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  • What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

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

It should include:

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

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Table of contents

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

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

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

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

Option 1: Start with your “what”

What research problem or question did you investigate?

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

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

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

Option 2: Start with your “why”

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

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

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

Quantitative methods

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

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

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

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

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

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

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

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

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

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

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

  • Information bias
  • Omitted variable bias
  • Regression to the mean
  • Survivorship bias
  • Undercoverage bias
  • Sampling bias

Qualitative methods

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

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

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

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

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

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

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

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

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

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

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

  • The Hawthorne effect
  • Observer bias
  • The placebo effect
  • Response bias and Nonresponse bias
  • The Pygmalion effect
  • Recall bias
  • Social desirability bias
  • Self-selection bias

Mixed methods

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

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

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

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

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

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

Specific methods might include:

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

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

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

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

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

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

1. Focus on your objectives and research questions

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

2. Cite relevant sources

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

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

3. Write for your audience

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

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

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.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles

Methodology

  • Cluster sampling
  • Stratified sampling
  • Thematic analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias

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

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

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

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

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

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

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

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

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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

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

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  • Knowledge Base
  • Dissertation
  • What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

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

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

It should include:

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

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Table of contents

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

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

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

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

Option 1: Start with your “what”

What research problem or question did you investigate?

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

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

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

Option 2: Start with your “why”

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

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

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

Quantitative methods

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

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

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

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

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

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

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

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

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

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

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

Qualitative methods

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

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

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

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

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

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

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

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

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

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

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

Mixed methods

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

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

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

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

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

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

Specific methods might include:

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

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

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

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

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

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

1. Focus on your objectives and research questions

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

2. Cite relevant sources

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

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

3. Write for your audience

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

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

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

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

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

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

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

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

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

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

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

Cite this Scribbr article

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McCombes, S. (2022, October 10). What Is a Research Methodology? | Steps & Tips. Scribbr. Retrieved 12 August 2024, from https://www.scribbr.co.uk/thesis-dissertation/methodology/

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Dissertation Methodology – Structure, Example and Writing Guide

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Dissertation Methodology

Dissertation Methodology

In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.

Here are the basic elements that are typically included in a dissertation methodology:

  • Introduction : This section should explain the importance and goals of your research .
  • Research Design : Outline your research approach and why it’s appropriate for your study. You might be conducting an experimental research, a qualitative research, a quantitative research, or a mixed-methods research.
  • Data Collection : This section should detail the methods you used to collect your data. Did you use surveys, interviews, observations, etc.? Why did you choose these methods? You should also include who your participants were, how you recruited them, and any ethical considerations.
  • Data Analysis : Explain how you intend to analyze the data you collected. This could include statistical analysis, thematic analysis, content analysis, etc., depending on the nature of your study.
  • Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your study. For instance, you could discuss measures taken to reduce bias, how you ensured that your measures accurately capture what they were intended to, or how you will handle any limitations in your study.
  • Ethical Considerations : This is where you state how you have considered ethical issues related to your research, how you have protected the participants’ rights, and how you have complied with the relevant ethical guidelines.
  • Limitations : Acknowledge any limitations of your methodology, including any biases and constraints that might have affected your study.
  • Summary : Recap the key points of your methodology chapter, highlighting the overall approach and rationalization of your research.

Types of Dissertation Methodology

The type of methodology you choose for your dissertation will depend on the nature of your research question and the field you’re working in. Here are some of the most common types of methodologies used in dissertations:

Experimental Research

This involves creating an experiment that will test your hypothesis. You’ll need to design an experiment, manipulate variables, collect data, and analyze that data to draw conclusions. This is commonly used in fields like psychology, biology, and physics.

Survey Research

This type of research involves gathering data from a large number of participants using tools like questionnaires or surveys. It can be used to collect a large amount of data and is often used in fields like sociology, marketing, and public health.

Qualitative Research

This type of research is used to explore complex phenomena that can’t be easily quantified. Methods include interviews, focus groups, and observations. This methodology is common in fields like anthropology, sociology, and education.

Quantitative Research

Quantitative research uses numerical data to answer research questions. This can include statistical, mathematical, or computational techniques. It’s common in fields like economics, psychology, and health sciences.

Case Study Research

This type of research involves in-depth investigation of a particular case, such as an individual, group, or event. This methodology is often used in psychology, social sciences, and business.

Mixed Methods Research

This combines qualitative and quantitative research methods in a single study. It’s used to answer more complex research questions and is becoming more popular in fields like social sciences, health sciences, and education.

Action Research

This type of research involves taking action and then reflecting upon the results. This cycle of action-reflection-action continues throughout the study. It’s often used in fields like education and organizational development.

Longitudinal Research

This type of research involves studying the same group of individuals over an extended period of time. This could involve surveys, observations, or experiments. It’s common in fields like psychology, sociology, and medicine.

Ethnographic Research

This type of research involves the in-depth study of people and cultures. Researchers immerse themselves in the culture they’re studying to collect data. This is often used in fields like anthropology and social sciences.

Structure of Dissertation Methodology

The structure of a dissertation methodology can vary depending on your field of study, the nature of your research, and the guidelines of your institution. However, a standard structure typically includes the following elements:

  • Introduction : Briefly introduce your overall approach to the research. Explain what you plan to explore and why it’s important.
  • Research Design/Approach : Describe your overall research design. This can be qualitative, quantitative, or mixed methods. Explain the rationale behind your chosen design and why it is suitable for your research questions or hypotheses.
  • Data Collection Methods : Detail the methods you used to collect your data. You should include what type of data you collected, how you collected it, and why you chose this method. If relevant, you can also include information about your sample population, such as how many people participated, how they were chosen, and any relevant demographic information.
  • Data Analysis Methods : Explain how you plan to analyze your collected data. This will depend on the nature of your data. For example, if you collected quantitative data, you might discuss statistical analysis techniques. If you collected qualitative data, you might discuss coding strategies, thematic analysis, or narrative analysis.
  • Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your research. This might include steps you took to reduce bias or increase the accuracy of your measurements.
  • Ethical Considerations : If relevant, discuss any ethical issues associated with your research. This might include how you obtained informed consent from participants, how you ensured participants’ privacy and confidentiality, or any potential conflicts of interest.
  • Limitations : Acknowledge any limitations in your research methodology. This could include potential sources of bias, difficulties with data collection, or limitations in your analysis methods.
  • Summary/Conclusion : Briefly summarize the key points of your methodology, emphasizing how it helps answer your research questions or hypotheses.

How to Write Dissertation Methodology

Writing a dissertation methodology requires you to be clear and precise about the way you’ve carried out your research. It’s an opportunity to convince your readers of the appropriateness and reliability of your approach to your research question. Here is a basic guideline on how to write your methodology section:

1. Introduction

Start your methodology section by restating your research question(s) or objective(s). This ensures your methodology directly ties into the aim of your research.

2. Approach

Identify your overall approach: qualitative, quantitative, or mixed methods. Explain why you have chosen this approach.

  • Qualitative methods are typically used for exploratory research and involve collecting non-numerical data. This might involve interviews, observations, or analysis of texts.
  • Quantitative methods are used for research that relies on numerical data. This might involve surveys, experiments, or statistical analysis.
  • Mixed methods use a combination of both qualitative and quantitative research methods.

3. Research Design

Describe the overall design of your research. This could involve explaining the type of study (e.g., case study, ethnography, experimental research, etc.), how you’ve defined and measured your variables, and any control measures you’ve implemented.

4. Data Collection

Explain in detail how you collected your data.

  • If you’ve used qualitative methods, you might detail how you selected participants for interviews or focus groups, how you conducted observations, or how you analyzed existing texts.
  • If you’ve used quantitative methods, you might detail how you designed your survey or experiment, how you collected responses, and how you ensured your data is reliable and valid.

5. Data Analysis

Describe how you analyzed your data.

  • If you’re doing qualitative research, this might involve thematic analysis, discourse analysis, or grounded theory.
  • If you’re doing quantitative research, you might be conducting statistical tests, regression analysis, or factor analysis.

Discuss any ethical issues related to your research. This might involve explaining how you obtained informed consent, how you’re protecting participants’ privacy, or how you’re managing any potential harms to participants.

7. Reliability and Validity

Discuss the steps you’ve taken to ensure the reliability and validity of your data.

  • Reliability refers to the consistency of your measurements, and you might discuss how you’ve piloted your instruments or used standardized measures.
  • Validity refers to the accuracy of your measurements, and you might discuss how you’ve ensured your measures reflect the concepts they’re supposed to measure.

8. Limitations

Every study has its limitations. Discuss the potential weaknesses of your chosen methods and explain any obstacles you faced in your research.

9. Conclusion

Summarize the key points of your methodology, emphasizing how it helps to address your research question or objective.

Example of Dissertation Methodology

An Example of Dissertation Methodology is as follows:

Chapter 3: Methodology

  • Introduction

This chapter details the methodology adopted in this research. The study aimed to explore the relationship between stress and productivity in the workplace. A mixed-methods research design was used to collect and analyze data.

Research Design

This study adopted a mixed-methods approach, combining quantitative surveys with qualitative interviews to provide a comprehensive understanding of the research problem. The rationale for this approach is that while quantitative data can provide a broad overview of the relationships between variables, qualitative data can provide deeper insights into the nuances of these relationships.

Data Collection Methods

Quantitative Data Collection : An online self-report questionnaire was used to collect data from participants. The questionnaire consisted of two standardized scales: the Perceived Stress Scale (PSS) to measure stress levels and the Individual Work Productivity Questionnaire (IWPQ) to measure productivity. The sample consisted of 200 office workers randomly selected from various companies in the city.

Qualitative Data Collection : Semi-structured interviews were conducted with 20 participants chosen from the initial sample. The interview guide included questions about participants’ experiences with stress and how they perceived its impact on their productivity.

Data Analysis Methods

Quantitative Data Analysis : Descriptive and inferential statistics were used to analyze the survey data. Pearson’s correlation was used to examine the relationship between stress and productivity.

Qualitative Data Analysis : Interviews were transcribed and subjected to thematic analysis using NVivo software. This process allowed for identifying and analyzing patterns and themes regarding the impact of stress on productivity.

Reliability and Validity

To ensure reliability and validity, standardized measures with good psychometric properties were used. In qualitative data analysis, triangulation was employed by having two researchers independently analyze the data and then compare findings.

Ethical Considerations

All participants provided informed consent prior to their involvement in the study. They were informed about the purpose of the study, their rights as participants, and the confidentiality of their responses.

Limitations

The main limitation of this study is its reliance on self-report measures, which can be subject to biases such as social desirability bias. Moreover, the sample was drawn from a single city, which may limit the generalizability of the findings.

Where to Write Dissertation Methodology

In a dissertation or thesis, the Methodology section usually follows the Literature Review. This placement allows the Methodology to build upon the theoretical framework and existing research outlined in the Literature Review, and precedes the Results or Findings section. Here’s a basic outline of how most dissertations are structured:

  • Acknowledgements
  • Literature Review (or it may be interspersed throughout the dissertation)
  • Methodology
  • Results/Findings
  • References/Bibliography

In the Methodology chapter, you will discuss the research design, data collection methods, data analysis methods, and any ethical considerations pertaining to your study. This allows your readers to understand how your research was conducted and how you arrived at your results.

Advantages of Dissertation Methodology

The dissertation methodology section plays an important role in a dissertation for several reasons. Here are some of the advantages of having a well-crafted methodology section in your dissertation:

  • Clarifies Your Research Approach : The methodology section explains how you plan to tackle your research question, providing a clear plan for data collection and analysis.
  • Enables Replication : A detailed methodology allows other researchers to replicate your study. Replication is an important aspect of scientific research because it provides validation of the study’s results.
  • Demonstrates Rigor : A well-written methodology shows that you’ve thought critically about your research methods and have chosen the most appropriate ones for your research question. This adds credibility to your study.
  • Enhances Transparency : Detailing your methods allows readers to understand the steps you took in your research. This increases the transparency of your study and allows readers to evaluate potential biases or limitations.
  • Helps in Addressing Research Limitations : In your methodology section, you can acknowledge and explain the limitations of your research. This is important as it shows you understand that no research method is perfect and there are always potential weaknesses.
  • Facilitates Peer Review : A detailed methodology helps peer reviewers assess the soundness of your research design. This is an important part of the publication process if you aim to publish your dissertation in a peer-reviewed journal.
  • Establishes the Validity and Reliability : Your methodology section should also include a discussion of the steps you took to ensure the validity and reliability of your measurements, which is crucial for establishing the overall quality of your research.

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Guide for Thesis Research

  • Introduction to the Thesis Process
  • Project Planning
  • Literature Review
  • Theoretical Frameworks
  • Research Methodology
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Basics of Methodology

Research is a process of inquiry that is carried out in a pondered, organized, and strategic manner. In order to obtain high quality results, it is important to understand methodology.

Research methodology refers to how your project will be designed, what you will observe or measure, and how you will collect and analyze data. The methods you choose must be appropriate for your field and for the specific research questions you are setting out to answer.

A strong understanding of methodology will help you:

  • apply appropriate research techniques
  • design effective data collection instruments
  • analyze and interpret your data
  • develop well-founded conclusions

Below, you will find resources that mostly cover general aspects of research methodology. In the left column, you will find resources that specifically cover qualitative, quantitative, and mixed methods research.

General Works on Methodology

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Qualitative Research

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Quantitative Research

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Mixed Methods Research

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  • PhD/Doctorate

What are acceptable dissertation research methods?

August 16, 2023

Reading time:  3–4 minutes

Doctoral research is the cornerstone of a PhD program .

In order to write a dissertation, you must complete extensive, detailed research. Depending on your area of study, different types of research methods will be appropriate to complete your work.

“The choice of research method depends on the questions you hope to answer with your research,” says Curtis Brant, PhD, Capella University dean of research and scholarship.

Once you’ve identified your research problem, you’ll employ the methodology best suited for solving the problem.

There are two primary dissertation research methods: qualitative and quantitative.

Qualitative

Qualitative research focuses on examining the topic via cultural phenomena, human behavior or belief systems. This type of research uses interviews, open-ended questions or focus groups to gain insight into people’s thoughts and beliefs around certain behaviors and systems.

Dr. Brant says there are several approaches to qualitative inquiry. The three most routinely used include:

Generic qualitative inquiry. The researcher focuses on people’s experiences or perceptions in the real world. This often includes, but is not limited to, subjective opinions, attitudes and beliefs .

Case study. The researcher performs an in-depth exploration of a program, event, activity or process with an emphasis on the experience of one or more individuals. The focus of this kind of inquiry must be defined and often includes more than one set of data, such as interviews and field notes, observations or other qualitative data.

Phenomenological. The researcher identifies lived experiences associated with how an individual encounters and engages with the real world .

Qualitative research questions seek to discover:

  • A participant’s verbal descriptions of a phenomenon being investigated
  •  A researcher’s observations of the phenomenon being investigated
  • An integrated interpretation of participant’s descriptions and researchers observations

Quantitative

Quantitative research involves the empirical investigation of observable and measurable variables. It is used for theory testing, predicting outcomes or determining relationships between and among variables using statistical analysis.

According to Dr. Brant, there are two primary data sources for quantitative research.

Surveys: Surveys involve asking people a set of questions, usually testing for linear relationships, statistical differences or statistical independence. This approach is common in correlation research designs.

Archival research (secondary data analysis). Archival research involves using preexisting data to answer research questions instead of collecting data from active human participants.

Quantitative research questions seek to address:

  • Descriptions of variables being investigated
  • Measurements of relationships between (at least two) variables
  • Differences between two or more groups’ scores on a variable or variables

Which method should you choose?

Choosing a qualitative or quantitative methodology for your research will be based on the nature of the questions you ask, the preferred method in your field, the feasibility of the approach and other factors. Many programs offer doctoral mentors and support teams that can help guide you throughout the process.

Capella University offers PhD and professional doctorate degree programs ranging from business to education and health to technology. Learn more about Capella doctoral programs and doctoral support.

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PhD Thesis Writing Process: A Systematic Approach—How to Write Your Methodology, Results and Conclusion

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Methodology Template

The fastest (and smartest) way to craft a research methodology that communicates your design and earns you marks.

Available in Google Doc, Word & PDF format 4.9 star rating, 5000 + downloads

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research methodology in phd thesis

What It Covers

This template covers all the core components required in the methodology section of a typical dissertation, thesis or research paper, including:

  • The opening section
  • Research philosophy
  • Research type
  • Research strategy
  • Time horizon
  • Sampling strategy
  • Data collection methods
  • Data analysis methods
  • Conclusion & summary

The purpose of each section is explained in plain language, along with  practical examples to help you understand exactly what’s required.

The cleanly-formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

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FAQs: Research Methdodology Template

What format is the template (doc, pdf, ppt, etc.).

The methodology chapter template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

What types of dissertations/theses can this template be used for?

The methodology template follows the standard format for academic research projects, which means it will be suitable for the vast majority of dissertations and theses (especially those within the sciences), whether they adopt a qualitative, quantitative, or mixed-methods approach. The template is loosely based on Saunders’ research onion , which is recommended as a methodological framework by many universities.

Keep in mind that the exact requirements for the methodology chapter/section will vary between universities and degree programs. These are typically minor, but it’s always a good idea to double-check your university’s requirements before you finalize your structure.

Is this template for an undergrad, Master or PhD-level thesis?

This template can be used for a dissertation, thesis or research project at any level of study. Doctoral-level projects typically require the methodology chapter to be more extensive/comprehensive, but the structure will typically remain the same.

How long should the methodology chapter be?

This can vary a fair deal, depending on the level of study (undergrad, Master or Doctoral), the field of research, as well as your university’s specific requirements. Therefore, it’s best to check with your university or review past dissertations from your program to get an accurate estimate. 

How detailed should my methodology be?

As a rule of thumb, you should provide enough detail for another researcher to replicate your study. This includes clear descriptions of procedures, tools, and techniques you used to collect and analyse your data, as well as your sampling approach.

How technical should my language be in this chapter?

In the methodology chapter, your language should be technical enough to accurately convey your research methods and processes, but also clear and precise to ensure it’s accessible to readers within your field.

Aim for a balance where the technical aspects of your methods are thoroughly explained without overusing jargon or overly complex language.

Should I include a pilot study in my methodology?

If you conducted a pilot study, you can include it in the methodology to demonstrate the feasibility and refinement of your methods. Be sure to obtain the necessary permissions from your research advisor before conducting any pilot studies, though. 

Can I share this template with my friends/colleagues?

Yes, you’re welcome to share this template in its original format (no editing allowed). If you want to post about it on your blog or social media, we kindly request that you reference this page as your source.

Do you have templates for the other chapters?

Yes, we do. We are constantly developing our collection of free resources to help students complete their dissertations and theses. You can view all of our template resources here .

Can Grad Coach help me with my methodology?

Yes, we can assist with your methodology chapter (or any other chapter) on a coaching basis. If you’re interested, feel free to get in touch to discuss our private coaching services .

Additional Resources

If you’re working on a research proposal, you’ll also want to check these out…

Methodology Bootcamp

1-On-1 Private Coaching

The Grad Coach YouTube Channel

The Grad Coach Podcast

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Likelihood-based haplotype frequency modeling using variable-order Markov chains
Statistical Divergences for Learning and Inference: Limit Laws and Non-Asymptotic Bounds ,
Missing Data Methods for Observational Health Dataset
Methods, Models, and Interpretations for Spatial-Temporal Public Health Applications
Statistical Methods for Clustering and High Dimensional Time Series Analysis
Causal Structure Learning in High Dimensions ,
Geometric algorithms for interpretable manifold learning
2021
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Improving Uncertainty Quantification and Visualization for Spatiotemporal Earthquake Rate Models for the Pacific Northwest ,
Statistical modeling of long memory and uncontrolled effects in neural recordings
Distribution-free consistent tests of independence via marginal and multivariate ranks
Causality, Fairness, and Information in Peer Review ,
Subnational Estimation of Period Child Mortality in a Low and Middle Income Countries Context
Progress in nonparametric minimax estimation and high dimensional hypothesis testing ,
Likelihood Analysis of Causal Models
Bayesian Models in Population Projections and Climate Change Forecast
2020
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Statistical Methods for Adaptive Immune Receptor Repertoire Analysis and Comparison
Statistical Methods for Geospatial Modeling with Stratified Cluster Survey Data
Representation Learning for Partitioning Problems
Estimation and Inference in Changepoint Models
Space-Time Contour Models for Sea Ice Forecasting ,
Non-Gaussian Graphical Models: Estimation with Score Matching and Causal Discovery under Zero-Inflation ,
Scalable Learning in Latent State Sequence Models
2019
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Latent Variable Models for Prediction & Inference with Proxy Network Measures
Bayesian Hierarchical Models and Moment Bounds for High-Dimensional Time Series ,
Estimation and testing under shape constraints ,
Inferring network structure from partially observed graphs
Fitting Stochastics Epidemic Models to Multiple Data Types
Realized genome sharing in random effects models for quantitative genetic traits
Large-Scale B Cell Receptor Sequence Analysis Using Phylogenetics and Machine Learning
Statistical Methods for Manifold Recovery and C^ (1, 1) Regression on Manifolds
2018
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Topics in Statistics and Convex Geometry: Rounding, Sampling, and Interpolation
Estimation and Testing Following Model Selection
Topics on Least Squares Estimation
Discovering Interaction in Multivariate Time Series
Nonparametric inference on monotone functions, with applications to observational studies
Model-Based Penalized Regression
Bayesian Methods for Graphical Models with Limited Data
Parameter Identification and Assessment of Independence in Multivariate Statistical Modeling
Preferential sampling and model checking in phylodynamic inference
Linear Structural Equation Models with Non-Gaussian Errors: Estimation and Discovery
Coevolution Regression and Composite Likelihood Estimation for Social Networks
2017
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"Applications of Robust Statistical Methods in Quantitative Finance"
"Scalable Manifold Learning and Related Topics"
"Topics in Graph Clustering"
"Methods for Estimation and Inference for High-Dimensional Models" ,
"Scalable Methods for the Inference of Identity by Descent"
2016
Title Author Supervisor
"Space-Time Smoothing Models for Surveillance and Complex Survey Data"
"Testing Independence in High Dimensions & Identifiability of Graphical Models"
"Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes"
"Bayesian Methods for Inferring Gene Regulatory Networks" ,
"Finite Sampling Exponential Bounds"
"Finite Population Inference for Causal Parameters"
"Projection and Estimation of International Migration"
"Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling"
2015
Title Author Supervisor
"Theory and Methods for Tensor Data"
"Discrete-Time Threshold Regression for Survival Data with Time-Dependent Covariates"
"Degeneracy, Duration, and Co-Evolution: Extending Exponential Random Graph Models (ERGM) for Social Network Analysis"
"The Likelihood Pivot: Performing Inference with Confidence"
"Lord's Paradox and Targeted Interventions: The Case of Special Education" ,
"Bayesian Modeling of a High Resolution Housing Price Index"
"Phylogenetic Stochastic Mapping"
2014
Title Author Supervisor
"Monte Carlo Estimation of Identity by Descent in Populations"
"Bayesian Spatial and Temporal Methods for Public Health Data" ,
"Functional Quantitative Genetics and the Missing Heritability Problem"
"Predictive Modeling of Cholera Outbreaks in Bangladesh" ,
"Gravimetric Anomaly Detection Using Compressed Sensing"
"R-Squared Inference Under Non-Normal Error"
2013
Title Author Supervisor
"Learning and Manifolds: Leveraging the Intrinsic Geometry"
"An Algorithmic Framework for High Dimensional Regression with Dependent Variables"
"Bayesian Population Reconstruction: A Method for Estimating Age- and Sex-Specific Vital Rates and Population Counts with Uncertainty from Fragmentary Data"
"Bayesian Nonparametric Inference of Effective Population Size Trajectories from Genomic Data"
"Modeling Heterogeneity Within and Between Matrices and Arrays"
"Shape-Constrained Inference for Concave-Transformed Densities and their Modes"
"Statistical Inference Using Kronecker Structured Covariance"
2012
Title Author Supervisor
"Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data"
"Tests for Differences between Least Squares and Robust Regression Parameter Estimates and Related To Pics"
"Bayesian Modeling of Health Data in Space and Time"
"Coordinate-Free Exponential Families on Contingency Tables" ,
2011
Title Author Supervisor
"Seeing the trees through the forest; a competition model for growth and mortality"
"Bayesian Inference of Exponential-family Random Graph Models for Social Networks"
"Statistical Models for Estimating and Predicting HIV/AIDS Epidemics"
"Modeling the Game of Soccer Using Potential Functions"
"Parametrizations of Discrete Graphical Models"
"A Bayesian Surveillance System for Detecting Clusters of Non-Infectious Diseases"
"Statistical Approaches to Analyze Mass Spectrometry Data Graduating Year" ,
2010
Title Author Supervisor
"Multivariate Geostatistics and Geostatistical Model Averaging"
"Covariance estimation in the Presence of Diverse Types of Data"
"Portfolio Optimization with Tail Risk Measures and Non-Normal Returns"
"Convex analysis methods in shape constrained estimation."
"Estimating social contact networks to improve epidemic simulation models"
2009
Title Author Supervisor
"Models for Heterogeneity in Heterosexual Partnership Networks"
"A comparison of alternative methodologies for estimation of HIV incidence"
"Bayesian Model Averaging and Multivariate Conditional Independence Structures"
"Conditional tests for localizing trait genes"
"Combining and Evaluating Probabilistic Forecasts"
"Probabilistic weather forecasting using Bayesian model averaging"
"Statistical Analysis of Portfolio Risk and Performance Measures: the Influence Function Approach"
"Factor Model Monte Carlo Methods for General Fund-of-Funds Portfolio Management"
"Statistical Models for Social Network Data and Processes"
2008
Title Author Supervisor
"Inference from partially-observed network data"
"Models and Inference of Transmission of DNA Methylation Patterns in Mammalian Somatic Cells"
"Estimates and projections of the total fertility rate"
"Nonparametric estimation of multivariate monotone densities"
"Learning transcriptional regulatory networks from the integration of heterogeneous high-throughout data"
"Extensions of Latent Class Transition Models with Application to Chronic Disability Survey Data"
"Statistical Solutions to Some Problems in Medical Imaging" ,
"Statistical methods for peptide and protein identification using mass spectrometry"
2007
Title Author Supervisor
"Statistical Methodology for Longitudinal Social Network Data"
"Probabilistic weather forecasting with spatial dependence"
"Wavelet variance analysis for time series and random fields" ,
"Bayesian hierarchical curve registration"
""Up-and-Down" and the Percentile-Finding Problem"
2006
Title Author Supervisor
"An efficient and flexible model for patterns of population genetic variation"
"Learning in Spectral Clustering"
"Variable selection and other extensions of the mixture model clustering framework"
"Algorithms for Estimating the Cluster Tree of a Density"
"Likelihood inference for population structure, using the coalescent"
"Exploring rates and patterns of variability in gene conversion and crossover in the human genome"
"Alleviating ecological bias in generalized linear models and optimal design with subsample data" ,
"Nonparametric estimation for current status data with competing risks" ,
"Goodness-of-fit statistics based on phi-divergences"
2005
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"Alternative models for estimating genetic maps from pedigree data"
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"Robust estimation of factor models in finance"
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2004
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"The genetic structure of related recombinant lines"
"Joint relationship inference from three or more individuals in the presence of genotyping error"
"Personal characteristics and covariate measurement error in disease risk estimation" ,
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2002
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"Practical importance sampling methods for finite mixture models and multiple imputation"
"Applying graphical models to partially observed data-generating processes" ,
"Generalized linear mixed models: development and comparison of different estimation methods"
2001
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"Bayesian inference for deterministic simulation models for environmental assessment"
"Modeling recessive lethals: An explanation for excess sharing in siblings"
"Estimation with bivariate interval censored data"
"Latent models for cross-covariance" ,
2000
Title Author Supervisor
"Wavelet-based estimation for trend contaminated long memory processes" ,
"Global covariance modeling: A deformation approach to anisotropy"
"Likelihood inference for parameteric models of dispersal"
"Bayesian inference in hidden stochastic population processes"
"Logic regression and statistical issues related to the protein folding problem" ,
"Likelihood ratio inference in regular and non-regular problems"
"Estimating the association between airborne particulate matter and elderly mortality in Seattle, Washington using Bayesian Model Averaging" ,
"Nonhomogeneous hidden Markov models for downscaling synoptic atmospheric patterns to precipitation amounts" ,
"Detecting and extracting complex patterns from images and realizations of spatial point processes"
"A model selection approach to partially linear regression"
1999
Title Author Supervisor
"Generalization of boosting algorithms and applications of Bayesian inference for massive datasets" ,
"Bayesian inference for noninvertible deterministic simulation models, with application to bowhead whale assessment"
"Monte Carlo likelihood calculation for identity by descent data"
"Fast automatic unsupervised image segmentation and curve detection in spatial point processes"
"Semiparametric inference based on estimating equations in regressions models for two phase outcome dependent sampling" ,
"Capture-recapture estimation of bowhead whale population size using photo-identification data" ,
"Lifetime and disease onset distributions from incomplete observations"
"Statistical approaches to distinct value estimation" ,
1998
Title Author Supervisor
"Application of ridge regression for improved estimation of parameters in compartmental models"
"Bayesian modeling of highly structured systems using Markov chain Monte Carlo"
"Assessing nonstationary time series using wavelets" ,
"Lattice conditional independence models for incomplete multivariate data and for seemingly unrelated regressions" ,
"Estimation for counting processes with incomplete data"
"Regularization techniques for linear regression with a large set of carriers"
"Large sample theory for pseudo maximum likelihood estimates in semiparametric models"
"Additive mixture models for multichannel image data"
1997
Title Author Supervisor
"Phylogenies via conditional independence modeling"
"Bayesian model averaging in censored survival models"
"Bayesian information retrieval"
"Statistical inference for partially observed markov population processes"
"Tools for the advancement of undergraduate statistics education"
"A new learning procedure in acyclic directed graphs"
1996
Title Author Supervisor
"Variability estimation in linear inverse problems"
"Inference in a discrete parameter space"
"Bootstrapping functional m-estimators"
1995
Title Author Supervisor
"Estimation of heterogeneous space-time covariance"
"Semiparametric estimation of major gene and random environmental effects for age of onset"
"Statistical analysis of biological monitoring data: State-space models for species compositions"
1994
Title Author Supervisor
"Spatial applications of Markov chain Monte Carlo for bayesian inference"
"Accounting for model uncertainty in linear regression"
"Robust estimation in point processes"
"Multilevel modeling of discrete event history data using Markov chain Monte Carlo methods"
"Estimation in regression models with interval censoring"
1993
Title Author Supervisor
"A Bayesian framework and importance sampling methods for synthesizing multiple sources of evidence and uncertainty linked by a complex mechanistic model"
"State-space modeling of salmon migration and Monte Carlo Alternatives to the Kalman filter"
"The Poisson clumping heuristic and the survival of genome in small pedigrees"
"Markov chain Monte Carlo estimates of probabilities on complex structures"
"A class of stochastic models for relating synoptic atmospheric patterns to local hydrologic phenomena"
1992
Title Author Supervisor
"Auxiliary and missing covariate problems in failure time regression analysis"
"A high order hidden markov model"
"Bayesian methods for the analysis of misclassified or incomplete multivariate discrete data"
1991
Title Author Supervisor
"General-weights bootstrap of the empirical process"
"The weighted likelihood bootstrap and an algorithm for prepivoting"
1990
Title Author Supervisor
"Modelling agricultural field trials in the presence of outliers and fertility jumps"
"Modeling and bootstrapping for non-gaussian time series"
"Genetic restoration on complex pedigrees"
"Incorporating covariates into a beta-binomial model with applications to medicare policy: A Bayes/empirical Bayes approach"
"Likelihood and exponential families"
1989
Title Author Supervisor
"Classical inference in spatial statistics"
"Estimation of mixing and mixed distributions"
1988
Title Author Supervisor
"Exploratory methods for censored data"
"Aspects of robust analysis in designed experiments"
"Diagnostics for time series models"
"Constrained cluster analysis and image understanding"
1987
Title Author Supervisor
"Time series models for continuous proportions"
"The data viewer: A program for graphical data analysis"
"Additive principal components: A method for estimating additive constraints with small variance from multivariate data"
"Kullback-Leibler estimation of probability measures with an application to clustering"
1986
Title Author Supervisor
"Estimation for infinite variance autoregressive processes"
"A computer system for Monte Carlo experimentation"
1985
Title Author Supervisor
"Robust estimation for the errors-in-variables model"
"Robust statistics on compact metric spaces"
"Weak convergence and a law of the iterated logarithm for processes indexed by points in a metric space"
1983
Title Author Supervisor
"The statistics of long memory processes"

IMAGES

  1. 2: Steps of methodology of the thesis

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  2. 2 Research methodology used in this PhD research

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  3. Steps for preparing research methodology

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  4. PhD : Dr. Maryam Kausar: Research Methodology for my thesis

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  5. phd research methodology ppt

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  6. Dissertation Research Methodology Structure

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COMMENTS

  1. PDF 3 Methodology

    3 Methodology3. Methodology(In this unit I use the word Methodology as a general term to cover whatever you decide to include in the chapter where you discuss alternative methodological approaches, justify your chosen research method, and describe the process and participants i. your study).The Methodology chapter is perhaps the part of a ...

  2. What Is a Research Methodology?

    Step 1: Explain your methodological approach. Step 2: Describe your data collection methods. Step 3: Describe your analysis method. Step 4: Evaluate and justify the methodological choices you made. Tips for writing a strong methodology chapter. Other interesting articles.

  3. PDF PhD Thesis Writing Process: A Systematic Approach—How to Write ...

    ious steps of thesis methodology, results and conclusion writing to pilot the PhD students. This road map is a useful guidance especially for students of ... The Significance of This Research Writing methodology for your PhD thesis requires exceptional skill that every . Q. Faryadi DOI: 10.4236/ce.2019.104057 770 Creative Education

  4. How To Write The Methodology Chapter

    Do yourself a favour and start with the end in mind. Section 1 - Introduction. As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims. As we've discussed many times on the blog ...

  5. Research Methodology Example (PDF + Template)

    Research Methodology Example. Detailed Walkthrough + Free Methodology Chapter Template. If you're working on a dissertation or thesis and are looking for an example of a research methodology chapter, you've come to the right place. In this video, we walk you through a research methodology from a dissertation that earned full distinction ...

  6. Research Methodology Chapter: 5 Tips & Tricks

    Overview: Writing The Methodology Chapter. Develop a (rough) outline before you start writing. Draw inspiration from similar studies in your topic area. Justify every research design choice that you make. Err on the side of too much detail, rather than too little. Back up every design choice by referencing literature. 1.

  7. PDF Writing up your PhD (Qualitative Research)

    This is for PhD students working on a qualitative thesis who have completed their data collection and analysis and are at the stage of writing up. The materials should also be useful if you are writing up a 'mixed-methods' thesis, including chapters of analysis and discussion of qualitative data.

  8. PDF PhD Thesis Writing Process: A Systematic Approach—How to Write ...

    1) To help PhD candidates in writing scientifically correct PhD thesis. 2) To describe PhD thesis writing process. 3) To assist PhD candidates to understand what PhD means. 4. Methodology The methodology applied in this research was descriptive as it discusses and de-scribes the various parts of PhD thesis and explains the how to do of them in a

  9. What Is a Research Methodology?

    A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research. It should include: The type of research you conducted; How you collected and analysed your data; Any tools or materials you used in the research ...

  10. PDF A Practical Guide to Dissertation and Thesis Writing

    In the British university tradition, on the other hand, a research degree by thesis requires no courses and the candidate does a lot of self-study to build a base of knowledge from the ground up on their own with occasional help from a supervisor. The degree is awarded solely on the quality of the original research, as reported in a thesis.

  11. Dissertation Methodology

    In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.

  12. PDF Presenting Methodology and Research Approach

    Presenting Methodology and Research Approach OVERVIEW Chapter 3 of the dissertation presents the research design and the specific procedures used in conducting your study. A research design includes various interrelated elements that reflect its sequential nature. This chapter is intended to show the reader that you have an understanding of the ...

  13. PDF Guidelines for The PhD Dissertation

    3 sample title page for a phd dissertation copyright notice abstract sample abstract formatting errors front and back matter supplemental material tables and figures visual material acknowledging the work of others page 19 references footnotes bibliography citation & style guides use of copyrighted material page 20 services and information page 22 proquest publishing orders and payments

  14. How To Choose The Right 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. ... If you're undertaking research as part of a PhD, you may have a fairly open-ended time limit, but ...

  15. LibGuides: Guide for Thesis Research: Research Methodology

    ISBN: 9788132105961. Publication Date: 2010. A Gentle Guide to Research Methods Gordon Rugg. Provides an overview of research methods, including research design, data collection methods, statistics, and academic writing. This book also includes a coverage of data collection methods - from interviews to indirect observation to card sorts.

  16. PDF PhD Thesis Writing Process: A Systematic Approach—How to Write ...

    The methodology applied in this research was descriptive as it discusses and de-scribes the various parts of PhD literature writing process and explains the how to do of them in a very simple and understanding language (Faryadi, 2018). De-scriptive analysis is applied to explain the basic features of thesis writing process (García et al., 2015).

  17. (PDF) CHAPTER FIVE RESEARCH DESIGN AND METHODOLOGY 5.1. Introduction

    chapter five research design and methodology 5.1. Introduction Citation: Lelissa TB (2018); Research Methodology; University of South Africa, PHD Thesis December 2018

  18. PDF The Method Chapter

    structure of your study. For example, a dissertation by Macdonald (1990) examined the relationships among empathy, personal maturity, and emotional articulation. Macdonald described her design as follows: The research design was a correlational design utilizing cross-sectional survey methodology and includes a number of survey instruments. The pur-

  19. What are acceptable dissertation research methods?

    In order to write a dissertation, you must complete extensive, detailed research. Depending on your area of study, different types of research methods will be appropriate to complete your work. "The choice of research method depends on the questions you hope to answer with your research," says Curtis Brant, PhD, Capella University dean of ...

  20. How To Write A Dissertation Or Thesis

    Craft a convincing dissertation or thesis research proposal. Write a clear, compelling introduction chapter. Undertake a thorough review of the existing research and write up a literature review. Undertake your own research. Present and interpret your findings. Draw a conclusion and discuss the implications.

  21. PhD Thesis Writing Process: A Systematic Approach—How to Write Your

    PDF | On Jan 1, 2019, Qais Faryadi published PhD Thesis Writing Process: A Systematic Approach—How to Write Your Methodology, Results and Conclusion | Find, read and cite all the research you ...

  22. 10 powerful methodology courses for PhD students [online]

    Good knowledge of research methodology is a precondition for a successful PhD thesis. However, not all PhD students have access to methodology courses as part of their PhD programme. Fortunately, there are good options online, such as the following 10 powerful methodology online courses for PhD students provided via Coursera. Disclosure: This post contains affiliate

  23. Free Thesis Methodology Template (+ Examples)

    This template covers all the core components required in the methodology section of a typical dissertation, thesis or research paper, including: The opening section. Research philosophy. Research type. Research strategy. Time horizon. Sampling strategy. Data collection methods. Data analysis methods.

  24. PhD Dissertations

    Bayesian methods for variable selection : Anupreet Porwal: Abel Rodriguez, Adrian E Raftery: Statistical methods for genomic sequencing data : Alan Min: William Noble: Estimating subnational health and demographic indicators using complex survey data : Peter Gao: Jon Wakefield: Inference and Estimation for Network Data : Shane Lubold