time to Build a Fairer, Healthier World for Everyone, Everywhere. World Health Organization. 2021. [Cited 2024 Feb 29]. https://cdn.who.int/media/docs/default-source/world-health-day-2021/health-equity-and-its-determinants.pdf?sfvrsn=6c36f0a5_1&download=true

Oetzel J, Scott N, Hudson M, et al. Implementation framework for chronic disease intervention effectiveness in Māori and other indigenous communities. Glob Health. 2017;13(1):69. https://doi.org/10.1186/s12992-017-0295-8

Article Google Scholar

Huria T, Palmer S, Pitama S, et al. Consolidated criteria for strengthening reporting of health research involving indigenous peoples: the CONSIDER statement. BMC Med Res Methodol. 2019;19(1):173.

Article PubMed PubMed Central Google Scholar

Smith LT. Decolonising methodologies: Research and Indigenous peoples. 2nd ed. Zed Books; 2012.

Pihama L. Kaupapa Māori theory: transforming theory in Aotearoa. He Pukenga Korero. 2010;9(2):5–14.

Google Scholar

Curtis E. Indigenous positioning in health research: the importance of Kaupapa Māori theory-informed practice. Altern Int J Indig Peoples. 2016;12(4):396–410.

Reid P, Paine SJ, Curtis E, et al. Achieving health equity in Aotearoa: strengthening responsiveness to Māori in health research. N Z Med J. 2017;130(1465):96–103.

PubMed Google Scholar

Gustafson P, Lambert M, Bartholomew K, et al. Adapting an equity-focused implementation process framework with a focus on ethnic health inequities in the Aotearoa New Zealand context. Int J Equity Health. 2024;23(1):1–14. https://doi.org/10.1186/s12939-023-02087-y

Umaefulam V, Kleissen T, Barnabe C. The representation of indigenous peoples in chronic disease clinical trials in Australia, Canada, New Zealand, and the United States. Clin Trials Lond Engl. 2022;19(1):22–32. https://doi.org/10.1177/17407745211069153

Roy LM, Neill A, Swampy K, et al. Preference-based measures of health-related quality of life in indigenous people: a systematic review. Qual Life Res. 2024;33(2):317–33. https://doi.org/10.1007/s11136-023-03499-7

Bullen J, Hill-Wall T, Anderson K, et al. From deficit to strength-based Aboriginal Health Research—moving toward flourishing. Int J Environ Res Public Health. 2023;20(7):5395. https://doi.org/10.3390/ijerph20075395

Chittleborough E, Delbridge R, Coveney J, Wilson R, Mackean T, Wilson A. Challenging deficit discourse in Aboriginal and Torres Strait Islander nutrition and dietetics research: a critical discourse analysis. SSM - Qual Res Health. 2023;4:100323. https://doi.org/10.1016/j.ssmqr.2023.100323

Gilmore-Bykovskyi A, Jackson JD, Wilkins CH. The urgency of Justice in Research: Beyond COVID-19. Trends Mol Med. 2021;27(2):97–100. https://doi.org/10.1016/j.molmed.2020.11.004

Article CAS PubMed Google Scholar

Luke J, Verbunt E, Bamblett M, et al. Is all that glitters gold? Assessing the quality and ethics of ‘gold standard’randomised controlled trials conducted in Aboriginal and Torres Strait Islander settings. First Nations Health Wellbeing- Lowitja J. 2023;1:100007. https://www.sciencedirect.com/science/article/pii/S2949840623000074

Loudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ. 2015;350:h2147. https://doi.org/10.1136/bmj.h2147

Hudson M, Milne M, Reynolds P, Russell K, Smith B. Te Ara Tika: guidelines for Māori Research Ethics: a Framework for Researchers and Ethics Committee members. Health Research Council of New Zealand on behalf of the Pūtaiora Writing Group; 2010.

Chelberg GR, Butten K, Mahoney R, eHRCATSIH Group. Culturally safe eHealth interventions with Aboriginal and Torres Strait Islander people: protocol for a best practice Framework. JMIR Res Protoc. 2022;11(6):e34904. https://doi.org/10.2196/34904

Bogic M, Hebert LE, Evanson A, et al. Keep up the messages, sometimes it was a lifesaver: effects of cultural adaptation on a suicide prevention clinical trial in American Indian/Alaska native communities. Behav Res Ther. 2023;166:104333. https://doi.org/10.1016/j.brat.2023.104333

Maar M, Yeates K, Barron M, et al. I-RREACH: an engagement and assessment tool for improving implementation readiness of researchers, organizations and communities in complex interventions. Implement Sci. 2015;10(1):64. https://doi.org/10.1186/s13012-015-0257-6

Parker K, Colhoun S, Bartholomew K, et al. Invitation methods for Indigenous New Zealand Māori in lung cancer screening: protocol for a pragmatic cluster randomized controlled trial. PLoS ONE. 2023;18(8):e0281420. https://doi.org/10.1371/journal.pone.0281420

Article CAS PubMed PubMed Central Google Scholar

Craig P, Dieppe P, Macintyre S, et al. Developing and evaluating complex interventions: the new Medical Research Council guidance. Br Med J. 2008;337:a1655.

Baier RR, Jutkowitz E, Mitchell SL, McCreedy E, Mor V. Readiness assessment for pragmatic trials (RAPT): a model to assess the readiness of an intervention for testing in a pragmatic trial. BMC Med Res Methodol. 2019;19(1):1–8. https://doi.org/10.1186/s12874-019-0794-9

Aranda MP, Baier R, Hinton L, et al. Preparing for pragmatic trials in dementia care: Health equity considerations for nonpharmacological interventions. J Am Geriatr Soc. 2023;71(12):3874–85. https://doi.org/10.1111/jgs.18568

Hastings SN, Stechuchak KM, Choate A, et al. Implementation of a stepped wedge cluster randomized trial to evaluate a hospital mobility program. Trials. 2020;21(1):863. https://doi.org/10.1186/s13063-020-04764-7

Valentine G. Tell me about… using interviews as a research methodology. Methods in human geography: a guide for students doing a Research Project. Harlow: Pearson; 2005. pp. 110–27.

Fink A, Kosecoff J, Chassin M, Brook RH. Consensus methods: characteristics and guidelines for use. Am J Public Health. 1984;74(9):979–83. https://doi.org/10.2105/AJPH.74.9.979

Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13(1):117. https://doi.org/10.1186/1471-2288-13-117

Thomas D. A general inductive approach for analysing qualitative evaluation data. Am J Eval. 2006;27(2):237–46.

Snell-Rood C, Jaramillo ET, Hamilton AB, Raskin SE, Nicosia FM, Willging C. Advancing health equity through a theoretically critical implementation science. Transl Behav Med. 2021;11(8):1617–25. https://doi.org/10.1093/tbm/ibab008

McNulty M, Smith JD, Villamar J et al. Implementation Research Methodologies for Achieving Scientific Equity and Health Equity. Ethn Dis 29(Suppl 1):83–92. https://doi.org/10.18865/ed.29.S1.83

Brownson RC, Kumanyika SK, Kreuter MW, Haire-Joshu D. Implementation science should give higher priority to health equity. Implement Sci. 2021;16(1):28. https://doi.org/10.1186/s13012-021-01097-0

NIA IMPACT Collaboratory. Best Practices for Integrating Health Equity into Embedded Pragmatic Clinical Trials for Dementia Care. NIA IMPACT Collaboratory Transforming Dementia Care. Published 2022. https://impactcollaboratory.org/new-guidance-document-offers-best-practices-for-incorporating-health-equity-concerns-into-embedded-pragmatic-clinical-trials-for-dementia/

Maar MA, Beaudin V, Yeates K, et al. Wise practices for Cultural Safety in Electronic Health Research and clinical trials with Indigenous people: secondary analysis of a Randomized Clinical Trial. J Med Internet Res. 2019;21(11):e14203. https://doi.org/10.2196/14203

Yeates K, Campbell N, Maar MA, et al. The effectiveness of text messaging for detection and management of hypertension in Indigenous people in Canada: protocol for a Randomized Controlled Trial. JMIR Res Protoc. 2017;6(12):e7139. https://doi.org/10.2196/resprot.7139

Jamieson LM, Paradies YC, Eades S, et al. Ten principles relevant to health research among indigenous Australian populations. Med J Aust. 2012;197(1):16–8. https://doi.org/10.5694/mja11.11642

Jones L, Jacklin K, O’Connell ME. Development and Use of Health-Related technologies in Indigenous communities: critical review. J Med Internet Res. 2017;19(7):e7520. https://doi.org/10.2196/jmir.7520

Kennedy M, Kumar R, Ryan NM, Bennett J, La Hera Fuentes G, Gould GS. Codeveloping a multibehavioural mobile phone app to enhance social and emotional well-being and reduce health risks among Aboriginal and Torres Strait Islander women during preconception and pregnancy: a three-phased mixed-methods study. BMJ Open. 2021;11(11):e052545. https://doi.org/10.1136/bmjopen-2021-052545

Ivers R, Coombes J, Sherrington C, et al. Healthy ageing among older Aboriginal people: the Ironbark study protocol for a cluster randomised controlled trial. Inj Prev. 2020;26(6):581–7. https://doi.org/10.1136/injuryprev-2020-043915

Wali S, Superina S, Mashford-Pringle A, Ross H, Cafazzo JA. What do you mean by engagement? - evaluating the use of community engagement in the design and implementation of chronic disease-based interventions for indigenous populations - scoping review. Int J Equity Health. 2021;20(1):8. https://doi.org/10.1186/s12939-020-01346-6

Liu H, Laba TL, Massi L, et al. Facilitators and barriers to implementation of a pragmatic clinical trial in Aboriginal health services. Med J Aust. 2015;203(1):24–7. https://doi.org/10.5694/mja14.00581

Nguyen HQ, McMullen C, Haupt EC, et al. Findings and lessons learnt from early termination of a pragmatic comparative effectiveness trial of video consultations in home-based palliative care. BMJ Support Palliat Care. 2022;12(e3):e432–40. https://doi.org/10.1136/bmjspcare-2020-002553

Rainie SC, Kukutai T, Walter M, Figueroa-Rodriguez OL, Walker J, Axelsson P. Issues in open data - Indigenous Data Sovereignty. In: The State of Open Data: Histories and Horizons. Cape Town and Ottawa: African Minds and International Development Centre; 2019. Accessed September 5, 2023. https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

Download references

Acknowledgements

The authors thank the participants who generously contributed their time and expertise including the following who chose to be acknowledged by name: Lauren W. Yowelunh McLester-Davis.

This research was supported in part by the Health Research Council of New Zealand (HRC: 21/1062). Drs. Baier, Jutkowitz, McCreedy and McCarthy were supported by the National Institute on Aging (NIA) of the National Institutes of Health under Award Number U54AG063546, which funds NIA Imbedded Pragmatic Alzheimer’s Disease and AD-Related Dementias Clinical Trials Collaboratory (NIA IMPACT Collaboratory). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role or influence over the study design, the collection, analysis and interpretation, or reporting of the data.

Author information

Authors and affiliations.

Department of General Practice and Primary Health Care, University of Auckland, Auckland, New Zealand

Joanna Hikaka

Center for Long-Term Care Quality & Innovation, Brown University School of Public Health, Providence, RI, USA

Ellen M. McCreedy & Rosa R. Baier

Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA

Ellen M. McCreedy, Eric Jutkowitz & Rosa R. Baier

Providence Veterans Affairs Medical Center, Providence VA, RI, USA

Eric Jutkowitz

Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA

Ellen P. McCarthy

Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

You can also search for this author in PubMed Google Scholar

Contributions

JH and RB conceptualized the study and wrote the main manuscript. All authors (JH, EMM, EJ, EPM, RB) contributed to study design, contributed to data analysis and reviewed the manuscript.

Corresponding author

Correspondence to Joanna Hikaka .

Ethics declarations

Ethical approval and consent to participate.

This study was approved by the Auckland Health Research Ethics Committee (AH24242). All participants provided written, informed consent prior to participation. The research was performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Hikaka, J., McCreedy, E.M., Jutkowitz, E. et al. Modifications of the readiness assessment for pragmatic trials tool for appropriate use with Indigenous populations. BMC Med Res Methodol 24 , 121 (2024). https://doi.org/10.1186/s12874-024-02244-z

Download citation

Received : 24 October 2023

Accepted : 14 May 2024

Published : 31 May 2024

DOI : https://doi.org/10.1186/s12874-024-02244-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Implementation science
Health services research
Indigenous health
Data sovereignty

BMC Medical Research Methodology

ISSN: 1471-2288

General enquiries: [email protected]

DeepFD: a deep learning approach to fast generate force-directed layout for large graphs

Regular Paper
Published: 31 May 2024

Cite this article

Shuhang Zhang 1 nAff3 ,
Ruihong Xu 1 ,
Qing Zhang 1 ,
Yining Quan 1 &

Deep learning techniques have been applied to the graph drawing of node-link diagrams to help figure out user preference of layout in recent research. However, when revisiting existing studies, only stress model and dimensional reduction methods are utilized in the unsupervised learning of graph drawing tasks since their gradient descent conditions can be easily constructed, and few works have explored their scalability on large graphs. In this paper, we propose a framework that can adapt most of the graph layout methods to a form of loss function and develop an implementation DeepFD, which takes the force-directed algorithm as the prototype to design the loss function. Our model is built with the graph-LSTM as encoder and multilayer perceptron as decoder and trained with dataset split from huge graphs with millions of nodes by Louvain. We design a set of qualitative and quantitative experiments to evaluate our method and compare with classical layout techniques such as F-R and K-K algorithms, while deep-learning based models with various architecture or loss function are adopted to perform ablation experiments. The results indicate that our developed approach can generate a high-quality layout of large graph within a low time cost, and the model we propose shows strong robustness and high efficiency.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

www.graphdrawing.org/data.html .

socialnetworks.mpi-sws.org/data-imc2007.html .

snap.stanford.edu/data/soc-Pokec.html .

Arleo A, Didimo W, Liotta G, Montecchiani F (2019) A distributed multilevel force-directed algorithm. IEEE Trans Parallel Distrib Syst 30(4):754–765. https://doi.org/10.1109/TPDS.2018.2869805

Article Google Scholar

Barnes J, Hut P (1986) A hierarchical o (n log n) force-calculation algorithm. Nature 324(6096):446–449

Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396. https://doi.org/10.1162/089976603321780317

Brandes U, Pich C (2007) Eigensolver methods for progressive multidimensional scaling of large data. In: Kaufmann M, Wagner D (eds) Graph drawing. Springer, Berlin, pp 42–53

Chapter Google Scholar

Cohen JD (1997) Drawing graphs to convey proximity: an incremental arrangement method. ACM Trans Comput Hum Interact 4(3):197–229. https://doi.org/10.1145/264645.264657

Dunne C, Shneiderman B (2013) Motif simplification: Improving network visualization readability with fan, connector, and clique glyphs. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’13. Association for Computing Machinery, New York, pp 3247–3256. https://doi.org/10.1145/2470654.2466444

Eades P (1984) A heuristic for graph drawing. In: Proceedings of the 13th Manitoba conference on numerical mathematics and computing, vol. 24. Utilitas Mathematica, University of Manitoba, Winnipeg

Egorov D, Bezgodov A (2015) Improved force-directed method of graph layout generation with adaptive step length. Procedia Comput Sci 66:689–696

Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Softw Pract Exp 21(11):1129–1164. https://doi.org/10.1002/spe.4380211102

Gansner ER, Koren Y, North S (2005) Graph drawing by stress majorization. In: Pach J (ed) Graph drawing. Springer, Berlin, pp 239–250

Gibson H, Faith J, Vickers P (2013) A survey of two-dimensional graph layout techniques for information visualisation. Inf Vis 12(3–4):324–357. https://doi.org/10.1177/1473871612455749

Giovannangeli L, Lalanne F, Auber D, Giot R, Bourqui R (2021) Deep neural network for drawing networks, ${(DNN)^{\mathit{2}\,}}$ . Graph drawing and network visualization. Springer International Publishing, Cham, pp 375–390

Giovannangeli L, Lalanne F, Auber D, Giot R, Bourqui R (2022) Toward efficient deep learning for graph drawing (DL4GD). IEEE Trans Vis Comput Graph. https://doi.org/10.1109/TVCG.2022.3222186

Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16. Association for Computing Machinery, New York, pp 855–864. https://doi.org/10.1145/2939672.2939754

Haleem H, Wang Y, Puri A, Wadhwa S, Qu H (2019) Evaluating the readability of force directed graph layouts: a deep learning approach. IEEE Comput Graph Appl 39(4):40–53. https://doi.org/10.1109/MCG.2018.2881501

Heimann M, Koutra D (2017) On generalizing neural node embedding methods to multi-network problems. In: KDD MLG workshop

Kamada T, Kawai S et al (1989) An algorithm for drawing general undirected graphs. Inf Process Lett 31(1):7–15

Article MathSciNet Google Scholar

Kwon O-H, Ma K-L (2020) A deep generative model for graph layout. IEEE Trans Visual Comput Graph 26(1):665–675. https://doi.org/10.1109/TVCG.2019.2934396

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

Liang X, Shen X, Feng J, Lin L, Yan S (2016) Semantic object parsing with graph lstm. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision—ECCV 2016. Springer International Publishing, Cham, pp 125–143

Meidiana A, Hong S-H, Torkel M, Cai S, Eades P (2020) Sublinear time force computation for big complex network visualization. Comput Graph Forum 39(3):579–591. https://doi.org/10.1111/cgf.14003

Noack A (2004) An energy model for visual graph clustering. In: Liotta G (ed) Graph drawing. Springer, Berlin, pp 425–436

Ohsaka N, Sonobe T, Fujita S, Kawarabayashi K-i (2017) Coarsening massive influence networks for scalable diffusion analysis. In: Proceedings of the 2017 ACM international conference on management of data, SIGMOD ’17. Association for Computing Machinery, New York, pp 635–650. https://doi.org/10.1145/3035918.3064045

Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14. Association for Computing Machinery, New York, pp 701–710. https://doi.org/10.1145/2623330.2623732

Tamassia R (2013) Handbook of graph drawing and visualization. CRC Press, Boca Raton

Book Google Scholar

Taud H, Mas J (2018) Multilayer perceptron (MLP). Springer International Publishing, Cham, pp 451–455

Google Scholar

Tiezzi M, Ciravegna G, Gori M (2022) Graph neural networks for graph drawing. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3184967

Traag VA, Waltman L, Van Eck NJ (2019) From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9(1):5233

Tutte WT (1963) How to draw a graph. Proc Lond Math Soc 3(1):743–767

Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579

Wang X, Yen K, Hu Y, Shen H-W (2021) DeepGD: a deep learning framework for graph drawing using GNN. IEEE Comput Graph Appl 41(5):32–44. https://doi.org/10.1109/MCG.2021.3093908

Wang Y, Jin Z, Wang Q, Cui W, Ma T, Qu H (2020) Deepdrawing: a deep learning approach to graph drawing. IEEE Trans Visual Comput Graph 26(1):676–686. https://doi.org/10.1109/TVCG.2019.2934798

Wang Y, Yao H, Zhao S (2016) Auto-encoder based dimensionality reduction. Neurocomputing 184:232–242. https://doi.org/10.1016/j.neucom.2015.08.104

Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386

Wu Z, Shen C, van den Hengel A (2019) Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recogn 90:119–133. https://doi.org/10.1016/j.patcog.2019.01.006

Xie C, Tan M, Gong B, Yuille A, Le QV (2021) Smooth adversarial training

Xue M, Wang Z, Zhong F, Wang Y, Xu M, Deussen O, Wang Y (2023) Taurus: towards a unified force representation and universal solver for graph layout. IEEE Trans Visual Comput Graph 29(1):886–895. https://doi.org/10.1109/TVCG.2022.3209371

You J, Ying R, Ren X, Hamilton W, Leskovec J (2018) GraphRNN: generating realistic graphs with deep auto-regressive models. In: Dy J, Krause A (eds) Proceedings of the 35th international conference on machine learning, vol 80. PMLR, 10–15 July, pp 5708–5717

Zellmann S, Weier M, Wald I (2020) Accelerating force-directed graph drawing with rt cores. In: 2020 IEEE visualization conference (VIS), pp 96–100. https://doi.org/10.1109/VIS47514.2020.00026

Zhao Y, Jiang H, Chen Q, Qin Y, Xie H, Wu Y, Liu S, Zhou Z, Xia J, Zhou F (2021) Preserving minority structures in graph sampling. IEEE Trans Visual Comput Graph 27(2):1698–1708. https://doi.org/10.1109/TVCG.2020.3030428

Zhou H, Xu P, Yuan X, Qu H (2013) Edge bundling in information visualization. Tsinghua Sci Technol 18(2):145–156. https://doi.org/10.1109/TST.2013.6509098

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61872432).

Author information

Shuhang Zhang

Present address: Data Analysis Application and Security Assess Lab, Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co., Ltd., Shanghai, China

Authors and Affiliations

School of Computer Science and Technology, Xidian University, Xi’An, China

Shuhang Zhang, Ruihong Xu, Qing Zhang & Yining Quan

China Telecom Bestpay Co., Ltd., Shenzhen, China

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yining Quan .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Zhang, S., Xu, R., Zhang, Q. et al. DeepFD: a deep learning approach to fast generate force-directed layout for large graphs. J Vis (2024). https://doi.org/10.1007/s12650-024-00991-1

Download citation

Received : 22 December 2023

Revised : 22 December 2023

Accepted : 20 February 2024

Published : 31 May 2024

DOI : https://doi.org/10.1007/s12650-024-00991-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Graph layout
Deep learning
Loss function
Find a journal
Publish with us
Track your research

This paper is in the following e-collection/theme issue:

Published on 31.5.2024 in Vol 26 (2024)

Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange

Authors of this article:

Original Paper

Dukyong Yoon 1, 2, 3 , MD, PhD ;
Changho Han 1 , MD ;
Dong Won Kim 1 , MS ;
Songsoo Kim 1 , MD ;
SungA Bae 3, 4 , MD, PhD ;
Jee An Ryu 1 , BS ;
Yujin Choi 1 , BS

1 Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea

2 Institute for Innovation in Digital Healthcare (IIDH), Severance Hospital, Seoul, Republic of Korea

3 Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea

4 Department of Cardiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea

Corresponding Author:

Dukyong Yoon, MD, PhD

Department of Biomedical Systems Informatics

Yonsei University College of Medicine

50-1 Yonsei-ro Seodaemun-gu

Seoul, 03722

Republic of Korea

Phone: 82 31 5189 8450

Email: [email protected]

Background: Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange.

Objective: This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability.

Methods: Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM ( International Classification of Diseases, Ninth Revision, Clinical Modification ), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes).

Results: The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names.

Conclusions: This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.

Introduction

Efficient health care data exchange is essential in medicine, particularly in facilitating continuous care [ 1 ]. Such data exchange becomes crucial when a patient uses multiple health care facilities or receives concurrent care, significantly influencing accurate treatment strategies. The emergence of personalized health care, becoming a cornerstone of modern medicine, necessitates the use of personal health records. This shift complicates data exchange processes as it demands the integration of data from multiple health care institutions, thereby posing substantial challenges [ 2 , 3 ]. Additionally, health care is increasingly including patient-generated health data (PGHD) from a diverse range of devices, including wearable technology, given the heterogeneity of products from different vendors [ 4 - 6 ].

Globally, health care systems contend with varying medical record formats and disparate coding systems. In the globalized health care paradigm, the mobility of patients across international boundaries introduces an added layer of complexity. The necessity for efficiently leveraging consolidated information from multiple nations escalates as international collaborative research broadens [ 7 , 8 ]. The International Classification of Diseases ( ICD ) has served as a global standard for diagnostic nomenclature, whereas the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) presents a detailed, structured, and multiaxial medical terminology system, gaining adoption worldwide, including in the United States and Europe. Divergent drug coding systems also continue to exist between the United States and Europe, with the RxNorm system adopted in the United States and the ATC system used across Europe. These discrepancies underscore the urgent need for robust and effective health care data exchange pipelines.

Over the years, significant attempts have been made toward the standardization of health care data amid notable challenges and limitations. Protocols, such as Health Level Seven International and Fast Healthcare Interoperability Resources (FHIR), have been introduced to enhance data exchange between medical devices and electronic health records [ 9 ]. However, despite their use, these standards often meet with noncompliance or suboptimal implementation. Specifically, FHIR has received criticism for its inherent complexity, obstructing its widespread adoption [ 10 ]. Moreover, a key obstacle in the exchange of health care data lies in the initial state of medical records, many of which are not stored following a universal standard. This inconsistency creates a significant challenge even before leveraging exchange protocols like Health Level Seven International and FHIR, designed to facilitate data sharing. The presence of standards does not automatically solve the issue of initiating the exchange when the starting point involves aligning diverse data formats.

The Observational Health Data Sciences and Informatics initiative represents one of the most robust efforts toward data standardization for research purposes. This initiative has developed a common data model and promoted data standardization across various institutions in accordance with this format, significantly accelerating data analysis across institutions [ 8 , 11 ]. Nonetheless, the standardization process has its limitations. One is a notable risk of information loss from the original data during standardization [ 12 , 13 ]. Despite sustained global efforts to transition data into standardized formats, the inherent challenges of standardization inhibit complete conversion and representation of the finer details in the original data. Therefore, effective data standardization remains a pervasive challenge in health care data exchange.

To address the challenges associated with data standardization, we attempted to explore alternatives beyond traditional approaches. A potential solution might be a system that supports flexible communication of raw data, for example, in natural language, permitting the end user to process and interpret data as required, thereby reducing the necessity for strict standardization. Large language models (LLMs), such as ChatGPT, which are designed to produce contextually relevant and coherent natural language responses based on input data, might be promising tools in this regard. Leveraging the capabilities of LLMs can enhance natural human interaction and streamline the management and summarization of extensive language-based data sets. Multiple studies have reported these potential applications of LLMs in the medical field; for example, mining medical text data for relevant clinical information, summarizing patient records and research findings, inferring medical outcomes from complex case histories, and reviewing medical literature to identify trends and validate clinical practices [ 14 - 17 ]. Consequently, if LLMs can proficiently transcribe patient data into text format and the receiving end can efficiently structure the resultant text data, then the intricate stages of data standardization may become redundant. This paradigm shift could significantly alter health care data exchange, heralding a future of seamless and universal data interoperability.

This study tests the hypothesis that text-based conversion and integration of hospital data in different databases would be more effective than current methods. To prove this, we focused on 3 aspects: accuracy of numerical data transformation into text and back, fidelity of text-based transformation for semantic data using ICD codes (ie, ICD-9-CM [ International Classification of Diseases, Ninth Revision, Clinical Modification ]), and effectiveness of extracting specific information, such as intensive care unit (ICU) medication details, during the transfer of text-format data. This study aims to demonstrate the potential of natural language–based systems for future health care data exchange.

Ethical Considerations

This study was approved by the institutional review board of Yongin Severance Hospital (9-2023-0037), and conducted in accordance with the Declaration of Helsinki, and the requirement for written informed consent was waived due to its retrospective nature.

Data Sources

This study used 2 comprehensive public health care data sets, namely, the UK Biobank and the Medical Information Mart for Intensive Care III (MIMIC-III). The UK Biobank serves as a notable national and international health resource, monitoring the lives of 500,000 voluntary participants aged between 40 and 69 years across the United Kingdom from 2006 to 2010. This resource aims to bolster the prevention, diagnosis, and treatment of a wide range of serious and life-threatening diseases. The data set includes genotypic and phenotypic data, covering medical, lifestyle, and environmental aspects. The UK Biobank contains structured data from diverse diagnostic tests, medical and family histories, and various physical measures. The MIMIC-III database, crafted by the Lab for Computational Physiology at MIT, is a broad, publicly available resource containing the deidentified health data of approximately 40,000 critical care patients [ 18 ]. This data set includes demographic information, vital signs, laboratory tests, and medications, among other features. It is valued for its over 2 million free-text clinical notes, presenting a rich source of natural language medical data.

This study used ChatGPT (version 3.5; OpenAI), an artificial intelligence model recognized for its exceptional performance among universally applicable models [ 19 - 21 ]. Given that our primary aim was to assess the ability of LLMs to facilitate health care data exchange in general scenarios, we opted against fine-tuning the model to prevent overspecialization to specific data sets. As a result, we used ChatGPT (version 3.5) in its original form, without any modifications. Furthermore, our focus was on testing the accuracy of information extraction and transformation rather than the creativity of the language model. Therefore, in all experiments, we set the temperature to 0 to ensure a deterministic output from the model.

The objectives of our study required the conduct of multiple trials featuring a range of prompts, a process termed prompt engineering. This process carries the potential risk of introducing an overfitting bias, which could boost the performance on specific data sets. Hence, we differentiated between the data used for prompt engineering experiments and those used to assess the performance of our experiments ( Figure 1 ). Given the absence of a standardized methodology for prompt engineering, researchers often carry out this process manually, relying on trial-and-error approaches based on experience.

Overview of the Experimental Design

We hypothesized that converting a hospital’s data into text format and then integrating such data in another hospital’s database can be more accurate and comprehensive compared with other data transformation methods. To prove this, we tested 3 key aspects ( Figure 2 ). First, we investigated whether the original data could be accurately conveyed when transformed into text (experiment 1). This involved converting numerical data into text and back into numerical form to check for any deviations from the original data. Second, we sought to validate that text-based transformation of information with numerical and semantic meaning would result in less distortion compared with rule-based transformations (experiment 2). To this end, we experimented with converting ICD -based diagnostic codes into text and back, comparing this with the results of converting them to and from the SNOMED-CT coding system. Finally, we evaluated whether the receiving institution could accurately extract specific desired information during the transmission of complex medical information in text form to another institution (experiment 3). In this experiment, we assumed that the content would resemble a discharge summary when all aspects of a patient’s hospital stay were compiled into a text format. Therefore, we aimed to test whether specific data, such as medication information prescribed in the ICU, could be accurately extracted from these summaries. In this experiment, we specifically worked under the assumption that the information to be extracted would be medication information prescribed in the ICU. From the 3 experiments, we aimed to evaluate the possibility of our hypothesis: a potential solution for health care data exchange in the future might be a system that supports flexible communication of raw data for example, in natural language (experiments 1 and 2), permitting the end user to process and interpret such data as required (experiment 3).

Experiment 1: Evaluating Accuracy in Data Exchange via an LLM

To evaluate the feasibility of data exchange using an LLM, we randomly selected laboratory test result data from 1000 individuals from the UK Biobank data set. For each individual, we gathered laboratory test results and converted them into an unstructured format. Subsequently, the data were restructured to comply with the MIMIC-III data architecture. The prompts used throughout this process are detailed in Textbox 1 .

Experiment 1

Step 1: Translating laboratory test results into free text

“I have the following patient. Based on this information, summarize the patient’s condition in natural language. Make sure to include all the information presented. The values of the lab results should remain numerical. (For the Sex variable, 0 = female and 1 = male.)”

{List of lab results}

Step 2: Transforming free text data into the structured format

“I have the following patient.”

{Generated text from the above step}

“Extract and organize information on the following items.”

(Add the value next to the variable name with no further explanation.)

{Defined result extraction format}

Experiment 2

Step 1: Translating diagnosis codes to natural language text

“I have a diagnosis called {Diagnosis code}.

Describe it in natural language used by doctors and other health care professionals.

Write it as a single phrase of only a few words (less than 15 words but do not use abbreviations).

All semantics must be included.”

Step 2: Translating natural language text to diagnosis codes

“Where does {Descriptions on diagnosis} fit in the following categories?

{Categories according to International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)}

Provide the most appropriate ICD-9-CM code directly or choose one of the categories above.

Choose only one answer that seems the most relevant and answer in the following format.

The corresponding code: [Code (without periods): a description of the code].”

Experiment 3

Step 1: Extracting medication list from discharge summary

role: “system,” “content:” Your role is to interpret medical records.

role: “assistant,” “content:” I only need prescriptions from the ICU, not from the general ward or not from outside our hospital.

Organize by ingredient name, not generic name.

Never include medications on admission and discharge medications.

Exclude information before ICU admission or after ICU discharge, even if it is for a hospital stay.

In other words, exclude prescriptions that were written in a regular ward or emergency room.

Exclude any medications that may not have been prescribed in the ICU.

Finally, exclude all prescriptions for procedures, and tests. that are not prescriptions for medication.

role: “user,” “content:” Observing the following patient record, organize a list of medications prescribed during the ICU visit.

Organize them in the following format (Provide only the name, not the dose)

drug name 1

drug name 2

If any information on the medications prescribed in the ICU is unavailable, simply answer “None.”

Step 2: Converting drug names to ingredient names

{extracted drug list from the above step}

Organize the above medications by ingredient name.

If the drug is recorded by trade name, replace it with the ingredient name.

In the case of multiple ingredient names, record a representative one.

The format should be a single line of ingredient names with no further explanation, like this

List: Ingredient 1, Ingredient 2,...

Step 3: Comparing extracted drug information with actual prescription records

Here is the medication information extracted from the discharge summary.

These are the medication details actually recorded in the prescription record.

{Ingredient list from the above step}

Organize the medication information extracted from the discharge summary by its actual inclusion in the prescription record.

Medications not mentioned in the discharge summary should not be listed.

The exact name of the medication may not be recorded, or a synonym may be used.

In these cases, mark the medication as actually prescribed.

For example, warfarin might be described as coumadin.

Record the same medication under different names as the one that was prescribed.

Match the same ingredient even if the added bases differ.

For example, the ingredient name of Lopressor is Metoprolol tartrate, but the ingredient must be confirmed as “true” even if it is Metoprolol.

Ingredient names may be written as abbreviations. For example, acetaminophen may be written as APAP.

Exclude P.R.N. prescriptions.

Exclude simple fluid prescriptions.

Provide only “true” or “false” information for each drug.

Do not provide Python code. Provide only the results in an array.

Fill in the blanks with a “true” or “false” result in the following format

After the conversion to MIMIC-III data format via the LLM, we checked for potential omissions of information and any discrepancies in numerical values. We assessed the absence or presence of data omissions using sensitivity, specificity, and positive and negative predictive values. To assess the accuracy of the conversion, we used values transformed manually as the reference standard. Sensitivity indicated whether information from the original data set also existed in the transformed data. Conversely, specificity pertained to whether data absent in the original were also absent in the transformed data. The positive predictive value (PPV) referred to whether data present in the transformed data also existed in the original, whereas the negative predictive value determined whether data absent in the transformed data were also absent in the original. Numerical discrepancies were calculated only for test results presented in numerical format. They were assessed via the computation of the mean squared error between the original and transformed values.

Experiment 2: Evaluating Possible Information Distortion During Conversion of Diagnosis Codes

In this experiment, we explored a scenario of diagnostic codes from the primary data set undergoing transformation for sharing across different institutions or to diverse end users. We aimed to clarify potential discrepancies emerging from transitions between the original and an alternate coding framework. Initially, we used a code-mapping table to facilitate the transition from one coding system to another. Subsequently, we reverted the transformed codes to the original coding framework, and then quantified discrepancies by comparing the reverted data against the primary data set. Using the MIMIC-III database, we converted diagnoses encoded in ICD-9-CM to SNOMED-CT, and subsequently reverted the same to ICD-9-CM . This conversion was based on a mapping table from a previous study [ 22 ]. Our proposed approach primarily leveraged the capabilities of the LLM, converting the primary coding structure into a natural text format. For a comparative analysis with the traditional approach, we recoded the text-converted diagnoses into the primary coding system ( ICD-9-CM ) using the LLM, as illustrated in Figure 2 B. However, for this experiment, we excluded E and V codes (supplementary classifications for external causes of injury).

In assessing the accuracy of the restoration of diagnostic codes, we conducted evaluations based on the depth of the ICD-9-CM coding system. The highest level was labeled level 1, with each subsequent, more specific layer labeled level 2, level 3, and so forth. For instance, if the original data had been coded as “401.1 Hypertension, benign” but the restored data were denoted as “401.9 Hypertension, unspecified,” then the evaluation would be a mismatch at level 3. However, at level 2 granularity (ie, “401. Hypertension”), the codes were considered matching.

Experiment 3: Assessing the Efficacy of LLMs in Extracting Targeted Information From Unstructured Medical Records

To evaluate the capability of our model in extracting targeted medical information from unstructured text, we selected narrative-style discharge summaries from the EVENTNOTES section of the MIMIC-III database, based on the assumption that they would reflect the comprehensive format typical of patient summaries transmitted between hospitals. These summaries provide a comprehensive account of a patient’s stay in the ICU, including clinicians’ assessments, patient medical history, laboratory results, interpretations of medical imaging, prescriptions, and ensuing care plans. This data set presents a detailed array of narrative insights that illustrate the complexities of patient care, diagnostics, and therapeutic strategies within the ICU context.

For this experiment, we specifically extracted discharge summaries documented by clinicians. These summaries encapsulated patient diagnoses, vital sign readings, current medication regimens, and other relevant status updates, all expressed in natural language. The prompts used in this process are presented in Textbox 1 .

To evaluate the performance, we compared the information extracted from natural language with the information stored in structured tables. For this assessment, we made a random selection of 1000 discharge summaries, and we used structured data—prescription records—to verify the accuracy of the information retrieved through the LLM. Our focus was on assessing the PPV, representing the precision of the information extracted by the LLM. The extracted information was considered correct if it was also present within the structured data; otherwise, it was classified as incorrect. Notably, not all prescriptions are routinely documented in natural language by clinicians. Generally, only therapeutics significantly influencing the patient’s clinical status would be transcribed in the notes. As such, calculating the negative predictive value (ie, the number of medications not mentioned in the narrative notes that were actually not administered) was deemed impracticable. Similarly, sensitivity (ie, the degree to which prescribed medications are documented in narrative notes) and specificity (ie, the extent to which nonprescribed medications are not mentioned in narrative notes) could not be reliably estimated.

Used Software

We accessed ChatGPT (version 3.5) via its API interface. We used Google BigQuery to manage and deploy the MIMIC-III and UK Biobank data sets. We used Python for certain tasks, such as assessing model performance.

Features of the Extracted Data

In experiment 1, we used the lab test results of 1000 individuals randomly selected from the UK Biobank data set. For experiment 2, we used all diagnosis codes recorded within the MIMIC-III database. Finally, we used 1000 discharge summaries extracted randomly from the MIMIC-III database for experiment 3. Table 1 presents a detailed summary of the data used across all experiments.

a MIMIC-III: Medical Information Mart for Intensive Care III.

b ICD-9-CM: International Classification of Diseases, Ninth Revision, Clinical Modification.

c N/A: not applicable.

Results of Experiment 1: Efficiency of the LLM in Data Transformation and Retrieval

In experiment 1, our objective was to assess the capability of the LLM in transforming and extracting laboratory results. We randomly selected the laboratory results of 1000 individuals from an initial data set of 502,396 individuals. This resulted in 11,996 data points spanning 13 distinct test items (excluding tests with null results). These data points were subsequently translated into natural language. Remarkably, only 23 items were lost during the transformation process, with 11,973 (99.8%) being successfully converted. Among the transformed data, 24 items did not match their original values perfectly. However, upon closer examination of these discrepancies, all inconsistencies were found to stem from the rounding off of decimal values. For instance, an original BMI value of 24.4383 was translated as 24.44. Consequently, the calculated mean squared error was a minimal 1.76e-07. Table 2 provides a comprehensive summary of errors for each laboratory test.

a MSE: mean squared error.

b ALT: alanine transaminase.

c AST: aspartate transaminase.

d GGT: gamma-glutamyl transferase.

e HbA 1c : hemoglobin A 1c .

f HDL: high-density lipoprotein.

g LDL: low-density lipoprotein.

Results of Experiment 2: Analysis of Diagnostic Code Conversion (Mapping Table vs Text-Based Methods)

In the conversion, diagnostic codes were adapted based on a mapping table. Specifically, the original ICD-9-CM codes transitioned through SNOMED-CT before being remapped to ICD-9-CM . During this procedure, 5748 diagnostic codes expanded to 218,088 codes. This expansion may be attributed to the fact that specific mapping codes do not always allow for a direct 1:1 representation, leading to a 1:n relationship owing to challenges in semantic translation. As an illustration, the ICD-9-CM code for “Malignant pleural effusion: Malignant pleural effusion (51181)” was mapped as 2 distinct codes in SNOMED-CT: “Malignant pleural effusion (363346000)” and “Pleural effusion owing to malignant neoplastic disease (disorder) (860792009).” However, when converting through text, the mapping was nearly direct with a 1:1 ratio, ensuring that the 5748 original codes corresponded to 5748 records.

Assessing the results before and after the conversion, we found that the mapping table achieved the following consistency values: 0.096 (21,000/218,088), 0.248 (54,068/218,088), and 0.626 (136,431/218,088) at levels 3, 2, and 1, respectively. Conversely, when relying on text-based methods, the consistency was higher, with corresponding values of 0.597 (3430/5748), 0.844 (4850/5,748), and 0.904 (5197/5748) for the same levels. An important observation pertained to the accuracy of conversion in relation to frequency use is that as the frequency increased, accuracy followed suit. Specifically, the top 1000 diagnostic names, based on their frequency, achieved values of 0.733, 0.896, and 0.918 at levels 3, 2, and 1, respectively, outperforming less common names. This observed relation was linear, as demonstrated in Figure 3 . These results suggested that the frequent use of diagnostic names may provide better precision when shared between different databases.

During a review of the misclassified instances, we identified several cases as errors based on our evaluation standards. Notably, the semantic core of the original and converted phrases remained largely consistent. For example, we observed a transformation from “51881: Acute respiratory failure” to “78609: Respiratory abnorm NEC: Other respiratory abnormalities.” A comprehensive list of these misclassifications is provided in Table S1 in Multimedia Appendix 1 .

Results of Experiment 3: Effectiveness of the LLM in Extracting Relevant Information From Medical Records

In reviewing 1000 discharge summaries, the LLM identified a total of 5604 instances of medication prescriptions within the ICU setting. Of these, 2483 perfectly matched the entries in the prescription table, resulting in a PPV of 44.3%. When evaluated based on the shared active ingredient, we found a higher level of agreement, with 5055 out of the 5604 (90.2%) prescriptions showing alignment ( Table 3 ). These findings, as exemplified by instances where “Acetaminophen” in the prescription information was referred to as “Paracetamol” in the discharge summaries and cases where “Metoprolol Tartrate” was simply documented as “Metoprolol,” underscore the tendency of physicians to note down familiar medication names. This behavior occurs instead of strictly adhering to the terminology prescribed in the prescription database. These examples highlight a preference for more universally recognized or familiar terms over the precise terminology listed in medical records. Despite this inherent variability in naming conventions, the LLM showed significant effectiveness in identifying and extracting the necessary information.

a A total of 2572 medications were described using different terminology than the prescription.

Our research highlighted a new direction in health care by demonstrating the effective use of LLMs in medical data exchange. We aimed to overcome the current challenges related to data sharing among health care institutions, particularly owing to the unstructured nature of several medical records. We successfully validated all the key aspects we aimed to investigate, demonstrating the efficacy of our approach in enhancing health care data interoperability. The experiments revealed that converting hospital data into text format and subsequently integrating the converted data into another hospital’s database was not only feasible but also more accurate and comprehensive compared with traditional data transformation methods. Notably, our findings confirmed that the original data retained their accuracy and integrity when transformed into and back from the text format, a crucial factor in health care where precision is paramount. Moreover, our results indicated that text-based transformation, particularly for semantically rich information such as ICD -based diagnostic codes, resulted in significantly less distortion compared with rule-based methods. Finally, our method effectively enhanced medical data exchange by enabling precise extraction of specific information, such as ICU medication details, from text-transmitted data, thus, bolstering health care systems’ efficiency in integrating such data.

Our study highlights the significant role of LLMs in the field of health care informatics, demonstrating their transformative ability to manage, interpret, and share large volumes of medical data. Traditional data standardization methods, while important, have often been slow and challenging, creating barriers to fast and efficient data exchange. Our results showed that LLMs can not only interpret unstructured data but also convert it into easily understandable formats, greatly reducing the need for time-consuming standardization and allowing for faster data transfer.

Furthermore, the impact of our research extends beyond the clinical or institutional settings, affecting the broader area of personal health records. Integrating data from multiple providers into a single, unified record has always been a complex task. Different institutions often use varied formats, terminologies, and standards. Our work with LLMs suggested that these models can simplify this integration process. By understanding, transforming, and combining different data sources, LLMs can improve data sharing and enrich the information available.

LLMs’ adaptability in processing and interpreting structured and unstructured data hints at their potential to significantly enhance the handling of PGHD. Given the variety and unstructured nature of PGHD, from health diaries to wearable technology outputs, our findings suggest a promising avenue for applying LLMs to integrate and understand these diverse data sources more effectively. This capability aligns with our current results. Moreover, it opens up new pathways for creating more personalized and comprehensive approaches to patient care, leveraging the vast and untapped resources of PGHD.

Our study also provided significant insights into the process of converting diagnostic codes between standard coding systems, such as ICD-9-CM and SNOMED-CT. The higher number of diagnostic codes produced through this conversion process highlights the detailed and comprehensive nature of code capture enabled by the LLM. However, the approximate 1:1 ratio achieved in text-based conversions points to a more accurate and straightforward method. Importantly, these text-based conversions emphasize the major advantage of keeping the accuracy of the original data. For frequently used diagnostic terms, this method ensured that the core information from the original data remained consistent. Our examination of misclassifications revealed that, although identified as errors based on our criteria, several converted codes maintained similarity in their underlying meaning. Thus, despite “errors” in conversion, the core medical information is typically retained. Moreover, the direct relationship between the accuracy of conversion and frequency of diagnostic names hints at a possible inherent alignment of standard coding systems with commonly used terms. Our findings highlighted the critical importance of preserving data accuracy when moving between detailed medical coding systems. This aligns with the findings of previous studies, which suggest that using LLMs can lead to more accurate phenotype extraction from medical data [ 23 , 24 ].

Our findings have implications beyond individual health care systems and emphasize the potential for a significant change in the global health care landscape. Our data revealed that using LLMs can enhance international health information exchanges. Such improved communication can lead to better collaboration between countries, potentially benefiting patient care worldwide by ensuring that medical knowledge and practices are more consistently applied. Furthermore, our research points to a new direction in the design and operation of electronic medical record systems. The ability of LLMs to efficiently process and structure natural language data can make extracting, analyzing, and presenting medical data more straightforward. This not only allows for immediate analyses using the latest data but also promotes a more adaptable environment within electronic medical record systems to meet the dynamic needs of the health care sector, as illustrated in Figure 3 .

While our study demonstrates the promising capabilities of LLMs in medical data processing, it is not without limitations. In this study, we used the GPT-3.5 model. Notably, using the newer GPT-4 might lead to better results, given that the efficiency of LLMs is continually improving. Comparative studies have demonstrated that GPT-4 performs better than its predecessors in various domains [ 25 , 26 ]. This progress in language model capabilities indicates the ongoing advancements we can expect. In addition to technological considerations, our reliance on specific data sets such as MIMIC-III and the UK Biobank, while providing valuable insights, introduces limitations regarding representativeness across diverse health care environments and languages. These data sets, representing particular health care settings and populations, may not fully encapsulate the complexity and diversity of global medical practices, especially in non–English speaking countries. This aspect underscores the necessity for broader research in applying LLMs across more varied data sets to ensure generalizability and applicability to different health care contexts. Regarding technological improvements, on-premise solutions can be expected to continue to improve in capabilities. Hence, our research serves as a foundation, showing the feasibility of data exchange based on LLMs. The accuracy and use of these transformations will be enhanced further in future versions. For institutions concerned with security implications, transitioning from externally provided models, such as ChatGPT, to an on-premise, self-built language model is a recommended strategy. Custom-built models can match the performance of GPT-3.5 for specific tasks [ 27 , 28 ]. Our choice to evaluate performance using the 3.5 version in this research provides a reference point and offers guidance for users considering the use of their custom language models.

Our research focused on specific data sets, and more extensive studies involving a wider range of data would be needed to confirm our initial observations. Moreover, the ability of LLMs to handle different types of unstructured data, each with its unique challenges, requires thorough assessment. Nevertheless, with ongoing advancements in artificial intelligence and machine learning, we expect that these challenges will be addressed, and the efficiency of LLMs in managing medical data will continue to improve. Future versions of LLMs, combined with careful validation, can bring significant improvements to health care informatics.

Conclusions

In conclusion, our in-depth study provides important insights into the potential transformation of health care data exchange in the near future. The LLMs have a significant role in enhancing medical data sharing, ensuring both precision and efficiency. As technology advances and these language models become more refined, their role in health care data management and communication is anticipated to expand. Their potential goes beyond merely simplifying processes; they might also play a key role in minimizing errors, guaranteeing that medical professionals worldwide can access accurate and timely data. Ultimately, our findings suggest that with the incorporation of LLMs, the global health care landscape could become more unified, facilitating seamless knowledge transfer and collaboration among health care providers everywhere.

Acknowledgments

This research was supported by a grant of the Korea Health Technology R&D (Research and Development) Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number HI22C0452). This study was conducted using data from UK Biobank (application number 85037).

Data Availability

The data sets analyzed during this study are available in the UK biobank [ 29 ] and MIMIC-III [ 30 ] repository.

Conflicts of Interest

None declared.

A comprehensive list of misclassifications in experiment 2.

Vest JR, Gamm LD. Health information exchange: persistent challenges and new strategies. J Am Med Inform Assoc. 2010;17(3):288-294. [ FREE Full text ] [ CrossRef ] [ Medline ]
Archer N, Fevrier-Thomas U, Lokker C, McKibbon KA, Straus SE. Personal health records: a scoping review. J Am Med Inform Assoc. 2011;18(4):515-522. [ FREE Full text ] [ CrossRef ] [ Medline ]
Tang PC, Ash JS, Bates DW, Overhage JM, Sands DZ. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc. 2006;13(2):121-126. [ FREE Full text ] [ CrossRef ] [ Medline ]
Celik Y, Godfrey A. Bringing it all together: wearable data fusion. NPJ Digit Med. 2023;6(1):149. [ FREE Full text ] [ CrossRef ] [ Medline ]
Kruse CS, Argueta DA, Lopez L, Nair A. Patient and provider attitudes toward the use of patient portals for the management of chronic disease: a systematic review. J Med Internet Res. 2015;17(2):e40. [ FREE Full text ] [ CrossRef ] [ Medline ]
Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA. 2015;313(5):459-460. [ CrossRef ] [ Medline ]
Voss EA, Makadia R, Matcho A, Ma Q, Knoll C, Schuemie M, et al. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. J Am Med Inform Assoc. 2015;22(3):553-564. [ FREE Full text ] [ CrossRef ] [ Medline ]
Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc. 2012;19(1):54-60. [ FREE Full text ] [ CrossRef ] [ Medline ]
Saripalle R, Runyan C, Russell M. Using HL7 FHIR to achieve interoperability in patient health record. J Biomed Inform. 2019;94:103188. [ FREE Full text ] [ CrossRef ] [ Medline ]
Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc. 2016;23(5):899-908. [ FREE Full text ] [ CrossRef ] [ Medline ]
Suchard MA, Schuemie MJ, Krumholz HM, You SC, Chen R, Pratt N, et al. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet. 2019;394(10211):1816-1826. [ FREE Full text ] [ CrossRef ] [ Medline ]
Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13(6):395-405. [ CrossRef ] [ Medline ]
Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013;20(1):144-151. [ FREE Full text ] [ CrossRef ] [ Medline ]
Fink MA, Bischoff A, Fink CA, Moll M, Kroschke J, Dulz L, et al. Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer. Radiology. 2023;308(3):e231362. [ CrossRef ] [ Medline ]
Tang L, Sun Z, Idnay B, Nestor JG, Soroush A, Elias PA, et al. Evaluating large language models on medical evidence summarization. NPJ Digit Med. 2023;6(1):158. [ FREE Full text ] [ CrossRef ] [ Medline ]
Guo E, Gupta M, Deng J, Park Y, Paget M, Naugler C. Automated paper screening for clinical reviews using large language models: data analysis study. J Med Internet Res. 2024;26:e48996. [ FREE Full text ] [ CrossRef ] [ Medline ]
Han C, Kim DW, Kim S, Chan You S, Park JY, Bae S, et al. Evaluation of GPT-4 for 10-year cardiovascular risk prediction: insights from the UK Biobank and KoGES data. iScience. 2024;27(2):109022. [ FREE Full text ] [ CrossRef ] [ Medline ]
Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035. [ FREE Full text ] [ CrossRef ] [ Medline ]
Patel SB, Lam K. ChatGPT: the future of discharge summaries? Lancet Digit Health. 2023;5(3):e107-e108. [ FREE Full text ] [ CrossRef ] [ Medline ]
Biswas SS. Role of Chat GPT in public health. Ann Biomed Eng. 2023;51(5):868-869. [ CrossRef ] [ Medline ]
van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL. ChatGPT: five priorities for research. Nature. 2023;614(7947):224-226. [ CrossRef ] [ Medline ]
OHDSI vocabularies repository. ATHENA. URL: https://athena.ohdsi.org/search-terms/start [accessed 2023-12-02]
Cunningham JW, Singh P, Reeder C, Claggett B, Marti-Castellote PM, Lau ES, et al. Natural language processing for adjudication of heart failure in a multicenter clinical trial: a secondary analysis of a randomized clinical trial. JAMA Cardiol. 2024;9(2):174-181. [ CrossRef ] [ Medline ]
Alsentzer E, Rasmussen MJ, Fontoura R, Cull AL, Beaulieu-Jones B, Gray KJ, et al. Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models. NPJ Digit Med. 2023;6(1):212. [ FREE Full text ] [ CrossRef ] [ Medline ]
Taloni A, Borselli M, Scarsi V, Rossi C, Coco G, Scorcia V, et al. Comparative performance of humans versus GPT-4.0 and GPT-3.5 in the self-assessment program of American Academy of Ophthalmology. Sci Rep. 2023;13(1):18562. [ FREE Full text ] [ CrossRef ] [ Medline ]
Rosoł M, Gąsior JS, Łaba J, Korzeniewski K, Młyńczak M. Evaluation of the performance of GPT-3.5 and GPT-4 on the Polish medical final examination. Sci Rep. 2023;13(1):20512. [ FREE Full text ] [ CrossRef ] [ Medline ]
Latif E, Zhai X. Fine-tuning ChatGPT for automatic scoring. ArXiv. Preprint posted online on October 16, 2023. [ FREE Full text ]
Soman K, Rose PW, Morris JH, E Akbas R, Smith B, Peetoom B, et al. Biomedical knowledge graph-enhanced prompt generation for large language models. ArXiv. Preprint posted online on November 29, 2023. [ FREE Full text ]
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. Mar 2015;12(3):e1001779. [ FREE Full text ] [ CrossRef ] [ Medline ]
Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. May 24, 2016;3:160035. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Tsafnat; submitted 22.01.24; peer-reviewed by J Lee, S Lee; comments to author 22.03.24; revised version received 22.04.24; accepted 27.04.24; published 31.05.24.

©Dukyong Yoon, Changho Han, Dong Won Kim, Songsoo Kim, SungA Bae, Jee An Ryu, Yujin Choi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

COMMENTS

Research Methodology
The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
Your Step-by-Step Guide to Writing a Good Research Methodology
Provide the rationality behind your chosen approach. Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome. 3. Explain your mechanism.
What Is a Research Methodology?
Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, ... The research design is a strategy for answering your research questions. It determines how you will collect and analyze your data. 4823.
Research Design
Table of contents. Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies.
The Ultimate Guide To Research Methodology
Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.
How to Write Research Methodology: 13 Steps (with Pictures)
A quantitative approach and statistical analysis would give you a bigger picture. 3. Identify how your analysis answers your research questions. Relate your methodology back to your original research questions and present a proposed outcome based on your analysis.
Research Methodology Example (PDF + Template)
Research methodology 101: an introductory video discussing what a methodology is and the role it plays within a dissertation; Research design 101: an overview of the most common research designs for both qualitative and quantitative studies; Variables 101: an introductory video covering the different types of variables that exist within research.
What Is a Research Methodology?
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.
Types of Research Designs Compared
Types of Research Designs Compared | Guide & Examples. Published on June 20, 2019 by Shona McCombes.Revised on June 22, 2023. When you start planning a research project, developing research questions and creating a research design, you will have to make various decisions about the type of research you want to do.. There are many ways to categorize different types of research.
Research Methods
Research Methods | Definition, Types, Examples. Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data.
What is Research Methodology? Definition, Types, and Examples
A research methodology should include the following components: 3,9. Research design—should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory. Research method—this can be quantitative, qualitative, or mixed-method.
What is research methodology? [Update 2024]
A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more. You can think of your research methodology as being a formula. One part will be how you plan on putting your research into ...
Research Methods Guide: Research Design & Method
Research design is a plan to answer your research question. A research method is a strategy used to implement that plan. Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively. Which research method should I choose?
Research Design
Research Design Research Methodology; The plan and structure for conducting research that outlines the procedures to be followed to collect and analyze data. The set of principles, techniques, and tools used to carry out the research plan and achieve research objectives.
What Is Research Methodology? Definition + Examples
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 ...
What is a Research Design? Definition, Types, Methods and Examples
Research design methods refer to the systematic approaches and techniques used to plan, structure, and conduct a research study. The choice of research design method depends on the research questions, objectives, and the nature of the study. Here are some key research design methods commonly used in various fields: 1.
What is design research methodology and why is it important?
Design research is the process of gathering, analyzing and interpreting data and insights to inspire, guide and provide context for designs. It's a research discipline that applies both quantitative and qualitative research methods to help make well-informed design decisions. Not to be confused with user experience research - focused on the ...
Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
The Complete Guide to UX Research Methods
UX research includes two main types: quantitative (statistical data) and qualitative (insights that can be observed but not computed), done through observation techniques, task analysis, and other feedback methodologies. The UX research methods used depend on the type of site, system, or app being developed.
Qualitative Significance as First-Class Evidence in the Design and
Based on the results of a mixed-methods RCT research design, this article explores how qualitative research tools might provide qualitative significance - information as valuable as numerical data obtained using standardised scales. This proposal is based on the results of a research project addressing problems associated with barriers to ...
Contents and Layout of Research Report
The layout of research report means as to what the research report should contain. The contents of the research report are noted below: Preliminary Page. Main Text. End Matter. (1) Preliminary Pages: These must be title of the research topic and data. There must be preface of foreword to the research work.
Understanding the use of co-design methods for research involving older
There is a growing population of adults aged 50 years or older living with HIV, facing unique challenges in care due to age, minority status, and stigma. Co-design methodologies, aligning with patient-centered care, have potential for informing interventions addressing the complex needs of older adults with HIV. Despite challenges, co-design has shown promise in empowering older individuals to ...
Modifications of the readiness assessment for pragmatic trials tool for
Our research team had expertise in qualitative research, co-design and co-creation, public health, health equity, survey methodology, quality improvement, and clinical care in older adult settings. The researchers recognise the right of Indigenous peoples, and the right of people living with dementia, to experience equitable health outcomes.
DeepFD: a deep learning approach to fast generate force-directed layout
This section reviews the related work of this paper, which includes two parts: graph drawing and deep learning techniques on graph drawing. 2.1 Graph drawing. Graph layout has a profound research history, and its earliest research can be traced back to the barycentric method proposed by Tutte ().Nowadays, diverse approaches are developed based on different principles such as force based ...
Guide to Experimental Design
Step 1: Define your variables. You should begin with a specific research question. We will work with two research question examples, one from health sciences and one from ecology: Example question 1: Phone use and sleep. You want to know how phone use before bedtime affects sleep patterns.
Comprehensive Analysis and Design of Electrical Power Systems for
To achieve a robust validation of the most appropriate MPPT control method under steady-state conditions, this paper presents an experimental investigation into the proposed EPS hardware design. The proposed SMC method achieved an increase in power generation from 10% to 12% for buck and boost power converters, respectively, compared to ...
Journal of Medical Internet Research
Background: Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. Objective: This study aims to evaluate the capability of LLMs in transforming and transferring ...
Applied Sciences
Time-domain numerical simulation is generally considered an accurate method to predict the mooring system performance, but it is also time and resource-consuming. This paper attempts to completely replace the time-domain numerical simulation with machine learning approaches, using a catenary anchor leg mooring (CALM) system design as an example.
How to Write a Research Proposal
Research design and methods. Following the literature review, restate your main objectives. This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

The Ultimate Guide To Research Methodology

What Is Research Methodology?

Facilitating Rigorous Inquiry

Ensuring Reproducibility And Reliability

Guiding Decision-Making Processes

Contributing To Academic Excellence

Enhancing Problem-Solving Abilities

Understanding Research Methodology

Basics Of Research

Types of Research

Components Of Research Methodology

Research Design

Data Collection Methods

Data Analysis Techniques

The research paper we write have:

Choosing a Research Method

Quantitative Research Methods

Experimental Research

Survey Research

Descriptive Research

Qualitative Research Methods

Ethnography

Grounded Theory

Exploratory, Descriptive, And Explanatory Designs

Descriptive Design

Explanatory Design

Cross-Sectional Vs. Longitudinal Designs

Longitudinal Design

Experimental Vs Non-experimental Designs

Non-Experimental Design

Designing Effective Surveys

Conducting Interviews

Observation

2. Non-participant Observation

Archival Research

2. Challenges and Considerations

Common Challenges in Research Methodology

Sampling Issues

Sample Size Determination:

Data Quality And Validity

Construct Validity:

Time And Resource Constraints

Resource Availability:

Managing Bias in Research

Confirmation Bias:

Tips On How To Write A Research Methodology

Tip 1. Clear Research Objectives

Tip 2. Comprehensive Literature Review

Tip 3. Detailed Research Plan

Tip 4. Ethical Considerations

Tip 5. Stay Updated On Methodologies

Tip 6. Adaptability In Methods

Tip 7. Iterative Approach

Frequently Asked Questions

What are the methodologies in research?

How to write research methodology?

How to write the methodology section of a research paper?

What is mixed research methodology?

You May Also Like

USEFUL LINKS

How to Write Research Methodology

Describing Your Methods

Justifying Your Choice of Methods

Connecting Your Methods to Your Research Goals

Template to Write Research Methodology

Community Q&A

You Might Also Like

About This Article

Reader Success Stories

Did this article help you?

Featured Articles

Trending Articles

Watch Articles

Have a language expert improve your writing

Research Methods | Definition, Types, Examples

Table of contents

Qualitative vs quantitative data

Primary vs secondary data

Descriptive vs experimental data

Prevent plagiarism, run a free check.