the type of research methodology

Research Methodology Explained: A Beginner's Guide

Research methodology stands as the backbone of credible study, guiding the generation and analysis of data towards solving research queries. It encompasses not just the practical aspects of data collection but also the theoretical framework that shapes the study's direction, distinguishing methodology in research from mere methods.

This foundational process, characterized by its systematic, logical, empirical, and replicable nature, underscores the importance of research methodology in contributing to the vast expanse of knowledge across disciplines.

Beyond a mere overview, we will explore varied research methodology types such as applied, basic, and correlational research, offering insight into how each approach serves the objectives of research methodology. Through a methodological approach, readers will gain knowledge of the critical steps and decisions that shape a robust study, from selecting the right research methodology to interpreting findings.

Understanding Research Methodology

Research methodology is essential in scientific investigations, providing a structured approach to data collection, analysis, and interpretation. This systematic method ensures that research findings are reliable, valid, and generalizable, making it possible to draw credible conclusions that contribute to existing knowledge.

Key Elements of Research Methodology

Research Design : This includes the overall strategy that outlines the procedures for collecting, analyzing, and interpreting data. The design is crucial as it helps align the research methods with the objectives of the study, ensuring that the results are effective in addressing the research questions.
Data Collection Methods : Depending on the nature of the study, researchers may employ various techniques such as surveys, interviews, or observation. Each method is chosen based on its ability to gather the necessary data effectively.
Data Analysis Techniques : After data collection, the next step is analyzing this data to derive meaningful insights. Techniques vary widely from statistical analysis in quantitative studies to content analysis in qualitative research.

Research Approaches and Their Applications

Qualitative Methods : These are used to gather in-depth insights into people’s attitudes, behaviors, and experiences and often involve methods like interviews and focus groups.
Quantitative Methods : In contrast, quantitative methods focus on numerical data and often employ statistical tests to validate hypotheses.
Mixed Methods : Combining both qualitative and quantitative approaches, mixed methods provide a comprehensive analysis that strengthens the research findings by addressing the limitations of each method alone.

By employing a well-structured research methodology, scientists and scholars can ensure that their studies are robust, replicable, and impactful. This foundation not only supports the validity of the research findings but also enhances the overall credibility of the scientific inquiry.

Types of Research Methodology

Overview of methodological approaches.

The landscape of research methodology is dominated by three primary approaches: quantitative, qualitative, and mixed methods. Each approach offers unique insights and tools for investigation, catering to different research objectives.

Objective : Focuses on quantifying data and generalizing results from a sample to a larger population.
Methods : Employs structured techniques such as surveys and statistical analysis to produce numerical data.
Applications : Ideal for testing hypotheses, establishing patterns, and making predictions.
Objective : Aims to provide a detailed description and interpretation of research subjects.
Methods : Utilizes interviews, focus groups, and observations to gather in-depth, non-numerical data.
Applications : Best suited for exploring complex concepts and understanding underlying motivations or behaviors.
Objective : Combines elements of both qualitative and quantitative research to cover more ground.
Methods : Integrates numerical data analysis with detailed descriptions, enhancing the robustness of the findings.
Applications : Useful for validating quantitative data with qualitative insights and explaining anomalies.

Data Collection and Analysis Techniques

Each methodological approach employs specific techniques for data collection and analysis, tailored to its unique requirements.

Data Collection : Includes sampling, surveys, and structured observations.
Data Analysis : Features statistical methods such as regression analysis, correlation, and descriptive statistics.
Data Collection : Comprises one-on-one interviews, document reviews, and qualitative observations.
Data Analysis : Involves methods like thematic analysis, discourse analysis, and narrative analysis.
Data Collection : A combination of both quantitative and qualitative data collection methods.
Data Analysis : Integrates quantitative statistical analysis with qualitative content analysis.

Sampling Designs

The choice of sampling design plays a critical role in the credibility and generalizability of the research.

Types : Includes simple random, stratified, systematic, and cluster sampling.
Feature : Each member of the population has a known chance of being selected.
Types : Encompasses convenience, purposive, snowball, and quota sampling.
Feature : Selection is based on the researcher’s judgment, often used when probability sampling is not feasible.

This structured approach to understanding the types of research methodology not only clarifies the distinctions between them but also highlights their specific applications and techniques, providing a comprehensive framework for researchers to base their methodological choices.

Choosing the Right Research Methodology

Assessing research goals and context.

Clarify Research Objectives : It's crucial to start by clearly understanding the research goals, objectives, and questions. This clarity will guide the choice of methodology, ensuring it aligns with what you aim to discover or prove.
Evaluate the Setting and Participants : Consider the physical, social, or cultural context of the study along with the characteristics of the population involved. This assessment helps in choosing a methodology that is sensitive to contextual variables and participant demographics.

Methodological Considerations

Review Previous Studies : Look at the methodologies employed in previous research within the same discipline or those that addressed similar objectives. This can provide insights into what methods might be most effective or what new approaches could offer fresh perspectives.
Practical Constraints : Acknowledge any practical limitations such as experimental conditions, resource availability, and time constraints. These factors can significantly influence the feasibility of certain research methodologies over others.

Choosing Between Qualitative and Quantitative Approaches

Quantitative Research : Opt for quantitative methods when the goal is to quantify data and generalize results from a sample to a larger population. This approach is suitable for establishing facts or testing hypotheses.
Qualitative Research : Choose qualitative methods if the aim is to gain a deeper understanding of people’s experiences or perspectives. This approach is ideal for exploring complex issues in detail.
Mixed Methods : Consider using mixed methods to leverage the strengths of both qualitative and quantitative approaches, especially when the research aims to provide a comprehensive analysis of the topic.

By carefully considering these factors, researchers can select the most appropriate methodology to address their specific research questions effectively and efficiently.

Key Components of Research Methodology

Research design and planning.

Clarify Research Objectives : Begin by defining clear and measurable objectives, which guide all subsequent decisions in the research process.
Select Research Type : Determine whether the study is exploratory, descriptive, explanatory, or experimental, as this shapes the research design.
Choose Appropriate Methods : Based on the research type, select methods for data collection and analysis that best suit the study's needs.

Data Collection and Analysis

Qualitative : Includes interviews, focus groups, and observations, which provide depth and context.
Quantitative : Involves surveys and experiments that yield quantifiable data for statistical analysis.
Probability Sampling : Ensures every member of the population has a known chance of selection.
Nonprobability Sampling : Used when probability sampling isn't feasible; based on researcher’s judgment.

Ethical Considerations and Methodological Rigor

Ethical Standards : Adhere to ethical guidelines such as informed consent, confidentiality, and minimizing harm.
Validity and Reliability : Implement measures to ensure the research is both valid (accurately measures what it is supposed to measure) and reliable (yields consistent results).
Pilot Testing : Conduct preliminary testing to refine data collection strategies and address potential issues.

By integrating these components, researchers can enhance the credibility and impact of their studies, ensuring that findings are both trustworthy and actionable.

Throughout this exploration of research methodology, we have journeyed from the foundational principles that delineate methodology from mere methods to the intricate distinctions between qualitative, quantitative, and mixed methods research.

This comprehensive guide underscores the pivotal role that a well-structured methodology plays in validating research findings, enhancing the credibility of scientific inquiries, and ultimately, contributing to the vast expanse of knowledge across various fields.

For those looking to dive deeper into the intricacies of research methods or seeking to refine their methodology choice, tools like TLDR This offer valuable resources for further exploration and understanding. By continually engaging with research methodologies and embracing their evolution, the scientific community can forge new paths of discovery, innovation, and impact.

1. How can one describe their research methodology effectively?

To effectively describe your research methodology, follow these steps:

Begin by restating your thesis or research problem.
Detail the approach you chose for the research.
Mention any unique methodologies you employed.
Describe the data collection process.
Explain how the data was analyzed.

2. What are the main types of research methodologies?

The four primary research methodologies are:

Qualitative research, which focuses on understanding concepts, thoughts, or experiences.
Quantitative research, which involves the statistical, mathematical, or numerical analysis of data.
Mixed methods research, which combines elements of both qualitative and quantitative research.

3. What does the term "research methodology" mean for beginners?

Research methodology refers to the section in a research paper that outlines the tools, techniques, and procedures used to gather and analyze data. This section is crucial as it helps readers assess the study's reliability and validity.

4. What are the seven fundamental research methods commonly used?

The seven basic research methods frequently utilized in studies are:

Observation and Participant Observation
Focus Groups
Experiments
Secondary Data Analysis or Archival Study
Mixed Methods, which is a combination of several of the aforementioned methods.

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About Research Methods

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

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

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

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

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

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

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

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

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

Library GIS Services

Other Data Services at Berkeley

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

General Research Methods Resources

Here are some general resources for assistance:

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

Consultants

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

Related Resourcex

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

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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.

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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.

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Choosing the Right Research Methodology: A Guide for Researchers

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Table of Contents

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

Understanding different research methods:

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

Qualitative vs quantitative research:

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

Qualitative research methodology:

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

Quantitative research methodology:

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

Analysing qualitative vs quantitative data:

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

When to use qualitative vs quantitative research:

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

Conclusion:

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

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A Comprehensive Guide to Methodology in Research

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

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

What is Research Methodology?

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

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

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

How to Define Research Methodology

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

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

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

Role of Methodology in Research

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

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

Steps in Choosing the Right Research Methodology

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

Identifying the Research Problem

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

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

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

Reviewing Previous Research

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

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

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

Formulating Research Questions

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

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

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

Implementing the Research Methodology

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

Data Collection Techniques

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

Data Analysis Methods

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

Ensuring the Validity and Reliability of Your Research

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

Understanding Validity in Research

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

Ensuring Reliability in Your Study

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

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

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How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

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

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

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

Overview: Choosing Your Methodology

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

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

1. Understanding the options

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

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

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

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

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

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

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

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

2. How to choose a research methodology

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

The three factors you need to consider are:

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

Let’s take a look at each of these.

Factor #1: The nature of your research

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

But, what types of research exist?

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

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

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

Let’s look at an example in action.

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

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

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

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

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

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Factor #2: The disciplinary norms

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

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

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

Factor #3: Practicalities

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

But what constraints, you ask?

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

Data access
Equipment and software
Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

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

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

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

Constraint #2: Time

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

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

Constraint #3: Money

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

Some of the costs that may arise include:

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

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

Constraint #4: Equipment & software

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

Constraint #5: Your knowledge and skillset

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

Some of the questions you should ask yourself are:

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

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

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

Recap: Choosing a methodology

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

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

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

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

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Very useful and informative especially for beginners

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

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

Hi I am Anna ,

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

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

I found the post very informative and practical.

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

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

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Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

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

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

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

Classification of Types of Research

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

According to its Purpose

Theoretical research.

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

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

Applied Research

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

This type of research is subdivided into two types:

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

According to your Depth of Scope

Exploratory research.

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

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

Descriptive Research

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

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

Explanatory Research

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

Correlational Research

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

According to the Type of Data Used

Qualitative research.

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

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

Quantitative Research

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

According to the Degree of Manipulation of Variables

Experimental research.

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

Non-Experimental Research

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

Quasi-Experimental Research

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

According to the Type of Inference

Deductive investigation.

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

Inductive Research

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

Hypothetical-Deductive Investigation

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

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

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

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

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

According to The Sources of Information

Primary research.

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

Secondary research

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

According to How the Data is Obtained

Documentary (cabinet).

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

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

From Laboratory

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

Mixed-Method: Documentary, Field and/or Laboratory

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

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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.

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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.

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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.

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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.

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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.

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Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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15 Types of Research Methods

types of research methods, explained below

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

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

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

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

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

Types of Research Methods

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

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

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

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

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

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

Qualitative Research Methods

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

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

1. Ethnographic Research

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

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

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

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

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

Example of Ethnography

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

2. Phenomenological Research

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

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

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

Example of Phenomenological Research

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

3. Historical Research

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

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

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

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

Example of Historical Research

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

4. Content Analysis

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

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

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

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

Example of Content Analysis

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

5. Grounded Theory Research

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

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

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

Grounded Theory Example

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

6. Action Research

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

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

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

Action Research Example

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

7. Natural Observational Research

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

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

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

Observational Research Example

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

8. Case Study Research

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

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

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

See More: Case Study Advantages and Disadvantages

Example of a Case Study

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

Quantitative Research Methods

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

9. Experimental Research

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

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

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

Example of Experimental Research

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

10. Surveys and Questionnaires

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

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

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

Example of a Survey Study

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

11. Longitudinal Studies

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

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

a visual representation of a longitudinal study demonstrating that data is collected over time on one sample so researchers can examine how variables change over time

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

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

Example of a Longitudinal Study

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

12. Cross-Sectional Studies

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

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

A visual representation of a cross-sectional group of people, demonstrating that the data is collected at a single point in time and you can compare groups within the sample

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

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

Example of a Cross-Sectional Study

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

13. Correlational Research

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

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

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

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

Example of Correlational Research

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

14. Quasi-Experimental Design Research

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

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

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

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

Example of Quasi-Experimental Design

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

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

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

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

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

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

Example of a Meta-Analysis

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

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

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

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

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

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

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

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

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

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

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Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

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

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

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

Quantitative Research Method

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

Mixed Method Research

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

Key Differences Between Research Methods

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

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

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

Quantitative Research Example:

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

Mixed Research Example:

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

Applications of Research Methods

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

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

Purpose of Research Methods

Research methods serve several purposes, including:

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

When to Use Research Methods

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

Here are some situations when research methods may be appropriate:

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

Advantages of Research Methods

Research methods provide several advantages, including:

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

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Researcher, Academic Writer, Web developer

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Social/Behavioral Sciences Research Guide

Types of Research Methodology

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

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

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

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

Empirical Study

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

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

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

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

Quantitative vs Qualitative Studies

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 5 different types of research methodology for 2024.

5 Different Types of Research Methodology for 2024

Research Methodology refers to the systematic process used to conduct and analyze research. It involves a set of procedures and techniques employed to gather, organize, and interpret data. Various types of research methodology , such as qualitative and quantitative methods, form the foundation for investigating and understanding diverse phenomena.

Diverse research methodology provide a spectrum of advantages in scientific exploration. Qualitative methodologies, such as interviews and observations, delve deep into understanding human behavior and motivations. Quantitative approaches, like surveys and experiments, offer precise numerical data for statistical analysis. Mixed-methods enable a comprehensive view by merging qualitative and quantitative strengths. Experimental methods establish cause-and-effect relationships, while case studies offer in-depth insights into specific instances. Each methodology caters to different research needs, fostering a nuanced understanding of complex phenomena and contributing to the richness and depth of scholarly inquiry.

This blog is your guide to the Top 5 types of research methods that haven’t been fully tapped into yet. We’re talking about different ways to do research, the kind that hasn’t been widely used or discovered. It’s crucial to stay on top of these categories of research methodology because as the world moves forward, so does the way we study and understand things. So, we’ll be checking out the latest and coolest Research methodology types , from new technologies to fresh ways of combining different fields of study.

Different types of methodology in research

Research methodology encompasses a variety of approaches and techniques to gather and analyze data. Here are some Different types of methodology in research:

Qualitative Methodology:

In-depth exploration of attitudes, behaviors, and motivations.

Utilizes methods like interviews, focus groups, and content analysis.

Quantitative Methodology:

Focuses on numerical data and statistical analysis.

Involves surveys, experiments, and structured observations.

Mixed-Methods Approach:

Integrates both qualitative and quantitative methods.

Offers a thorough comprehension of the research problem.

Experimental Research:

Investigates cause-and-effect relationships.

Involves controlled experiments with manipulated variables.

Survey Research:

Gathers data from a selected group through structured questionnaires.

Examines trends, attitudes, and opinions.

Case Study Methodology:

In-depth analysis of a specific instance or case.

Offers detailed insights into complex phenomena.

Action Research:

Involves collaboration between researchers and practitioners.

Aims to solve real-world problems through iterative cycles of planning, acting, observing, and reflecting.

Ethnographic Research:

Immersive study of a specific group or culture.

Requires prolonged engagement and participant observation.

Methodology 1: Neurobiological Methodology

Neurobiological Methodology stands at the forefront of methodology in research paper, bridging the realms of neuroscience and traditional research methodologies. This is one of the Research methodology types which aims to unravel the intricacies of human cognition and behavior by integrating cutting-edge brain imaging techniques with established research methods.

Key Components:

Neuroimaging Technologies: Utilizes advanced technologies such as fMRI (functional Magnetic Resonance Imaging) and EEG (Electroencephalography) for all the research methodologies including exploratory research in research methodology. Enables real-time monitoring of brain activity, offering insights into cognitive processes during various tasks.

Biometric Data Integration: Incorporates biometric data, including heart rate variability and skin conductance, to supplement neurobiological findings. Provides a comprehensive understanding of emotional responses and physiological changes related to cognitive activities.

Experimental Designs with Neural Correlates: Designs experiments that correlate specific neural activities with behavioral responses. Allows researchers to identify neural markers associated with decision-making, memory, and learning.

Cross-Disciplinary Collaboration: Encourages collaboration between neuroscientists and researchers from diverse fields. Integrates expertise from psychology, sociology, and other disciplines to ensure a holistic approach.

Applications: Neurobiological Methodology, which is a descriptive methodology in research, holds immense potential across various research domains:

In Psychology: Unraveling the neural basis of psychological disorders, emotions, and cognitive functions.

In Marketing: Understanding consumer behavior by examining the neural responses to advertisements and product choices.

In Education: Enhancing learning methodologies by identifying neural patterns associated with effective teaching strategies.

Challenges and Future Directions: Despite its promises, Neurobiological Methodology faces challenges such as data complexity and ethical considerations. Future research should focus on refining methodologies, establishing ethical guidelines, and fostering interdisciplinary collaboration to unlock the full potential of this unexplored approach. Neurobiological Methodology emerges as a groundbreaking frontier, offering a novel lens through which researchers can delve into the intricacies of human cognition and behavior. As one of the different types of methodology in research, it holds the potential to reshape our understanding of the mind and pave the way for innovative solutions across diverse fields.

Methodology 2: Augmented Reality (AR) Research Methodology

Augmented Reality (AR) Research Methodology marks an unexplored frontier, intertwining cutting-edge AR technologies with traditional research methods. This is one of the types of methodology in research which seeks to create immersive environments for data collection, offering a unique perspective on human behavior and decision-making.

Constructs simulated environments using AR technology to observe and analyze real-time human behavior.

Enables researchers to study reactions and interactions in controlled yet dynamic settings.

Integrates AR-generated data collection points within physical spaces.

Facilitates the gathering of diverse data sets by embedding virtual elements in real-world contexts.

Utilizes AR interfaces to track user interactions and responses.

Enhances the understanding of user engagement and decision-making processes within augmented scenarios.

Combines AR experiences with traditional research methods such as exploratory research in research methodology for a comprehensive approach.

Allows researchers to triangulate findings by comparing results obtained from both virtual and non-virtual settings.

Applications: AR Research Methodology, which is also a descriptive methodology in research, holds promise across various research domains:

Simulating scenarios to observe human responses to environmental changes in descriptive qualitative research methodology.

Analyzing consumer behavior within augmented retail environments for product placement and advertising strategies.

Creating interactive learning experiences to study the impact of AR on knowledge retention.

Challenges and Future Directions: Challenges such as technological constraints and the need for standardized protocols highlight the evolving nature of this Research methodology types. Future endeavors should focus on refining AR applications, establishing ethical guidelines, and exploring collaborative opportunities with AR developers. Augmented Reality Research Methodology stands as an exciting avenue among the types of methodology in research, offering a transformative approach to understanding human behavior within virtual and augmented spaces. As technology continues to advance, this methodology holds the potential to redefine the landscape of research methodologies across diverse disciplines.

Methodology 3: Predictive Analytics in Social Sciences

Predictive Analytics in Social Sciences emerges as a groundbreaking methodology in research papers, introducing advanced statistical models and machine learning algorithms to forecast social trends and behaviors. This type of exploratory research methodologies harnesses the power of predictive analytics to offer a new dimension to traditional categories of research methodology.

Advanced Statistical Models:

Applies sophisticated statistical models, including regression analysis and time-series forecasting.

Enables researchers to identify patterns and relationships within social data.

Machine Learning Algorithms:

Integrates machine learning algorithms to predict future outcomes based on historical data.

Provides a dynamic and adaptive approach to understanding social phenomena in descriptive qualitative research methodology.

Big Data Utilization:

Harnesses large datasets from diverse sources, including social media, surveys, and public records.

Facilitates the identification of trends and correlations within complex social systems.

Real-Time Analysis:

Conducts real-time analysis of social data to generate instant predictions.

Allows for timely interventions and policy adjustments based on emerging social patterns.

Applications: Predictive Analytics in Social Sciences holds immense potential across various applications:

In Sociology: Forecasting demographic shifts, social movements, and cultural trends.

In Public Policy: Informing policy decisions by predicting the potential impact of interventions.

In Market Research: Anticipating consumer behavior and market trends for strategic planning.

Challenges and Future Directions: Despite its promises, integrating predictive analytics into social sciences faces challenges such as data privacy concerns and model interpretability, which is a type of exploratory research methodologies. Future research should focus on refining models, addressing ethical considerations, and enhancing the transparency of predictive analytics methodologies.

Predictive Analytics in Social Sciences stands as a dynamic methodology, extending beyond basic research methodology to offer foresight into the complex dynamics of human societies. As we embrace the era of big data, this approach holds the potential to revolutionize how we understand and respond to social changes in real time.

Methodology 4: Quantum Research Methodology

Quantum Research Methodology represents a paradigm shift, bridging the world of quantum physics with a basic research methodology. This unexplored approach challenges the traditional classification of research methodology by harnessing the principles of quantum mechanics for data analysis.

Quantum Computing for Data Processing: Utilizes quantum computing’s parallel processing capabilities for handling complex datasets. Offers a quantum leap in computational efficiency, enabling the analysis of vast amounts of information.

Quantum Entanglement in Data Relationships: Applies the concept of quantum entanglement to identify interconnected relationships within datasets. Provides a unique perspective on the interdependence of variables in comprehensive research methodology.

Superposition for Multifaceted Analysis: Exploits quantum superposition to analyze data simultaneously from multiple perspectives. Enhances researchers’ ability to examine complex phenomena from various angles.

Quantum Algorithms for Pattern Recognition: Develops quantum algorithms for advanced pattern recognition within datasets. Enables the identification of subtle patterns that may go unnoticed with classical algorithms.

Applications: Quantum Research Methodology holds potential classification of research methodology across diverse fields:

Exploring quantum phenomena and complex physical systems with enhanced computational power.

Analyzing intricate biological datasets to uncover hidden relationships and patterns.

Enhancing predictive modeling and risk analysis with quantum algorithms.

Challenges and Future Directions: The integration of quantum principles into research methodologies presents challenges such as the need for quantum expertise and the development of quantum-safe data encryption. Future research should focus on refining quantum algorithms, expanding interdisciplinary collaborations, and addressing ethical considerations. Quantum Research Methodology offers a novel and comprehensive approach that transcends traditional classifications of research methodology. As quantum technologies continue to advance, this unexplored frontier holds the promise of revolutionizing how we conduct research, analyze data, and gain insights into the underlying structures of complex systems.

Methodology 5: Bibliometric Research Methodology

Bibliometric research methodology is a quantitative approach that analyzes patterns and trends within academic literature, utilizing bibliographic data to gain insights into the scholarly landscape in the comprehensive research methodology.

Citation Analysis:

Examines the frequency and impact of citations to understand the influence of a publication.

Identifies seminal works and measures the academic impact of research.

Co-authorship Networks:

Maps collaborations among researchers through analysis of co-authored publications.

Unveils research communities and the dynamics of collaborative efforts.

Journal Impact Factors:

Evaluates the prestige and impact of academic journals based on citation patterns.

Assists researchers in identifying reputable outlets for publication.

Keyword Co-occurrence:

Identifies prevalent themes and topics within a field by analyzing the co-occurrence of keywords.

Facilitates trend analysis and the identification of emerging research areas.

Applications:

Research Evaluation:

Assessing the impact and productivity of researchers, institutions, or journals.

Informing funding agencies and policymakers in decision-making processes.

Trend Analysis:

Identifying emerging topics and research directions within a discipline.

Assisting researchers in staying abreast of the latest developments.

Collaboration Mapping:

Facilitating the identification of potential collaborators and research networks.

Enhancing interdisciplinary research initiatives.

Challenges:

Data Quality and Availability:

Limited availability and consistency of bibliographic data.

Challenges in obtaining accurate and comprehensive citation information.

Discipline-specific Differences:

Variability in citation practices across disciplines.

Difficulty in creating standardized metrics applicable to all fields.

Self-citation Bias:

Influence of self-citations on impact metrics.

Requires careful consideration to avoid skewing results.

Future Directions:

Integration with Altmetrics:

Incorporating alternative metrics like social media mentions to provide a more comprehensive assessment of research impact.

Open Science Initiatives:

Embracing open access principles to enhance data sharing and transparency.

Facilitating broader collaboration and increasing the accessibility of research outputs.

Machine Learning Applications:

Implementing machine learning algorithms for more sophisticated analysis.

Enhancing the automation of bibliometric processes and improving accuracy.

Final Thoughts

In wrapping up our exploration of the top 5 unexplored types of research methodology for 2024, it’s like we’ve discovered a treasure chest of new ideas. These methods are like a breath of fresh air in the world of research. From understanding how our brains work to creating virtual worlds with Augmented Reality, and predicting social trends, we’re on the brink of something big.

Quantum research and Blockchain verification bring a touch of magic, making our data analysis smarter and more secure. These aren’t just fancy trends; they’re like keys to unlock a whole new era of learning. So, in 2024, researchers, buckle up and dive into these research methodology – the journey promises to be full of surprises, discoveries, and a whole lot of new knowledge!

Educba is a website that provides researchers with a comprehensive guide to different types of research methodologies. The website offers a wide range of courses and tutorials on research methodology, which can help researchers develop their research skills and knowledge. By taking these courses, researchers can learn about different research methods and techniques, such as surveys, case studies, and experiments.

This knowledge can help researchers design and conduct their research more effectively and efficiently. Additionally, the website provides a platform for researchers to connect with other researchers and experts in their field. This can help researchers build their professional network and find new opportunities for research and collaboration. Overall, educba.com is a valuable resource for researchers who are looking to develop their research skills and knowledge and build their professional network.

Frequently Asked Questions

What is the research methodology?

Research methodology is the systematic process used to conduct and analyze research.

What is literature review in research methodology?

Literature review in research methodology involves reviewing and analyzing existing literature on a specific topic.

What is qualitative research methodology?

Qualitative research methodology involves exploring and understanding complex phenomena through non-numerical data.

What are qualitative methodologies?

Qualitative methodologies encompass various approaches like interviews, focus groups, and content analysis.

What are research methodology types?

Research methodology types include qualitative, quantitative, mixed methods, experimental, and survey research.

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Research Methodologies

What are research designs?
What are research methodologies?

What are research methods?

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

Additional Sources

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

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

Constructing Questionnaires

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

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

Quantitative Research Measures

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

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

Focus Groups

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

Advantages of Focus Groups

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

Disadvantages of Focus Groups

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

Observation

There are two ways to conduct research observations:

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

Open-Ended Questionnaires

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

Semi-structured Interviews

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

Theoretical Analysis

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

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

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

Unstructured Interviews

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

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

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

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

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

A tutorial on methodological studies: the what, when, how and why

Lawrence mbuagbaw.

1 Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON Canada

2 Biostatistics Unit/FSORC, 50 Charlton Avenue East, St Joseph’s Healthcare—Hamilton, 3rd Floor Martha Wing, Room H321, Hamilton, Ontario L8N 4A6 Canada

3 Centre for the Development of Best Practices in Health, Yaoundé, Cameroon

Daeria O. Lawson

Livia puljak.

4 Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000 Zagreb, Croatia

David B. Allison

5 Department of Epidemiology and Biostatistics, School of Public Health – Bloomington, Indiana University, Bloomington, IN 47405 USA

Lehana Thabane

6 Departments of Paediatrics and Anaesthesia, McMaster University, Hamilton, ON Canada

7 Centre for Evaluation of Medicine, St. Joseph’s Healthcare-Hamilton, Hamilton, ON Canada

8 Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON Canada

Associated Data

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 – 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 – 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig. 1 .

An external file that holds a picture, illustration, etc.
Object name is 12874_2020_1107_Fig1_HTML.jpg

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 – 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

Comparing two groups
Determining a proportion, mean or another quantifier
Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.
Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].
Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]
Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 – 67 ].
Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].
Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].
Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].
Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

What is the aim?

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

2. What is the design?

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

3. What is the sampling strategy?

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n = 103) [ 30 ].

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

4. What is the unit of analysis?

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig. 2 .

An external file that holds a picture, illustration, etc.
Object name is 12874_2020_1107_Fig2_HTML.jpg

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Acknowledgements

Abbreviations, authors’ contributions.

LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.

This work did not receive any dedicated funding.

Availability of data and materials

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

DOL, DBA, LM, LP and LT are involved in the development of a reporting guideline for methodological studies.

Publisher’s Note

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

Types of Research Methods

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

Types of Research Methods According to Nature of the Study

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

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

Types of Research Methods According to the Purpose of the Study

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

Table 1 Differences between applied and fundamental research

Types of Research Methods according methods of data collection

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

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

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

Types of Research Methods according to the type of data

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

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

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

Types of Research Methods according to Research Design

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

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

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

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

John Dudovskiy

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

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

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

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

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

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

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

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

UNC Libraries
Collections
Creative Music Research in Special Collections
Creative Music Research Examples and Methodologies

Creative Music Research in Special Collections: Creative Music Research Examples and Methodologies

Archives and Libraries
Using a Finding Aid
Registering & Requesting Materials
Primary Source Analysis
Music Copyright
Creative Research Opportunities

Types of Projects

Here are a few possible project directions for using archives and primary sources. This is not an exhaustive list – the possibilities are endless!

Conceptual inspiration

Is there a unique item or story that you want to expand upon? Perhaps there is a diary entry, a letter or an oral history that speaks to you.

Understanding Repertoire and Playing Styles

Primary sources offer unique insight into historical repertoire and playing styles. This could come in the form of a sound recording or a score. How does the playing style and/or repertoire differ from that of contemporary players?

Improvisation and Composition

Any type of primary source can serve as an inspiration for improvisation or composition. It could be a recording, a photograph, a silent film – what ways can different medias inspire improvisation and composition?

Sampling and Production

What public domain recordings are available in the archive? How can sampling an oral history or a music recording add to the production?

Program and Album notes

Primary sources can also be helpful when writing program or album notes. What historical perspectives or reflections of artists or communities can be represented in program and album notes?

Installations and Exhibits

Multi-media installations can be a compelling way to combine primary source media with other creative content.

Creative Research Methodologies

A Guide to archives for artists and makers from Providence Public Library A guide to using archives for artists and makers in the form of a graphic novel. Created by artist and librarian Jeremy Ferris.
A Guide to archives for artists and makers Downloadable PDF

<< Previous: Creative Research Opportunities
Last Updated: Apr 15, 2024 10:30 AM
URL: https://guides.lib.unc.edu/musicresearch

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Transformations That Work

Michael Mankins
Patrick Litre

More than a third of large organizations have some type of transformation program underway at any given time, and many launch one major change initiative after another. Though they kick off with a lot of fanfare, most of these efforts fail to deliver. Only 12% produce lasting results, and that figure hasn’t budged in the past two decades, despite everything we’ve learned over the years about how to lead change.

Clearly, businesses need a new model for transformation. In this article the authors present one based on research with dozens of leading companies that have defied the odds, such as Ford, Dell, Amgen, T-Mobile, Adobe, and Virgin Australia. The successful programs, the authors found, employed six critical practices: treating transformation as a continuous process; building it into the company’s operating rhythm; explicitly managing organizational energy; using aspirations, not benchmarks, to set goals; driving change from the middle of the organization out; and tapping significant external capital to fund the effort from the start.

Lessons from companies that are defying the odds

Idea in Brief

The problem.

Although companies frequently engage in transformation initiatives, few are actually transformative. Research indicates that only 12% of major change programs produce lasting results.

Why It Happens

Leaders are increasingly content with incremental improvements. As a result, they experience fewer outright failures but equally fewer real transformations.

The Solution

To deliver, change programs must treat transformation as a continuous process, build it into the company’s operating rhythm, explicitly manage organizational energy, state aspirations rather than set targets, drive change from the middle out, and be funded by serious capital investments.

Nearly every major corporation has embarked on some sort of transformation in recent years. By our estimates, at any given time more than a third of large organizations have a transformation program underway. When asked, roughly 50% of CEOs we’ve interviewed report that their company has undertaken two or more major change efforts within the past five years, with nearly 20% reporting three or more.

Michael Mankins is a leader in Bain’s Organization and Strategy practices and is a partner based in Austin, Texas. He is a coauthor of Time, Talent, Energy: Overcome Organizational Drag and Unleash Your Team’s Productive Power (Harvard Business Review Press, 2017).
PL Patrick Litre leads Bain’s Global Transformation and Change practice and is a partner based in Atlanta.

Partner Center

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

Published on 17.4.2024 in Vol 26 (2024)

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach

Authors of this article:

Original Paper

Weicheng Qian 1 , PhD ;
Aranock Cooke 1 ;
Kevin Gordon Stanley 1 , PhD ;
Nathaniel David Osgood 1, 2, 3 , PhD

1 Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada

2 Department of Community Health & Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada

3 Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada

Corresponding Author:

Weicheng Qian, PhD

Department of Computer Science

University of Saskatchewan

110 Science Place

Saskatoon, SK, S7N 5C9

Phone: 1 3069661947

Email: [email protected]

Background: Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data.

Objective: We aim to compare GPS colocated versus Bluetooth beacon–derived proximity contact data for their impacts on transmission models’ results under community and types of diseases.

Methods: We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence.

Results: Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant ( P <.001).

Conclusions: We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

Introduction

Sensing modality of colocation for disease transmission models.

Infectious diseases have imposed a heavy burden on the global population throughout human history [ 1 , 2 ]. The COVID-19 pandemic has brought the threat of contagious diseases into sharp focus. With 6.95 million deaths; 769 million confirmed cases globally as of August 8, 2023 [ 3 ]; and an estimated >US $16 trillion in lost economic activity [ 4 ] for the United States alone, the COVID-19 pandemic has been one of the defining global crises of the 21st century [ 5 ].

Epidemiological models date back over a century [ 6 - 8 ] but have become more useful through leveraging sophisticated algorithms [ 9 , 10 ] and increasing computing power [ 11 , 12 ]. Epidemiological models to predict, plan, and respond to pandemics and outbreaks can inform decision-making to mitigate the impact of infectious diseases [ 6 - 8 ]. Epidemiological models require well-grounded physical and behavioral factors to provide reasonable estimates of disease spread [ 13 ]. Linking a population’s spatial behavior to infectious events that aids or inhibits the spread of disease is particularly difficult.

For airborne contagious diseases such as measles [ 14 ] or COVID-19 [ 5 ], a key enabler for disease spread is colocation (being in the same location at the same time). The effective spatial volume for COVID-19 is determined by aerosol dynamics and is often approximated at 2 m (6.56 ft) [ 15 ]. Measuring colocation can be conducted by self-reporting, as is commonly used in classic contact tracing and direct observation and counts [ 16 ], or by electronic means [ 17 - 19 ].

A total of 2 primary modalities for determining colocation using electronic devices exist: measurements based on estimating the distance from one person to another directly (beaconing) and measurements based on estimating the location of each person of interest within a coordinate system and calculating distances (localizing). Devices can be bespoke, such as the sociometric badge [ 20 - 22 ], or can leverage existing technologies such as Bluetooth(BT)–enabled phones, beacons, or dongles [ 23 - 26 ]. Localization techniques use systems such as GPS to place every user at a specific location at a specific time [ 27 , 28 ] and can be piggybacked on existing smartphones or mined from some social media platforms [ 29 - 31 ].

To estimate the probabilities of disease transmission, the total number of interactions, dwell times, and spatial proximity must be measured to properly baseline the parameter estimates. Techniques from companies such as Ethica Data (Avicenna Research) [ 32 ] and other companies made possible by a Google-Apple partnership [ 33 ] can be used to obtain these data for target populations under transparent and ethical data acquisition practices. However, the underlying physical processes and mathematical treatment of beaconing and localization data are substantially different and have different failure modes. Previous research had not elucidated the disparities in the estimated contact patterns for the same population between techniques. It is simple to hypothesize that colocating and beaconing will yield different contact patterns, but it is less apparent how the differences will interact with disease dynamics and impact the overall simulation outcomes.

In this study, we examine the contact patterns derived from 3 previously collected data sets using both BT beaconing and GPS localization on smartphones running the Ethica Data app. We demonstrated that while the underlying contact patterns generated from colocating and beaconing are broadly similar, they contain salient differences. For each of the 4 pathogens marked by different dynamics, we compared the results of an agent-based simulation of a communicable disease outbreak for the pathogen parameterized with beaconing- and localization-derived contact patterns. The results demonstrated that the method used to estimate contact patterns can result in significant differences between estimates of the key outbreak parameters. We showed that GPS-based contact patterns estimate significantly fewer and less-severe outbreaks than BT-derived contact patterns for the same participant and device.

Literature Review

Transmission models for communicable diseases are based on the characterization of natural history of a condition and contact networks [ 13 ]. In addition to traditional population-based nonspatial approaches, agent-based transmission models can use individual-level contact records and behaviors to identify emergent patterns using a bottom-up approach [ 9 ].

Real-world proximity tracking has applications in contact tracing, location-based risk assessment, mobility tracking, and outbreak detection [ 34 ]. Deriving real-world proximity contact mainly falls into 2 categories: calculating the delta of measured absolute positions—with, for example, GPS-assisted and Wi-Fi network–assisted locationing [ 35 , 36 ]—and directly measuring the relative distance with, for example, BT [ 19 , 37 ] or radio frequency identification [ 38 ].

Exemplars of each of these 2 approaches—GPS and BT—have been studied for digital contact tracing and transmission simulation [ 15 , 34 ]. Recent comparisons between GPS- and BT-inferred proximity contact collection approaches focus on privacy preservation, adoption, and compliance rates [ 15 ]. In contrast, the accuracy of simulations with GPS- and BT-derived proximity contacts is yet to be quantified across different underlying populations and pathogens [ 37 ].

Advances in digital contact tracing have also contributed to disease parameter estimation. For example, at the beginning of the COVID-19 pandemic, researchers focused on estimating the basic reproduction number R 0 from limited and highly regionally dependent infection data. As the pandemic spread, data collection and reporting standards enabled the daily reporting of incident cases, active cases, and mortality for various geographic scales over time, allowing the estimation of the effective reproduction number R e [ 39 - 42 ].

BT Proximity

BT is a short-range communications protocol incorporated into most smartphones and is commonly used to pair with devices such as wireless headsets. By default, BT is configured to be in a quiescent state, not advertising its presence and only communicating with devices that have been paired. Before 2020, it was possible to lock an Android phone into a more active discovery mode, where a device would beacon approximately every 8 seconds, advertising its presence to other devices. While this functionality was intended to provide ease of initial device pairing, it could be repurposed to detect the proximity of 2 devices by registering when 1 device receives a discovery ping from another.

Studies [ 43 , 44 ] have investigated the use of BT to estimate the spatial proximity between devices representing people. The simplest methodology will be to create a proximity event between 2 devices if 1 device detects a discovery ping from the other or vice versa. The distance between the devices is a relevant parameter for determining a valid proximity event or contact in many applications. Researchers have typically used the Received Signal Strength Indicator (RSSI) as a proxy for distance [ 44 - 46 ], assuming an exponential falloff of signal strength with distance [ 47 , 48 ]. This approximation is confounded by reflections or transmissions off or through objects, meaning that RSSI cannot be strictly interpreted as distance, except in all but the most controlled conditions. RSSI values can plausibly be used to filter out contacts that are either far away or on the other side of a barrier, such as a wall.

The RSSI measures signal strength in decibel-milliwatts (dBm), where RSSI=0 is defined by a “Golden Receiver Power Range,” whose lower threshold level corresponds to a received power between −56 dBm and 6 dB above the actual sensitivity of the receiver, and whose upper threshold level is 20 dB above the lower threshold level to an accuracy of 6 dB. Beyond the lower and upper threshold, any positive RSSI value indicates how many dB the RSSI is above the upper limit, and any negative value indicates how many dB the RSSI is below the lower limit. Usually, a stronger signal strength (higher RSSI) indicates closer distances between 2 BT devices; however, orientation, barriers, and interference can attenuate the signal strength beyond what the distance would suggest [ 49 ]. Young [ 50 ] and the Android Beacon Library [ 51 ] contributed an RSSI to the distance function based on the Nexus 4 and Apple’s iBeacon performance, which is often used as a first approximation for similar location awareness services on modern smartphones:

Where RSSI 0 is the RSSI value at a 1-m (3.3 ft) distance.

GPS and Location Proximity

GPS receivers are standard on smartphones, enabling location-based services and route finding. Consumer-grade GPS receivers typically have a nominal accuracy of 10 m (32.8 ft) but can be subject to substantially larger errors due to environmental factors. Neither iOS nor Android uses pure GPS localization in their location estimation services. Both additionally use initial estimates from cell tower locations (assisted GPS) as well as fingerprinting-based localization using databases of detected Wi-Fi routers. As GPS receivers often take several seconds to obtain a position lock, even with assisted GPS, smartphone localization services tend to default to Wi-Fi–based localization initially and then switch to GPS as better location estimates become available. For simplicity of presentation, the term GPS refers to location estimation in this paper, regardless of whether it was obtained through GPS, assisted GPS, Wi-Fi fingerprinting, or some combination thereof.

Given location records, a dichotomous notion of proximity can be defined, in which 2 agents are considered proximate if they are in the same place at the same time. The precision and accuracy of the measurements and the context of the definition of proximity determine how close, in time and space, agents must be to be considered proximate or in contact. When using commodity smartphone localization hardware and services, accuracy below 5 m (16.4 ft) is rare [ 28 ], so spatial proximity has a strong lower resolution limit. Temporal resolution is substantially better—on the order of seconds—and is more likely to be limited by the measurement regime or application requirements. Elevation estimates are even less reliable than spatial estimates; therefore, commodity GPS receivers are often projected onto a 2D plane, introducing the potential for erroneous connections between people at the same location but on different floors of a building, for example.

While both GPS and BT can provide higher fidelity estimates of proximity and contact than traditional surveys or diaries, both are prone to false positives and negatives. Given 2 devices separated by a mutually proximate wall, ceiling, or floor, BT can still report contacts because the attenuation of RSSI will be such that they appear in contact but farther away. GPS is prone to false positives for detecting the proximity of communicable pathogens because the distance over which transmission can occur is smaller than the accuracy threshold for commodity devices. GPS proximity can only be interpreted as close enough that contact was possible, given the error in measurement, and not that contact actually occurred. BT can produce false negatives if the beaconing and listening cycles of the devices are misaligned, such that 1 device is beaconing while the other is asleep. GPS can lose signal or accuracy when indoors, causing false negative contacts by either having no location reported for an agent or exhibiting position inaccuracies that render inaccurate colocation calculations. While the underlying true contact dynamics for the same devices are identical, the differing failure modes of GPS and BT mean that data drawn from those data collection modalities may generate different contact networks, thereby suggesting different contact dynamics and ultimately different outbreak dynamics.

Agent-Based Susceptible-Exposed-Infectious-Removed Models

The susceptible-exposed-infectious-removed (SEIR) disease state model is a classic model used to characterize pathogen transmission and the natural history of infection across a range of communicable diseases. Disease state transitions are unidirectional in the order of susceptible, exposed, infectious, and removed. The initial state of the model specifies the amount of population in each disease state, and the rate of transition between disease states is subject to both disease-characteristic parameters (such as latent period and infectious period) and the contact network (such as preferential mixing and average contact rate). It is common for a specific disease, given surveillance data, to obtain more detailed models. For example, there are models of COVID-19 splitting the SEIR states into more states and rerouting transitions in states [ 52 , 53 ]. As our goal was to probe the impact of contact measurement modality, in accordance with the Occam's razor [ 54 ], which recommends a parsimonious model with the fewest assumptions that are necessary, we chose the simplest SEIR model.

Agent-based models (ABMs) incorporate individual interactions and track the state and state transitions through which each individual progresses. Unlike a stock and flow model, which uses differential equations to model the flow of individuals from one state to another in aggregate, an ABM knows the state of every agent individually at any time step of the simulation, and aggregate statistics, for example, on infections, are queried and computed during postprocessing. An agent-based SEIR model captures both individual disease state transitions based on disease-specific parameters such as the latent period, the infectious period, R 0 , as well as some abstraction of the contact behavior of the population. As the simulation of an infectious disease can capture emerging patterns in a bottom-up manner [ 9 ] and more faithfully reflect dynamics due to the proximity contact network than compartmental models, ABMs provide higher fidelity at the cost of computation when compared to stock and flow models. As an ABM can directly use a contact pattern as part of the simulation, it is the logical choice for examining the sensitivity of simulations to the contact detection methodology.

Data Set Description

For this study, we used 3 previously collected data sets, all of which were collected from the city of Saskatoon, a city in the midwestern Canadian province of Saskatchewan. In all these data sets, additional sensor modalities (eg, accelerometer, gyroscope, and Wi-Fi traces) were also collected, but only the BT traces, GPS traces, and battery data were used in this study. Battery data were used to identify gaps in data collection. If the phone is on and Ethica is running, then battery data were recorded, providing a more reliable way to assess the continuity of data collection than is possible with GPS, where signals can be obscured by the built environment but where the phone is still actively recording. The Saskatchewan Human Ethology Datasets (SHEDs) are a collection of pilot projects and technical trials taking place during the iEpi project—the academic precursor for the Ethica Data system—and associated postprocessing and methodological outcomes [ 55 , 56 ]. The SHEDs were exclusively collected from populations at the University of Saskatchewan in Saskatoon. The SHED7 data set was collected between July 11 and August 8, 2016, and included 61 students. The SHED8 data set was collected between September 25 and October 25, 2016, and included 74 students. The SHED9 data set was collected between October 28 and December 9, 2016, and included 88 students. These participants were part of a social science student study pool that included both undergraduate and graduate students and was weighted toward undergraduates.

Ethical Considerations

Data collection and analysis were conducted under written approval (BEH-14-203) from the University of Saskatchewan Human Behavioral Ethics Review Board. All data were collected with the informed consent of the participants and under the oversight of the University of Saskatchewan Human Behavioral Ethics Review Board.

No experimental manipulations were conducted during data collection. The studies did not undertake stratified sampling according to ethnicity, grade, or gender. The study did not proscribe participation by those connected with the department or research laboratories involved, and the study team informed colleagues in laboratories and the Department of Computer Science first. The awareness of potential study involvement can be assumed to have spread across social networks. For each study, participants joined using their own phones to install the Ethica app and consented to have sensor data collected over the study period. Although for all these 3 studies, both Android and iPhone users were welcome, because BT beaconing did not work reliably on an iPhone due to security settings, iPhone users were removed from the analysis, and all participants reported here are Android users. Each participant receives approximately a CAD $50 (US $38.6) honorarium at the end of a study, and the exact amount varies by study length.

All 3 SHED studies stored sensor data anonymously, and the sensor data associated with a participant were identified by a device-identity number. Despite measures such as encryptions being used to protect sensor data, high-velocity GPS data can allow a skilled practitioner to determine salient information about participants, such as place of work, residence, and daily habits. We circumvented this issue by committing to our own research ethics board and the participants that researchers who access the data must commit to writing requests subject to the review and approval of our ethics boards.

Sensor Data Processing

To evaluate the performance of each sensor in real-world scenarios, we needed to account for the impact of participant compliance. We defined the active period of a study with the start day as the first day when we have ≥80% of the participants’ battery reading and the end day as the first day with all following days having <80% of the participants’ battery reading. We retained participants who had at least 50% of the daily battery data. The descriptive statistics are presented in Table 1 .

Ethica’s multisensor sensing requests that the Android operating system perform sensor scanning and reading periodically. We call our requested period length, that is, each of the repeated 5-minute time windows, a duty cycle. For the location and BT contact data used in this study, Ethica records for 1 minute, starting every 5 minutes.

The BT discovery record from the Android application programming interface includes the discovered BT device’s MAC address and RSSI. After linking such discovery records to participant IDs in the smartphone BT MAC address table collected after consent and before the experiment started, we created a table of BT discovery records for eligible participants. Those RSSI values were filtered to include records associated with an RSSI stronger than the RSSI values associated with the desired distance thresholds and then aggregated maximum RSSI values for unique tuples of discovered participant and duty cycle (data collection epoch), resulting in the final BT contact record table. Although BT discovery records are directional, our use of unique tuples will consider a pair of participants potentially in contact if at least one’s BT device discovers that of the other.

Starting with raw GPS readings for each participant, we first discarded GPS readings with an accuracy radius larger than 10 m (32.8 ft) as being too inaccurate to allocate even approximate colocation estimates. For each participant, we used the median of their GPS readings within a duty cycle as the estimated geolocation of that participant. We then mapped the estimated GPS coordinates of latitude and longitude onto the Universal Transverse Mercator coordinates as the northing and easting with units of meters. For the sake of estimating interparticipant proximity, we used the Euclidean distance between the estimated geolocation for all pairs of participants within the same duty cycle as the estimated distance between pairs of participants. For each duty cycle, participants who lacked GPS readings within that duty cycle were considered as not being in contact with any of the other participants for the duration of that cycle.

Agent-Based SEIR Model

An agent-based SEIR simulation model was used to characterize pathogen transmission and describe the natural history of infection. The model assumed the following:

There is no reinfection during the simulation period.
The population is closed, and no births, deaths, and migrations occur during the simulation time horizon.
The latent periods for diseases under consideration are similar to the incubation periods.
During the infectious period, an infectious patient will have a constant hazard rate of transmission to every one of their currently contacted persons, normalizing passive shedding from active spread (eg, sneezing) over a contact period.
There are no behavior changes in participants during the simulation period, conditional on the contact patterns measured. For small outbreaks, this is reasonable, but the COVID-19 pandemic has demonstrated the importance and magnitude of changes that can occur in hygienic personal protective behavior (eg, mask use) over the course of a pandemic.

We made use of a 4-fold duplication and concatenation of both GPS- and BT-inferred proximity contact data, such as successively replaying a movie, to allow the outbreak to run its course without running out of contact data.

All participants who connected to at least 1 other participant after filtering were included in the simulation ( Table 2 ). Each simulation starts with 1 initially exposed participant. We conducted multiple simulations with different random seeds to account for stochastics. Each simulation began with a single participant with an infection of the corresponding disease. All active participants were the initially exposed participants in turn, for 50 realizations each.

During the initialization of each simulation realization, incubation period and infectious period were drawn uniformly from the minimum-maximum range of corresponding parameters, as presented in Table 3 .

All diseases listed in Table 3 are investigated from a historical perspective, where estimates of the corresponding parameters are available.

a The sensing modality of Bluetooth and GPS are combined with distance thresholds of 8 m (26.2 ft) and 20 m (65.6 ft) in rows. For example, BT8 stands for proximity contacts inferred from Bluetooth-sensed distance within the threshold of 8 m (26.2 ft).

a Derived as midpoint of reported range.

b Derived range from different reports.

Simulation Configuration

For each SHED study, after preprocessing, we obtained GPS- and BT-inferred proximity contact data with distance-equivalent RSSI thresholds of −80 dBm (corresponding to approximately 8 m or 26.2 ft) and −90 dBm (approximately 20 m or 65.6 ft). A group of simulations for each of the 4 diseases—namely, influenza, COVID-19, measles, and norovirus—was run. Within each group of simulations sharing the same derived proximity contact data and disease parameters, we iterated each of the active participants as the initially exposed patient with 50 realizations, where each realization has a different predetermined random seed, resulting in 170,400 realizations across all data sets and conditions.

In our agent-based SEIR model, at any given time during the simulation, each agent resides in one of the 4 disease states (susceptible, exposed, infectious, or removed). At the start of the simulation, all agents, except the initially exposed agent, are susceptible. The transition from susceptible to exposed has a probability p when exposed to proximity to an infectious agent. Such occurrences of exposure are characterized by a Poisson process with a mean interarrival time of 5 minutes. The value assumed for p is derived from the disease-specific R 0 and the average empirically observed frequency of population contacts. The timing, the duration, and the pair of agents involved in each proximity contact are given by the proximity contact data fed to the simulation. The transitions of exposed-to-infectious and infectious-to-removed are timeouts with timers set as the corresponding latent period and infectious period as initialized for each individual.

Simulations were run on 2 servers, each with an Intel Xeon CPU E5-2690 v2 and 503 GB memory. Models were created in AnyLogic (version 8.1.0; The AnyLogic Company) and exported to a stand-alone Java application with OpenJDK (version 1.8.0_252; The OpenJDK Community) as the runtime environment. Analysis was conducted in R software (version 4.0.2; R Foundation for Statistical Computing) with major packages, including tidyverse (version 1.3.0), ggprah (version 2.0.5), and igraph (version 1.2.6), and in Python (version 3.8.0; Python Software Foundation) with major packages, including pandas (version 1.2.0), numpy (version 1.20.2), and scipy (version 1.6.1).

Evaluate Impacts on Transmission Models

We used the attack ratio as the metric to evaluate the impact of proximate contact data on transmission models. The attack ratio is the proportion of the total population that gets infected throughout the simulation. Although the ABM-SEIR can produce many estimates for different disease parameters given proximate contact data, the attack rate and individual risk of infection was chosen for simplicity, accessibility, and to serve as single summary statistics [ 65 , 66 ].

Attack Rates

The core research question of this study was whether and to what extent the differences in GPS- and BT-based proximity detection would alter the contact network and therefore the implied attack rate. We considered the attack rate θ defined as in the following equation:

Where I ( t ) is the number of infectious persons at time t , T is the end time of the simulation instance, and N is the population size. The attack rate θ denotes the proportion of the population that is infected throughout the simulation instance. As the response variable (denoted as Θ) to the controlled variables of the disease or pathogen M , the initial infectious individual ν ∈ V = { v 1 , v 2 ,..., v n }, n = ‖ V ‖ and collected proximity contact data D( ω , ε , V ). For the proximity contact data D( ω , ε , V ), ω ∈ {BT, GPS} is the sensor type, ε ∈ {8, 20} is the distance threshold of proximate contacts, and V is the underlying population. Therefore, with the ABM-SEIR model as P(∙) for a specific disease M and underlying population V , we can sample Θ∼P(Θ= θ | ω , ε , M , V ) with simulation realizations. While the initial infectious individual ν has been known to impact the attack rate Θ, investigation of that impact lies outside the scope of this paper.

Welch t Test

Assuming disease M and an underlying population V , the choice of an initial infectious individual ν is independent of the data collection configuration (sensor type ω and proximate distance threshold ε ). We were interested in the marginal probability, defined as in the following equation:

Limited by our knowledge of P( ν | M , V ), we assumed the initial infectious individual ν is chosen with uniform probability from the underlying population V , that is, P( ν | M , V ) =1 / ‖ V ‖. Consider θ as the sample mean from a sample, X i ∼ P(Θ| ν = v i , ω , ε , M , V ), i = 1,..., ‖ V ‖, and we sampled by repeating ‖ V ‖ simulations iterating every individual of the population V as the initial infectious individual. According to the central limit theorem, samples of θ∼P(Θ| ω , ε , M , V ) tend to be normally distributed to suffice the assumption of the Welch t test (2-tailed).

Pairwise t Test

Without assuming that the initial infectious individual ν is homogeneous among the underlying population V , that is, P( ν | M , V )=1/‖ V ‖, we could construct a pairwise t test by pairing the samples of attack rate having the same initial infectious individual μ , given sensor type ω and distance threshold ε , for each pair of disease M and underlying population V . In this case, we assumed the pairwise differences in the attack rate, such as for Θ i BT8-GPS20 = Θ BT8 – Θ GPS20 , are normally distributed, where Θ BT8 ∼ P(Θ| ν = v i , ω = BT, ε = 8, M , V ) and Θ GPS20 ∼ P(Θ| ν = v i , ω = GPS, ε = 20, M , V ).

Kullback-Leibler Divergence of Individual Infection Risks

Given the sensor type, proximate distance threshold, disease, and underlying population, we estimated the individual infection risk based on the Laplacian-smoothed rate of being infected across realizations, denoted by ρ v∈V ( ω , ε , M , V ). The likelihood of being the most likely infected individual for an individual v ∈ V follows P( v | ω , ε , M , V ), which can be estimated by normalizing vector ρ ={ ρ v | v ∈ V }. The Kullback-Leibler (KL) divergence was used to summarize the differences between pairs of sensor type and proximate distance threshold ( ω , ε ) within blocks by disease and the underlying population. For disease M and the underlying population V , we have

between sensing configurations ( w 1 , e 1 ) and ( w 2 , e 2 ), where

While the agent-based simulation uses dynamic contacts, some insight can be gained by examining the aggregate contact network of participants in each study. Figure 1 shows the aggregate contact networks for SHED7, SHED8, and SHED9 using BT and GPS at 8- and 20-m thresholds. If a connection ever occurred between 2 nodes given the protocol, a corresponding edge is drawn in the network, with the color of the edge proportional to the total contact duration over the course of the experiment between those nodes. Reflecting the Pareto-like distribution of contact duration, colors move from blue (weakly connected) to red (strongly connected) on a logarithmic scale, consistent with other human network observations [ 67 , 68 ]. As expected, most nodes appear to have weak connections compared to the highly connected dyads and triads in the network. The BT networks are denser and more highly connected than their GPS counterparts, implying a greater potential for disease spread. There is a greater preponderance of weak edges in the BT data sets than in their corresponding GPS counterparts. There is a modest increase in the number of edges between the 8- and 20-m thresholds for each data set.

Contact frequency (the rate at which contacts occur) and intercontact time (the time between contacts) are common aggregate distributions used to characterize contact data sets. Similar to many other data sets, both the BT and GPS demonstrate power law decay for the probability of contact duration and intercontact time ( Figure 2 ). GPS-based contact detection tends to infer more and shorter-duration contacts but exhibits truncated tails. In SHED7 and SHED9, the tail truncation leads to fewer long-duration contacts (>600 min) than BT. The intercontact times are similar for all data sets, but the BT distributions are skewed more heavily toward longer intercontact times than in the case of GPS. In contrast, for SHED8 and SHED9, BT tracking detects notably fewer moderately long contacts (those in the range of 50 min to 600 min). This may be due to localization noise–induced false positives in the GPS data set skewing the apparent contact durations higher.

After filtering the connections for the appropriate distance threshold (8 m and 20 m or approximately 26.2 ft and 65.6 ft), the agent-based simulation was run according to the protocol described in simulation configuration. Many runs do not produce an outbreak, with the initially exogenously infected individual being the only member of the network infected. This results in a zero-heavy bimodal distribution of cumulative infection counts per realization, with a Poisson spike at 0 cumulative endogenous infections (1 exogenous infection) and a second distribution describing the probability of an outbreak of a given size conditional on outbreak occurrence (ie, the probability of at least one endogenous infection). A stacked bar plot showing the ratio of runs in which further incidences beyond the initial infectious individual did or did not occur is shown in Figure 3 . The figure clearly shows a higher likelihood of an outbreak occurring with the BT data, as expected from the aggregate network diagrams and aggregate contact duration and frequency plots. The consistent difference in the probability of outbreak occurrence between the 2 conditions is our first substantial indication that the two means of measuring contact are not equivalent. To determine the impact of each dynamic contact pattern on the outbreaks themselves, the trials in which no endogenous infection occurred were removed, and statistical analysis was conducted on the distribution of outbreak severity conditional on outbreak occurrence.

The Bonferroni-corrected Shapiro-Wilk test was passed for each of the 50 samples of attack rate, denoted by θ, for every pair of disease M and the underlying population V , except for COVID-19, with contact records collected via GPS using a distance threshold of 20 over SHED8. The results of the Bonferroni-corrected Welch t test are presented in Table 4 .

a BT: Bluetooth.

Our predetermined α level was .05.

The results of Bonferroni-corrected pairwise t tests [ 69 ] between observed attack rates (having filtered out scenarios with 0 endogenous infections) across all simulation runs for a condition are presented in Table 5 .

These results confirm our hypothesis that BT- and GPS-based contact histories induce significantly different estimates of total disease burden across multiple simulated realizations. The primary comparisons are the BT8-GPS8 and the BT20-GPS20, with the others included for completeness. For SHED7 BT8-GPS8, the results are not significant. For all other diseases and data sets, the results are statistically significantly different. In the case of BT20-GPS20, all results are significantly different, with the exception of the SHED7 measles. While we suspected that the infectiousness of the disease would impact simulated outcomes, the results seem to be dominated by differences in the data set and contact measurement modality. Looking at the impact of resolution, some combinations of data set and disease are not significantly different, but for the most part, increasing the threshold increases the number of contacts, driving differences in simulated outcomes. The exception to this general rule seems to be SHED8 GPS8-GPS20, where increasing the threshold did not significantly alter the outcomes for most diseases and only marginally for measles.

Figure 4 shows the violin plots for the attack rates over each realization across all simulated conditions and provides insight into the statistical results from Table 5 . SHED7 consistently has lower attack rates for all diseases, with a smaller variance and mean than other data sources. The limited attack rate likely drives the similarity between the BT and the GPS. The denser SHED8 and SHED9 networks have substantially larger variance, leading to significant differences between the measurement modality conditions. The highly contagious measles virus, in particular, exhibits marked differences within the SHED8 and SHED9 data sets. In general, BT contact patterns have longer tails, indicating a greater possibility of larger outbreaks throughout the population. In cases where a substantial probability mass is contained in the tail, the median is also drawn higher, as in SHED8 with BT20 for measles.

Figure 5 shows the KL divergence on individual infection risks within blocks of disease and the underlying population. The individual infection risks are reflected by the likelihood of being the most likely infected individual between different sensing configurations, where a sensing configuration is a pair of selected sensor types and proximate distance thresholds. The distance threshold of proximate contact does not appear to impact GPS-collocated inferred proximity contacts in terms of individual infection risks, regardless of the underlying population. This invariance to distance thresholds suggests that the primary bottleneck lies in the GPS-colocation method’s inability to identify exact proximity contacts among a group of collocated individuals. Meanwhile, the BT-beaconing method may capture proximity contacts at certain distance thresholds (such as for SHED7 and SHED8), which can be important when considering droplet-based pathogen transmission. There seems to be a lower magnitude of KL divergence for BT8-BT20 and BT20-BT8. The KL divergence among pairs of different sensor types is similar regardless of the distance thresholds of proximate contact, suggesting that BT beaconing and GPS colocating collect different proximity contacts regardless of the resolution of the distance thresholds of proximate contacts. The magnitude of asymmetric | D KL ( p ‖ q ) – D KL ( q ‖ p )| shown in red lines is lower than either D KL ( p ‖ q ) or D KL ( q ‖ p ), indicating that the asymmetry of the KL divergence is not impairing our aforementioned analyses.

Disparate Results From GPS- and BT-Based Contact Tracking

Our results clearly indicate that GPS- and BT-based contact tracking yield disparate results for the same cohort under measurement. The ground truth contact network, while unknown, was the same for each data set—it was the same set of participants carrying a single phone measuring both quantities. Both BT- and GPS-derived contact measurements are estimates of the underlying contact pattern, admitting false positives (eg, BT contacts through a wall) and negatives (eg, a missed GPS contact because it occurred in an area of poor satellite reception). GPS-based contact tracking identifies fewer shorter contacts, leading to a significant decrease in expected outbreak intensity and the number of outbreaks, potentially because both participants need to have a sufficiently good location fix to estimate colocation. The denser contacts reported by BT-based contact tracking led to a higher probability of an outbreak and larger outbreaks, resulting in significantly different attack rates for most data sets and diseases. While there were conditions under which no significant differences were observed across the data collection modalities (particularly for SHED7 BT8-GPS8), differences were often significant enough to encourage caution in the uptake and interpretation of these sensed contact networks. GPS8 tends to underestimate the attack rate relative to the others (BT8, BT20, and GPS20), indicating the general inability of GPS colocating to capture proximate contacts within a short distance. Sensing configurations tend to estimate similar attack rates for infectious diseases without a comparatively high R 0 in a more distant underlying population, except for SHED9-measles. Our study cannot conclusively determine if the higher outbreak frequency and size in BT-derived networks is due to false positives in BT or false negatives in GPS, but based on the precision of commodity GPS receivers and their propensity to lose signal in large buildings, we suspected that the observed disparities are predominantly driven by GPS false negatives. If this suspicion is warranted, GPS location–based proximity measurement should be used in epidemiological simulations with caution and in a fashion that anticipates and accounts for the fact that the data collection modality used may be systemically underestimating contact. This is particularly true for the short contacts outside of normal contact networks that drive mixing.

The significance results were relatively insensitive to differences in simulated disease impacting differences in GPS and BT, but the data collection modality induced fewer differences in the results for less contagious diseases, such as seasonal influenza, than for more contagious diseases, such as measles. It is possible that weakly contagious diseases might not demonstrate differences, as outbreaks would be rare and limited in both GPS and BT networks. These findings hold for both a nominal 8-m and 20-m threshold for determining if contact has occurred. The thresholds chosen are already judicious and indicate participants being close enough during a measured portion to have come into close contact during a sensor sleep period, rather than explicitly detecting close contact. Comparing the within-sensor outcomes, the contact threshold impacted the simulated attack rate for most cases, with the exception of SHED8.

We used a stylized, agent-based SEIR model to determine the attack rate using both BT- and GPS-inferred temporal contact patterns. The stylized nature of the simulation implies that the results should be generally correct, but that more detailed models may diverge in the magnitude of the differences observed. SHED7, SHED8, and SHED9 are interesting data sets due to the multiple sensor modalities, but they are also highly biased, being drawn from a university social science participant pool comprised primarily of undergraduate students in the social and physical sciences. GPS or BT data from other demographics will almost certainly have different contact patterns, leading to different outcomes. At one extreme, institutionalized individuals (eg, in incarceration facilities or care homes) have limited mobility and would be expected to have much more convergent GPS and BT contact patterns. Perhaps not surprisingly, some of the worst COVID-19 outbreaks occurred in these institutional settings. Similarly, we analyzed 4 relatively contagious diseases and ignored diseases where a specific type of contact initiates infection, such as sexually transmitted or blood-borne diseases, or where disease propagation is slow or exhibits prolonged latent periods, such as with tuberculosis. As the definition of contact for such excluded diseases is substantially different from those analyzed in this study, the difference between GPS and BT contact patterns may be more or less pronounced. The process we have used to evaluate the differences should generalize to any contagious disease or measured contact pattern and can be used to evaluate the impact of novel contact detection algorithms or other novel diseases such as COVID-19 variants of concern.

While this study has made several meaningful contributions to the literature, particularly in highlighting divergent attack rates for GPS and BT measurements of the same underlying contact network, it is subject to notable limitations. We used 3 data sets drawn from a social sciences participant pool at our institution. These data sets included individuals who were often unknown to each other and likely produced more diffuse data sets than would have been expected had we used snowball or respondent-driven sampling or other socially connected recruiting techniques. Running a similar analysis on other data sets could provide more broadly generalizable or representative results. However, for reasonable privacy reasons, public data sets containing both GPS and BT records are not available, requiring additional measurement effort to extend this analysis. We used an agent-based SEIR model because it provided the most direct link between the data and the simulated diseases. We chose the stylized SEIR model to emphasize the role of evolving contact networks in other disease dynamics. These results could be extended to include more sophisticated disease models and compared against compartmental transmission models grounded in aggregate representations of the underlying contact network. The COVID-19 pandemic has driven innovation in contact tracing, and new measurement techniques based on dongles, beacons, or badges are now readily available. A similar analysis including these data sources could be valuable. Finally, we constrained our analysis to 4 canonical contagious diseases from a historical perspective with relatively well-parameterized behaviors. However, novel diseases will have novel disease parameters. An exploratory simulation study that outlined how diseases might be expected to behave over these contact networks using, for example, a random-walk through parameter space might be valuable in predicting new variants, existing diseases, or new diseases emerging from animal reservoirs.

Conclusions

Epidemiological models of disease propagation are an important tool in controlling and containing epidemic outbreaks. These models rely on the accurate measurement of key biological and behavioral parameters to ground the simulation results. Quantifying the characteristics of dynamic contact networks is a particularly challenging aspect of grounding these simulations. The significant differences in the predicted outcomes for contact networks demonstrated here between GPS- and BT-based contact tracking highlight the difficulty of grounding these simulations. Because of the nature of our data, we know that the contact networks being sought via measurement by BT and GPS should have been identical, as they corresponded to the same device held by the same individual as they went about their lives. The fact that the resulting contact networks and predicted attack rates were different indicates that these modalities are not interchangeable and that caution should be exercised by modelers employing these measures. While BT and GPS data provide more precise measurements than traditional surveys, they are still prone to error and disparate estimates of the underlying network structure and dynamics.

Acknowledgments

The authors would like to thank the Natural Sciences and Engineering Research Council of Canada for funding and Dr Regan Mandryk for her timely statistical advice. The authors would also like to thank Greg Oster and Mohammad Hashemian for their technical consulting and assistance. Finally, NDO would like to extend his gratitude to SYK for supporting his contributions.

Data Availability

Data cannot be made publicly because participant informed consent does not allow disclosure and because anonymized longitudinal location data can be reidentified through crosslinking by those skilled in spatial analysis. Data can be made available through a joint application to both the requester’s and the University of Saskatchewan ethics boards, outlining the intended secondary use of the data and processes for ensuring that reidentification does not occur. The latest contact information for the research ethics board (REB) can be found on the website of the University of Saskatchewan. Currently, the web page for the REB is available [ 70 ].

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 01.04.22; peer-reviewed by K Xu, I Zachary, A Angelucci, S Pesälä; comments to author 28.07.23; revised version received 15.11.23; accepted 27.02.24; published 17.04.24.

©Weicheng Qian, Aranock Cooke, Kevin Gordon Stanley, Nathaniel David Osgood. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

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.

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Methodology

What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

How does social media shape body image in teenagers?
How do children and adults interpret healthy eating in the UK?
What factors influence employee retention in a large organization?
How is anxiety experienced around the world?
How can teachers integrate social issues into science curriculums?

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

Observations: recording what you have seen, heard, or encountered in detailed field notes.
Interviews: personally asking people questions in one-on-one conversations.
Focus groups: asking questions and generating discussion among a group of people.
Surveys : distributing questionnaires with open-ended questions.
Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
You take field notes with observations and reflect on your own experiences of the company culture.
You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

Grounded theory involves collecting data in order to develop new theories.
Ethnography involves immersing yourself in a group or organization to understand its culture.
Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
Phenomenological research involves investigating phenomena through people’s lived experiences.
Action research links theory and practice in several cycles to drive innovative changes.

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

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

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

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

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

dairy or plant-based dairy products
nonstarchy vegetables
protein foods
whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

foods high in saturated fat
foods high in sodium, a mineral found in salt
sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

A woman in a wheelchair, chopping vegetables at a kitchen table.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

what medicines you take for diabetes
what your level of physical activity or your work schedule is
whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Plate method, with half of the circular plate filled with nonstarchy vegetables; one fourth of the plate showing carbohydrate foods, including fruits; and one fourth of the plate showing protein foods. A glass filled with water, or another zero-calorie drink, is on the side.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator or a registered dietitian on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

lower blood glucose, blood pressure, and cholesterol levels
better heart health
healthier weight
better mood and sleep
better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Group of people doing stretching exercises outdoors.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet before, during, and after physical activity.

A man checks his foot while a woman watches over his shoulder.

If you have diabetes, managing your weight may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

following a healthy meal plan
consuming fewer calories
being physically active
getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

lower your feelings of stress, depression, or anxiety
manage problems sleeping or remembering things
see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

Man with obesity looking distressed talking with a health care professional.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

how changing when you eat may affect body weight and metabolism
how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
whether low-carbohydrate meal plans can help lower blood glucose levels
which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.

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Plate method. The plate method helps you control portion sizes without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal. Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your ...

Research Methodology Explained: A Beginner's Guide

Understanding Research Methodology

Key Elements of Research Methodology

Research Approaches and Their Applications

Types of Research Methodology

Data Collection and Analysis Techniques

Sampling Designs

Choosing the Right Research Methodology

Methodological Considerations

Choosing Between Qualitative and Quantitative Approaches

Key Components of Research Methodology

Data Collection and Analysis

Ethical Considerations and Methodological Rigor

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Qualitative vs quantitative data

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Choosing the Right Research Methodology: A Guide for Researchers

Understanding different research methods:

Qualitative vs quantitative research:

Qualitative research methodology:

Quantitative research methodology:

Analysing qualitative vs quantitative data:

When to use qualitative vs quantitative research:

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Table of Contents

What is Research Methodology?

How to Define Research Methodology

Role of Methodology in Research

Steps in Choosing the Right Research Methodology

Identifying the Research Problem

Reviewing Previous Research

Formulating Research Questions

Implementing the Research Methodology

Data Collection Techniques

Data Analysis Methods

Ensuring the Validity and Reliability of Your Research

Understanding Validity in Research

Ensuring Reliability in Your Study

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