qualitative for phd

How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

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What is a theoretical framework?

Developing a theoretical framework for your dissertation is one of the key elements of a qualitative research project. Through writing your literature review, you are likely to have identified either a problem that need ‘fixing’ or a gap that your research may begin to fill.

The theoretical framework is your toolbox . In the toolbox are your handy tools: a set of theories, concepts, ideas and hypotheses that you will use to build a solution to the research problem or gap you have identified.

The methodology is the instruction manual: the procedure and steps you have taken, using your chosen tools, to tackle the research problem.

Why do I need a theoretical framework?

Developing a theoretical framework shows that you have thought critically about the different ways to approach your topic, and that you have made a well-reasoned and evidenced decision about which approach will work best. theoretical frameworks are also necessary for solving complex problems or issues from the literature, showing that you have the skills to think creatively and improvise to answer your research questions. they also allow researchers to establish new theories and approaches, that future research may go on to develop., how do i create a theoretical framework for my dissertation.

First, select your tools. You are likely to need a variety of tools in qualitative research – different theories, models or concepts – to help you tackle different parts of your research question.  

When deciding what tools would be best for the job of answering your research questions or problem, explore what existing research in your area has used. You may find that there is a ‘standard toolbox’ for qualitative research in your field that you can borrow from or apply to your own research.

You will need to justify why your chosen tools are best for the job of answering your research questions, at what stage they are most relevant, and how they relate to each other. Some theories or models will neatly fit together and appear in the toolboxes of other researchers. However, you may wish to incorporate a model or idea that is not typical for your research area – the ‘odd one out’ in your toolbox. If this is the case, make sure you justify and account for why it is useful to you, and look for ways that it can be used in partnership with the other tools you are using.

You should also be honest about limitations, or where you need to improvise (for example, if the ‘right’ tool or approach doesn’t exist in your area).

This video from the Skills Centre includes an overview and example of how you might create a theoretical framework for your dissertation:

How do I choose the 'right' approach?

When designing your framework and choosing what to include, it can often be difficult to know if you’ve chosen the ‘right’ approach for your research questions. One way to check this is to look for consistency between your objectives, the literature in your framework, and your overall ethos for the research. This means ensuring that the literature you have used not only contributes to answering your research objectives, but that you also use theories and models that are true to your beliefs as a researcher.

Reflecting on your values and your overall ambition for the project can be a helpful step in making these decisions, as it can help you to fully connect your methodology and methods to your research aims.

Should I reflect on my position as a researcher?

If you feel your position as a researcher has influenced your choice of methods or procedure in any way, the methodology is a good place to reflect on this.  Positionality  acknowledges that no researcher is entirely objective: we are all, to some extent, influenced by prior learning, experiences, knowledge, and personal biases. This is particularly true in qualitative research or practice-based research, where the student is acting as a researcher in their own workplace, where they are otherwise considered a practitioner/professional. It's also important to reflect on your positionality if you belong to the same community as your participants where this is the grounds for their involvement in the research (ie. you are a mature student interviewing other mature learners about their experences in higher education). 

The following questions can help you to reflect on your positionality and gauge whether this is an important section to include in your dissertation (for some people, this section isn’t necessary or relevant):

• How might my personal history influence how I approach the topic?
• How am I positioned in relation to this knowledge? Am I being influenced by prior learning or knowledge from outside of this course?
• How does my gender/social class/ ethnicity/ culture influence my positioning in relation to this topic?
• Do I share any attributes with my participants? Are we part of a s hared community? How might this have influenced our relationship and my role in interviews/observations?
• Am I invested in the outcomes on a personal level? Who is this research for and who will feel the benefits?

One option for qualitative projects is to write an extended literature review. This type of project does not require you to collect any new data. Instead, you should focus on synthesising a broad range of literature to offer a new perspective on a research problem or question.  

The main difference between an extended literature review and a dissertation where primary data is collected, is in the presentation of the methodology, results and discussion sections. This is because extended literature reviews do not actively involve participants or primary data collection, so there is no need to outline a procedure for data collection (the methodology) or to present and interpret ‘data’ (in the form of interview transcripts, numerical data, observations etc.) You will have much more freedom to decide which sections of the dissertation should be combined, and whether new chapters or sections should be added.

Here is an overview of a common structure for an extended literature review:

Introduction

• Provide background information and context to set the ‘backdrop’ for your project.
• Explain the value and relevance of your research in this context. Outline what do you hope to contribute with your dissertation.
• Clarify a specific area of focus.
• Introduce your research aims (or problem) and objectives.

Literature review

You will need to write a short, overview literature review to introduce the main theories, concepts and key research areas that you will explore in your dissertation. This set of texts – which may be theoretical, research-based, practice-based or policies – form your theoretical framework. In other words, by bringing these texts together in the literature review, you are creating a lens that you can then apply to more focused examples or scenarios in your discussion chapters.

Methodology

As you will not be collecting primary data, your methodology will be quite different from a typical dissertation. You will need to set out the process and procedure you used to find and narrow down your literature. This is also known as a search strategy.

Including your search strategy

A search strategy explains how you have narrowed down your literature to identify key studies and areas of focus. This often takes the form of a search strategy table, included as an appendix at the end of the dissertation. If included, this section takes the place of the traditional 'methodology' section.

If you choose to include a search strategy table, you should also give an overview of your reading process in the main body of the dissertation.  Think of this as a chronology of the practical steps you took and your justification for doing so at each stage, such as:

• Your key terms, alternatives and synonyms, and any terms that you chose to exclude.
• Your choice and combination of databases;
• Your inclusion/exclusion criteria, when they were applied and why. This includes filters such as language of publication, date, and country of origin;
• You should also explain which terms you combined to form search phrases and your use of Boolean searching (AND, OR, NOT);
• Your use of citation searching (selecting articles from the bibliography of a chosen journal article to further your search).
• Your use of any search models, such as PICO and SPIDER to help shape your approach.
• Search strategy template A simple template for recording your literature searching. This can be included as an appendix to show your search strategy.

The discussion section of an extended literature review is the most flexible in terms of structure. Think of this section as a series of short case studies or ‘windows’ on your research. In this section you will apply the theoretical framework you formed in the literature review – a combination of theories, models and ideas that explain your approach to the topic – to a series of different examples and scenarios. These are usually presented as separate discussion ‘chapters’ in the dissertation, in an order that you feel best fits your argument.

Think about an order for these discussion sections or chapters that helps to tell the story of your research. One common approach is to structure these sections by common themes or concepts that help to draw your sources together. You might also opt for a chronological structure if your dissertation aims to show change or development over time. Another option is to deliberately show where there is a lack of chronology or narrative across your case studies, by ordering them in a fragmentary order! You will be able to reflect upon the structure of these chapters elsewhere in the dissertation, explaining and defending your decision in the methodology and conclusion.

A summary of your key findings – what you have concluded from your research, and how far you have been able to successfully answer your research questions.

• Recommendations – for improvements to your own study, for future research in the area, and for your field more widely.
• Emphasise your contributions to knowledge and what you have achieved.

Alternative structure

Depending on your research aims, and whether you are working with a case-study type approach (where each section of the dissertation considers a different example or concept through the lens established in your literature review), you might opt for one of the following structures:

Splitting the literature review across different chapters:

This structure allows you to pull apart the traditional literature review, introducing it little by little with each of your themed chapters. This approach works well for dissertations that attempt to show change or difference over time, as the relevant literature for that section or period can be introduced gradually to the reader.

Whichever structure you opt for, remember to explain and justify your approach. A marker will be interested in why you decided on your chosen structure, what it allows you to achieve/brings to the project and what alternatives you considered and rejected in the planning process. Here are some example sentence starters:

In qualitative studies, your results are often presented alongside the discussion, as it is difficult to include this data in a meaningful way without explanation and interpretation. In the dsicussion section, aim to structure your work thematically, moving through the key concepts or ideas that have emerged from your qualitative data. Use extracts from your data collection - interviews, focus groups, observations - to illustrate where these themes are most prominent, and refer back to the sources from your literature review to help draw conclusions. 

Here's an example of how your data could be presented in paragraph format in this section:

Example from  'Reporting and discussing your findings ', Monash University .

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PhD in Qualitative Research and Evaluation Methodologies

If you have a deep interest in the methodological, theoretical, and ethical procedures and challenges inherent to social science research and evaluation, this is a program for you. Our students contribute to the methodological and theoretical development of qualitative research and program evaluation.

Our program prepares research methodologists to study and develop theories and methods for conducting empirical and conceptual social science research and evaluation in education and other social science fields. Specifically, this program develops scholars and methodologists who are prepared to contribute to the advancement of innovative theories and methods used in qualitative research and program evaluation.

• Focus on qualitative methodologies with interdisciplinary topics
• Open to students with a variety of educational backgrounds and experiences
• Small cohorts support individual faculty attention and mentorship

Video: Overview of Programs Offered by the Qualitative Research Program

Video: Meet the Qualitative Research Faculty

The organization of the research and evaluation methods degree program recognizes the wide variety of specialties in which you might develop research agendas.

Our mission is to build your capacity to contribute methodological expertise to collaborative research efforts through real-world opportunities in which you develop and practice the skills needed in your area of emphasis. As a result of this experience, you will have a broad knowledge of research methods along with specific expertise in a focused methodology. You can use this knowledge to pursue careers as research methodologists and evaluation specialists in higher education, corporations, and non-profit agencies.

The Ph.D. degree is a 54-credit hour degree program in which students engage in advanced study of qualitative theories and methods, mixed methods, and approaches to evaluation.

• Core Coursework - 21 hours
• Research Seminar - 3 hours
• Elective Coursework - 18 hours
• Internship - 6 hours
• Doctoral hours Minimum of - 3 hours
• Dissertation Minimum of - 3 hours
• TOTAL - 54 hours

Part 1: Apply to the University of Georgia

The Graduate School handles admission for all graduate programs at the University of Georgia, including those in the College of Education. The Graduate School website contains important details about the application process, orientation, and many other useful links to guide you through the process of attending UGA at the graduate level.

Start A Graduate School Application

Part 2: Apply to the Ph.D. in Qualitative Research and Evaluation Methodologies

Please upload the following materials in your online application:

• Personal statement
• Curriculum vitae
• Writing sample (20-25 pages maximum)

Summary of Application Materials

Both the Graduate School and the academic department review application materials simultaneously. The Graduate School then reviews the department’s recommendation and makes the final determination on admission. As an applicant you will receive a formal letter regarding your admission from the Office of Graduate Admissions. You must be admitted to a program to be eligible to register for courses. Admission is granted for a specific semester and is validated by registration for that semester. Applicants must be admitted to the Graduate School before they are eligible to register. International applicants whose primary language is not English must submit scores from the TOEFL or IELTS tests in addition to a Certificate of Finances form. No application will be considered until all materials are received. To apply to the Ph.D. in Qualitative Research and Evaluation Methodologies, applicants must have completed a master’s degree. GRE scores are required for all applications.

Personal Statement

The program requires a personal statement, usually in the form of a letter of 2 to 3 pages, describing your background, work and research experience, interests in research and/or evaluation methodologies, and career aspirations. Specifically, you should be sure to address the following questions:

• What experience or background do you have in research methodologies?
• Why are you interested in the study of qualitative or evaluation methodologies?
• What research or evaluation questions do you hope to pursue, and why?
• In what way will the study of research or evaluation methodologies shape your career?

Writing Sample

You are required to submit a sample of formal writing (20-25 pages maximum). Scholarly or academic writing is preferred whether or not it has been published. If you have not published, recent course papers or work-related reports are appropriate.

Admissions Interviews

After a review of applications, selected applicants will be interviewed by the Ph.D. in Research and Evaluation Methodologies (REM) Admissions Committee early in the Spring semester for a subsequent Fall semester admission.

Deadline To Apply

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Additional Resources

Please use our online form if you have any questions for the department. Please be as specific as possible so that we may quickly assist you.

The College’s programs are taught by dedicated faculty who are experts in a range of areas and are passionate about helping students succeed both in their programs and professionally.

Meet the Faculty View Affiliated Faculty

Most graduate students at UGA are not assigned to a faculty advisor until after admittance. A close working relationship with your advisor is paramount to progressing through your program of study.

Almost all in-state students begin their studies at UGA paying limited tuition or fees. Please note that these amounts are subject to change and are meant to give prospective students an idea of the costs associated with a degree at the University of Georgia College of Education.

Students may qualify for a variety of assistantships, scholarships, and other financial awards to help offset the cost of tuition, housing, and other expenses.

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In this program, you will take focused coursework with individual attention from faculty mentors .

Each semester, you will also take part in a seminar that brings together faculty in the qualitative research program to discuss topics of relevance to scholarship and teaching in qualitative research methodologies in higher education.

See for yourself how much UGA College of Education has to offer! Schedule a tour of campus to learn more about the UGA student experience.

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Useful Links

• Affiliate Faculty
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• Graduate Handbook
• Summary of Materials Required by Area of Emphasis
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Testimonials

What first attracted me to the Qualitative Research and Evaluation Ph.D. program was the balance it offers between the philosophy of science and the practical application of research and evaluation. The program’s faculty members are amazing at supporting students’ learning, and at pushing us to be creative, deep thinking, and daring as we explore methodologies. Nuria Jaumot-Pascual, Doctoral Student

My path through the Qualitative Research and Evaluation Ph.D. program had me exploring the depths of qualitative research rarely visited by researchers in the health sciences but that have been essential in my current position as Senior Research Associate at Evidera. Now I find myself often referencing qualitative research theory and history when selecting strategies for new studies or when arranging trainings across my organization. Moreover, the breadth of interests across students in the program provided unique perspectives that enriched my journey. Most of all though, I am impressed with how well the faculty helped guide me through the program, making theory, history, and applied work relevant for my own career goals. Sean Halpin, Former Doctoral Student

I really discovered the world of qualitative research with the Qualitative Research and Evaluation Methodologies Ph.D. program, and the instructors made me adore it. Their support for the students’ learning was so impeccable. They had genial ways to encourage students to think outside the box and to be creative from every point of view, especially when it comes to methodologies. Bidossessi Mariano Ghislain Dossou Kpanou, Former Doctoral Student

• Section 2: Home
• Developing the Quantitative Research Design
• Qualitative Descriptive Design
• Design and Development Research (DDR) For Instructional Design
• Qualitative Narrative Inquiry Research
• Action Research Resource
• Case Study Design in an Applied Doctorate

Qualitative Research Designs

Case study design, using case study design in the applied doctoral experience (ade), applicability of case study design to applied problem of practice, case study design references.

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The field of qualitative research there are a number of research designs (also referred to as “traditions” or “genres”), including case study, phenomenology, narrative inquiry, action research, ethnography, grounded theory, as well as a number of critical genres including Feminist theory, indigenous research, critical race theory and cultural studies. The choice of research design is directly tied to and must be aligned with your research problem and purpose. As Bloomberg & Volpe (2019) explain:

Choice of research design is directly tied to research problem and purpose. As the researcher, you actively create the link among problem, purpose, and design through a process of reflecting on problem and purpose, focusing on researchable questions, and considering how to best address these questions. Thinking along these lines affords a research study methodological congruence (p. 38).

Case study is an in-depth exploration from multiple perspectives of a bounded social phenomenon, be this a social system such as a program, event, institution, organization, or community (Stake, 1995, 2005; Yin, 2018). Case study is employed across disciplines, including education, health care, social work, sociology, and organizational studies. The purpose is to generate understanding and deep insights to inform professional practice, policy development, and community or social action (Bloomberg 2018).

Yin (2018) and Stake (1995, 2005), two of the key proponents of case study methodology, use different terms to describe case studies. Yin categorizes case studies as exploratory or descriptive . The former is used to explore those situations in which the intervention being evaluated has no clear single set of outcomes. The latter is used to describe an intervention or phenomenon and the real-life context in which it occurred. Stake identifies case studies as intrinsic or instrumental , and he proposes that a primary distinction in designing case studies is between single and multiple (or collective) case study designs. A single case study may be an instrumental case study (research focuses on an issue or concern in one bounded case) or an intrinsic case study (the focus is on the case itself because the case presents a unique situation). A longitudinal case study design is chosen when the researcher seeks to examine the same single case at two or more different points in time or to capture trends over time. A multiple case study design is used when a researcher seeks to determine the prevalence or frequency of a particular phenomenon. This approach is useful when cases are used for purposes of a cross-case analysis in order to compare, contrast, and synthesize perspectives regarding the same issue. The focus is on the analysis of diverse cases to determine how these confirm the findings within or between cases, or call the findings into question.

Case study affords significant interaction with research participants, providing an in-depth picture of the phenomenon (Bloomberg & Volpe, 2019). Research is extensive, drawing on multiple methods of data collection, and involves multiple data sources. Triangulation is critical in attempting to obtain an in-depth understanding of the phenomenon under study and adds rigor, breadth, and depth to the study and provides corroborative evidence of the data obtained. Analysis of data can be holistic or embedded—that is, dealing with the whole or parts of the case (Yin, 2018). With multiple cases the typical analytic strategy is to provide detailed description of themes within each case (within-case analysis), followed by thematic analysis across cases (cross-case analysis), providing insights regarding how individual cases are comparable along important dimensions. Research culminates in the production of a detailed description of a setting and its participants, accompanied by an analysis of the data for themes or patterns (Stake, 1995, 2005; Yin, 2018). In addition to thick, rich description, the researcher’s interpretations, conclusions, and recommendations contribute to the reader’s overall understanding of the case study.

Analysis of findings should show that the researcher has attended to all the data, should address the most significant aspects of the case, and should demonstrate familiarity with the prevailing thinking and discourse about the topic. The goal of case study design (as with all qualitative designs) is not generalizability but rather transferability —that is, how (if at all) and in what ways understanding and knowledge can be applied in similar contexts and settings. The qualitative researcher attempts to address the issue of transferability by way of thick, rich description that will provide the basis for a case or cases to have relevance and potential application across a broader context.

Qualitative research methods ask the questions of "what" and "how" a phenomenon is understood in a real-life context (Bloomberg & Volpe, 2019). In the education field, qualitative research methods uncover educational experiences and practices because qualitative research allows the researcher to reveal new knowledge and understanding. Moreover, qualitative descriptive case studies describe, analyze and interpret events that explain the reasoning behind specific phenomena (Bloomberg, 2018). As such, case study design can be the foundation for a rigorous study within the Applied Doctoral Experience (ADE).

Case study design is an appropriate research design to consider when conceptualizing and conducting a dissertation research study that is based on an applied problem of practice with inherent real-life educational implications. Case study researchers study current, real-life cases that are in progress so that they can gather accurate information that is current. This fits well with the ADE program, as students are typically exploring a problem of practice. Because of the flexibility of the methods used, a descriptive design provides the researcher with the opportunity to choose data collection methods that are best suited to a practice-based research purpose, and can include individual interviews, focus groups, observation, surveys, and critical incident questionnaires. Methods are triangulated to contribute to the study’s trustworthiness. In selecting the set of data collection methods, it is important that the researcher carefully consider the alignment between research questions and the type of data that is needed to address these. Each data source is one piece of the “puzzle,” that contributes to the researcher’s holistic understanding of a phenomenon. The various strands of data are woven together holistically to promote a deeper understanding of the case and its application to an educationally-based problem of practice.

Research studies within the Applied Doctoral Experience (ADE) will be practical in nature and focus on problems and issues that inform educational practice.  Many of the types of studies that fall within the ADE framework are exploratory, and align with case study design. Case study design fits very well with applied problems related to educational practice, as the following set of examples illustrate:

Elementary Bilingual Education Teachers’ Self-Efficacy in Teaching English Language Learners: A Qualitative Case Study

The problem to be addressed in the proposed study is that some elementary bilingual education teachers’ beliefs about their lack of preparedness to teach the English language may negatively impact the language proficiency skills of Hispanic ELLs (Ernst-Slavit & Wenger, 2016; Fuchs et al., 2018; Hoque, 2016). The purpose of the proposed qualitative descriptive case study was to explore the perspectives and experiences of elementary bilingual education teachers regarding their perceived lack of preparedness to teach the English language and how this may impact the language proficiency of Hispanic ELLs.

Exploring Minority Teachers Experiences Pertaining to their Value in Education: A Single Case Study of Teachers in New York City

The problem is that minority K-12 teachers are underrepresented in the United States, with research indicating that school leaders and teachers in schools that are populated mainly by black students, staffed mostly by white teachers who may be unprepared to deal with biases and stereotypes that are ingrained in schools (Egalite, Kisida, & Winters, 2015; Milligan & Howley, 2015). The purpose of this qualitative exploratory single case study was to develop a clearer understanding of minority teachers’ experiences concerning the under-representation of minority K-12 teachers in urban school districts in the United States since there are so few of them.

Exploring the Impact of an Urban Teacher Residency Program on Teachers’ Cultural Intelligence: A Qualitative Case Study

The problem to be addressed by this case study is that teacher candidates often report being unprepared and ill-equipped to effectively educate culturally diverse students (Skepple, 2015; Beutel, 2018). The purpose of this study was to explore and gain an in-depth understanding of the perceived impact of an urban teacher residency program in urban Iowa on teachers’ cultural competence using the cultural intelligence (CQ) framework (Earley & Ang, 2003).

Qualitative Case Study that Explores Self-Efficacy and Mentorship on Women in Academic Administrative Leadership Roles

The problem was that female school-level administrators might be less likely to experience mentorship, thereby potentially decreasing their self-efficacy (Bing & Smith, 2019; Brown, 2020; Grant, 2021). The purpose of this case study was to determine to what extent female school-level administrators in the United States who had a mentor have a sense of self-efficacy and to examine the relationship between mentorship and self-efficacy.

Suburban Teacher and Administrator Perceptions of Culturally Responsive Teaching to Promote Connectedness in Students of Color: A Qualitative Case Study

The problem to be addressed in this study is the racial discrimination experienced by students of color in suburban schools and the resulting negative school experience (Jara & Bloomsbury, 2020; Jones, 2019; Kohli et al., 2017; Wandix-White, 2020). The purpose of this case study is to explore how culturally responsive practices can counteract systemic racism and discrimination in suburban schools thereby meeting the needs of students of color by creating positive learning experiences. 

As you can see, all of these studies were well suited to qualitative case study design. In each of these studies, the applied research problem and research purpose were clearly grounded in educational practice as well as directly aligned with qualitative case study methodology. In the Applied Doctoral Experience (ADE), you will be focused on addressing or resolving an educationally relevant research problem of practice. As such, your case study, with clear boundaries, will be one that centers on a real-life authentic problem in your field of practice that you believe is in need of resolution or improvement, and that the outcome thereof will be educationally valuable.

Bloomberg, L. D. (2018). Case study method. In B. B. Frey (Ed.), The SAGE Encyclopedia of educational research, measurement, and evaluation (pp. 237–239). SAGE. https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Fmethods.sagepub.com%2FReference%2Fthe-sage-encyclopedia-of-educational-research-measurement-and-evaluation%2Fi4294.xml

Bloomberg, L. D. & Volpe, M. (2019). Completing your qualitative dissertation: A road map from beginning to end . (4th Ed.). SAGE.

Stake, R. E. (1995). The art of case study research. SAGE.

Stake, R. E. (2005). Qualitative case studies. In N. K. Denzin and Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (3rd ed., pp. 443–466). SAGE.

Yin, R. (2018). Case study research and applications: Designs and methods. SAGE.

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Qualitative research essentials for medical education

Sayra m cristancho.

1 Department of Surgery and Faculty of Education, Schulich School of Medicine and Dentistry, Western University, Canada

2 Centre for Education Research and Innovation, Schulich School of Medicine and Dentistry, Western University, Canada

Mark Goldszmidt

3 Department of Medicine, Schulich School of Medicine and Dentistry, Western University, Canada

Lorelei Lingard

Christopher watling.

4 Postgraduate Medical Education, Schulich School of Medicine and Dentistry, Western University, Canada

This paper offers a selective overview of the increasingly popular paradigm of qualitative research. We consider the nature of qualitative research questions, describe common methodologies, discuss data collection and analysis methods, highlight recent innovations and outline principles of rigour. Examples are provided from our own and other authors’ published qualitative medical education research. Our aim is to provide both an introduction to some qualitative essentials for readers who are new to this research paradigm and a resource for more experienced readers, such as those who are currently engaged in a qualitative research project and would like a better sense of where their work sits within the broader paradigm.

INTRODUCTION

Are you a medical education researcher engaged in qualitative research and wondering if you are on the right track? Are you contemplating a qualitative research project and not sure how to get started? Are you reading qualitative manuscripts and making guesses about their quality? This paper offers a selective overview of the increasingly popular domain of qualitative research. We consider the nature of qualitative research questions, describe common methodologies, discuss data collection and analysis methods, highlight recent innovations, and outline principles of rigour. The aim of this paper is to educate newcomers through introductory explanations while stimulating more experienced researchers through attention to current innovations and emerging debates.

WHAT IS QUALITATIVE RESEARCH?

Qualitative research is naturalistic; the natural setting – not the laboratory – is the source of data. Researchers go where the action is; to collect data, they may talk with individuals or groups, observe their behaviour and their setting, or examine their artefacts.( 1 ) As defined by leading qualitative researchers Denzin and Lincoln, qualitative research studies social and human phenomena in their natural settings, attempting to make sense of or interpret these phenomena in terms of the meanings participants bring to them.( 2 )

Because qualitative research situates itself firmly in the world it studies, it cannot aim for generalisability. Its aim is to understand, rather than erase, the influence of context, culture and perspective. Good qualitative research produces descriptions, theory or conceptual understanding that may be usefully transferred to other contexts, but users of qualitative research must always carefully consider how the principles unearthed might unfold in their own distinct settings.

WHAT QUESTIONS ARE APPROPRIATE FOR QUALITATIVE RESEARCH?

Meaningful education research begins with compelling questions. Research methods translate curiosity into action, facilitating exploration of those questions. Methods must be chosen wisely; some questions lend themselves to certain methodological approaches and not to others.

In recent years, qualitative research methods have become increasingly prominent in medical education. The reason is simple: some of the most pressing questions in the field require qualitative approaches for meaningful answers to be found.

Qualitative research examines how things unfold in real world settings. While quantitative research approaches that dominate the basic and clinical sciences focus on testing hypotheses, qualitative research explores processes, phenomena and settings ( Box 1 ). For example, the question “Does the introduction of a mandatory rural clerkship increase the rate of graduates choosing to practise in rural areas? ” demands a quantitative approach. The question embeds a hypothesis – that a mandatory rural clerkship will increase the rate of graduates choosing to practise in rural areas – and so the research method must test whether or not that hypothesis is true. But the question “ How do graduating doctors make choices about their practice location? ” demands a qualitative approach. The question does not embed a hypothesis; rather, it explores a process of decision-making.

Qualitative research questions:

Many issues in medical education could be examined from either a quantitative or qualitative approach; one approach is not inherently superior. The questions that drive the research as well as the products that derive from it are, however, fundamentally different. Consider two approaches to studying the issue of online learning. A quantitative researcher might ask, “ What is the effect of an online learning module on medical students’ end-of-semester OSCE [objective structured clinical examination] scores? ”, while a qualitative researcher might ask “ How do medical students make choices about using online learning resources? ” Although the underlying issue is the same – the phenomenon of online learning in medical school – the studies launched by these questions and the products of those studies will look very different.

WHAT ARE QUALITATIVE METHODOLOGIES AND WHY ARE THEY IMPORTANT?

Executing rigorous qualitative research requires an understanding of methodology – the principles and procedures that define how the research is approached. Far from being monolithic, the world of qualitative research encompasses a range of methodologies, each with distinctive approaches to inquiry and characteristic products. Methodologies are informed by the researcher’s epistemology – that is, their theory of knowledge. Epistemology shapes how researchers approach the researcher’s role, the participant-researcher relationship, forms of data, analytical procedures, measures of research quality, and representation of results in analysis and writing.( 3 )

In medical education, published qualitative work includes methodologies such as grounded theory, phenomenology, ethnography, case study, discourse analysis, participatory action research and narrative inquiry, although the list is growing as the field embraces researchers with diverse disciplinary backgrounds. This paper neither seeks to exhaustively catalogue all qualitative methodologies nor comprehensively describe any of them. Rather, we present a subset, with the aim of familiarising readers with its fundamental goals. In this article, we briefly introduce four common methodologies used in medical education research ( Box 2 ). Using one topic, professionalism, we illustrate how each methodology might be applied and how its particular features would yield different insights into that topic.

Common qualitative methodologies in medical education:

Grounded theory

Arguably the most frequently used methodology in medical education research today, grounded theory seeks to understand social processes. Core features of grounded theory include iteration, in which data collection and analysis take place concurrently with each informing the other, and a reliance on theoretical sampling to explore patterns as they emerge.( 4 ) While many different schools of grounded theory exist, they share the aim of generating theory that is grounded in empirical data.( 5 ) Theory, in this type of research, can be thought of as a conceptual understanding of the process under study, ideally affording a useful explanatory power. For example, if one were interested in the development of professionalism among senior medical students during clerkship, one might design a grounded theory study around the following question: “ What aspects of clerkship support or challenge professional behaviour among senior medical students? ” The resulting product would be a conceptual rendering of how senior medical students navigate thorny professionalism issues, which might in turn be useful to curriculum planners.

Phenomenology

This methodology begins with a phenomenon of interest and seeks to understand the subjective lived experience of that phenomenon.( 6 ) Core features of phenomenology include a focus on the individual experience (typically pursued through in-depth interviewing and/or examinations of personal narratives), inductive analysis and a particular attention to reflexivity.( 7 ) Phenomenological researchers typically enumerate their own ideas and preconceptions about the phenomenon under study and consider how these perceptions might influence their interpretation of data.( 8 ) A phenomenological study around professionalism in senior medical students, for example, might involve interviewing several students who have experienced a professionalism lapse about that experience. The resulting product might be an enhanced understanding of the emotional, social and professional implications of this phenomenon from the student’s perspective, which might in turn inform wellness or resilience strategies.

Ethnography

Ethnography aims to understand people in their contexts, exploring the influence of culture, social organisation and shared values on how people behave – their routines and rituals. Core features of ethnography include reliance on direct observation as a data source, and the use of sustained immersive engagement in the setting of interest in order to understand social dynamics from within.( 9 , 10 ) An ethnographic approach to studying how professional attitudes develop in senior medical students might gather data through observations of ward rounds, team meetings and clinical teaching sessions over a period of time. The resulting product – called an ethnography – would describe how professional values are socialised in junior learners in clinical settings, which could assist educators in understanding how the clinical experiences they programme for their learners are influencing their professional development.

Case study research seeks an in-depth understanding of an individual case (or series of cases) that is illustrative of a problem of interest. Like clinical case studies, the goal is not generalisation but a thorough exploration of one case, in hopes that the fruits of that exploration may prove useful to others facing similar problems. Core features of case study research include: thoughtful bounding or defining of the scope of the case at the outset; collection of data from multiple sources, ranging from interviews with key players to written material in policy documents and websites; and careful attention to both the phenomenon of interest and its particular context.( 11 ) A specific professionalism challenge involving medical students could provide fodder for a productive case study. For example, if a medical school had to discipline several students for inappropriately sharing personal patient information on social media, a case study might be undertaken. The ‘case’ would be the incident of social media misuse at a single medical school, and the data gathered might include interviews with students and school officials, examination of relevant policy documents, examination of news media coverage of the event, and so on. The product of this research might trigger similar institutions to carefully consider how they might approach – or prevent – a similar problem.

As these four examples illustrate, methodology is the backbone of qualitative research. Methodology shapes the way the research question is asked, defines the characteristics of an appropriate sample, and governs the way the data collection and analysis procedures are organised. The researcher’s role is also distinctive in each methodology; for instance, in constructivist grounded theory, the researcher actively constructs the theory,( 12 ) while in phenomenology, the researcher attempts to manage his or her ‘pre-understandings’ through either bracketing them off or being reflexive about them.( 13 ) Interested readers may wish to consult the reference list for recently published examples of research using grounded theory,( 14 ) phenomenology,( 15 ) ethnography( 16 ) and case study approaches( 17 ) in order to appreciate how researchers deploy these methodologies to tackle compelling questions in contemporary medical education.

WHAT ARE SOME COMMON METHODS OF QUALITATIVE DATA COLLECTION?

The most common methods of qualitative data collection are interview – talking to participants about their experiences relevant to the research question, and observation – watching participants while they are having those experiences. Depending on the research questions explored, a research design might combine interviews and observations.

Interview-based methods

Interviews are typically used for situations where a guided conversation with relevant participants can help provide insight into their lived experiences and how they view and interpret the world around them. Interviews are also particularly useful for exploring past events that cannot be replicated or phenomena where direct observation is impossible or unfeasible.

Participants may be interviewed individually or in groups. Focus group interviews are used when the researcher’s topic of interest is best explored through a guided, interactive discussion among the participants themselves. Therefore, when focus groups are used, the sample is conceptualised at the level of the group – three focus groups of five people constitutes a sample of three interactive discussions, not 15 individual participants. Because they centre on the group discussion and dynamic, focus groups are less well-suited for topics that are sensitive, highly personal or perceived to be culturally inappropriate to discuss publicly.( 18 )

Unlike quantitative interviews, where a set of structured, closed-ended (e.g. yes/no) questions are asked in the same order with the same wording every time, qualitative interviews typically involve a semi-structured design where a list of open-ended questions serves to guide, but not constrain, the interview. Therefore, at the interviewer’s discretion, the questions and their sequence may vary from interview to interview. This judgement is made based on both the interviewer’s understanding of the phenomenon under exploration and the emerging dynamic between the interviewer and participant.

The primary goal of a qualitative interview is to get the participants to think carefully about their experience and relate it to the interviewer with rich detail. Getting good data from interviewing relies on using creative strategies to avoid the common trap of getting politically correct answers – often called ‘cover stories’– or answers that are superficial rather than deep and reflective.( 19 ) A common design error occurs when researchers are overly explicit in their questioning, such as asking “ What are the top five criteria you use to assess student professionalism? ” A better approach involves questions that ask participants to describe what they do in practice, with follow-up probes that extend beyond the specific experience described. For example, starting with “ Tell me about a recent experience where you assessed a student’s professionalism ” allows the participant to relay an experience, to which the interviewer can respond with probes such as “ What was tricky about that? ” or “ How typical is that experience? ”

Another common strategy for prompting participants to engage in rich reflection on their experience and perceptions is to use vignettes as discussion prompts. Vignettes are often artificial scenarios presented to participants to read or watch on video, about which they are then asked probing questions.( 20 ) However, vignettes can also be used to recreate an authentic situation for the participant to engage with.( 21 ) For instance, in one interview study, we presented participants with a vignette in the form of the research assistant reading aloud a standard patient admission presentation that the interviewees would typically hear from their students on morning ward rounds. We then asked the participants to interact with the interviewer as though he or she was a student who had presented this case on morning rounds. Recreating this interaction in the context of the interview served as a stepping stone to questions such as “ Why did you ask the student ‘x’? ” and “ How would your approach have differed with a different student presenter, e.g. a stronger or weaker one? ”

Direct observation

Observation-based research can involve a wide spectrum of activities, ranging from brief observations of specific tasks (e.g. handover, preoperative team briefings) to prolonged field observations such as those seen in ethnography. When used effectively, direct observation can provide the researcher with powerful insight into the routines of a group.

Getting good data from observational research relies on several key components. First, it is essential to define the scope of the project upfront: limited budgets, the massive amount of detail to be attended to, and the ability of any individual or group of observers to attend to these make this essential. Good observational research therefore relies on collaboration between knowledgeable insiders and those with both methodological and theoretical expertise. Sampling demands particular attention; an initial purposive sampling approach is often followed by more targeted, theoretical sampling that is guided by the developing analysis. Observational research also typically involves a mix of data sources, including observational field notes, field interviews and document analysis. Audio and video may be helpful when the studied phenomena is particularly complex or nuances of interaction may be missed without the ability to review data, or when precision of verbal and nonverbal interactions is necessary to answer the research question.( 22 )

Field notes are often the dominant data source used for subsequent analysis in observational research. As such, they must be created with great diligence. Usually researchers will jot down brief notes during an observation and afterwards elaborate in as much detail as they can recall. Field notes have an important reflective component. In addition to the factual descriptions, researchers include comments about their feelings, reactions, hunches, speculations and working theories or interpretations. The content of field notes, therefore, usually includes: descriptions of the setting, people and activities; direct quotations or paraphrasing of what people said; and the observer’s reflections.( 23 ) Field notes are time-consuming when done well – even a single hour of observation can lead to several hours of reflective documentation.

An important aspect to consider when designing observation-based research is the ‘observer effect’, also known as the Hawthorne effect, more recently reframed as ‘participant reactivity’ by health professions education researchers Paradis and Sutkin.( 24 ) The Hawthorne effect is conventionally defined as “ when observed participants act differently from how they would act if the observer were not present ”.( 25 ) Researchers have implemented a number of strategies to mitigate this effect, including prolonged embedding of the observer, efforts to ‘fit in’ through dress or comportment, and careful recording of explicit instances of the effect.( 24 ) However, Paradis and Sutkin found that instances of the Hawthorne effect, as conventionally defined, have never been described in qualitative research manuscripts in the health professions education field, perhaps because, as they speculate, healthcare workers and trainees are accustomed to being observed. Based on this, they argued that researchers should worry less about mitigating the Hawthorne effect and instead invest in interpersonal relationships at their study site to mitigate the effects of altered behaviour and draw on theory to make sense of participants’ altered behaviour.( 23 ) Combining interviewing and observation is also common in qualitative research ( Box 3 ).

Combining interviews and observations:

WHAT ARE THE COMMON METHODS OF QUALITATIVE DATA ANALYSIS?

Qualitative data almost invariably takes the form of text; an interview is turned into a transcript and an observation is rendered into a field note. Analysing these qualitative texts is about uncovering meaning, developing understanding and discovering insights relevant to the research question. Analysis is not separated from data collection in qualitative research, and begins with the first interview, the first observation or the first reading of a document. In fact, the iterative nature of data collection and analysis is a hallmark of qualitative research, because it allows the researcher’s emerging insights about the study phenomena to inform subsequent rounds of data collection ( Box 4 ).

The iterative process of analysis:

Data that has been analysed while being collected is both parsimonious and illuminating. However, this process can extend indefinitely. There will always be another person to interview or another observation to record. Deciding when to stop depends on both practical and theoretical concerns. Practical concerns include deadlines and funding. More importantly, the decision should be guided by the theoretical concern of sufficiency.( 26 ) Sufficiency occurs when new data does not produce new insights into the phenomenon, in other words, when you keep hearing and seeing the same things you have heard and seen before.

Qualitative data analysis is primarily inductive and comparative. The overall process of data analysis begins by identifying segments in the data that are responsive to the research question. The next step is to compare one segment with the next, looking for recurring patterns in the data set. During this step, the focus is on sorting the raw data into categories that progressively build a coherent description or explanation of the phenomenon under study. This process of identifying pieces of data and grouping them into categories is called coding.( 14 ) Once a tentative scheme of categories is derived, it is applied to new data to see whether those categories continue to exist or not, or whether new categories arise – this step determines whether sufficiency has been reached. The final step in the analysis is to think about how categories interrelate. At this point, the analysis moves to interpreting the meaning of these categories and their interrelations.( 12 )

The process for data analysis laid out in this section is a basic inductive and comparative analysis strategy that is suitable for analysing data for most interpretive qualitative research methodologies, including the four featured in this paper – phenomenology, grounded theory, ethnography and case study – as well as others such as narrative analysis and action research. While each methodology attends to specific procedures, they all share the use of this basic inductive/comparative strategy. Overall, analysis should be guided by methodology, but different analytical procedures can be creatively combined across methodologies, as long as this combining is explicit and intentional.( 27 )

WHAT ARE SOME CURRENT INNOVATIONS IN QUALITATIVE RESEARCH?

Understanding the complex factors that influence clinical practice and medical education is not an easy research task. Many important issues may be difficult for the insider to articulate during interviews and impossible for the outsider to ‘see’ during observation. Innovations to address these challenges include guided walks,( 28 ) photovoice( 29 ) and point-of-view filming.( 30 ) Our own research has drawn intensively on the innovation termed ‘rich pictures’ to explore the features and implications of complexity in medical education.( 31 ) In one study, we asked medical students to draw pictures of clinical cases that they found complex: an exciting case and a frustrating one.( 32 ) Participants were given 30–60 minutes on their own to reflect on the situation and draw their pictures. This was followed by an in-depth interview using the pictures as triggers to explore the phenomenon under study – in this case, students’ experiences of and responses to complexity during their training.

Such innovations hold great promise for qualitative research in medical education. For instance, rich pictures can reveal emotional and organisational dimensions of complex clinical experiences, which are less likely to be emphasised in participants’ traditional interview responses.( 33 ) Methodological innovations, however, bring new challenges: they can be time-intensive for participants and researchers; they require new analytical procedures to be developed; and they necessitate efforts to educate audiences about the rigour and credibility of unfamiliar approaches.

WHAT ARE THE PRINCIPLES OF RIGOUR IN QUALITATIVE RESEARCH?

Like quantitative research, qualitative research has principles of rigour that are used to judge the quality of the work.( 34 ) Here, we discuss principles that appear in most criteria for rigour in the field: reflexivity, adequacy, authenticity, trustworthiness and resonance ( Box 5 ).

Principles of rigour in qualitative research:

The main data collection tool in qualitative research is the researcher. We talk to participants, observe their practices and interpret their documents. Consequently, a critical feature of rigour in qualitative data collection is researcher reflexivity: the ability to consider our own orientations towards the studied phenomenon, acknowledge our assumptions and articulate regularly our impressions of the data.( 35 ) Only this way can we assure others that our subjectivity has been thoughtfully considered and afford them the ability to judge its influence on the work for themselves. Qualitative research does not seek to remove this subjectivity; it treats research perspective as unavoidable and enriching, not as a form of bias to purge.

Every qualitative dataset is an approximation of a complex phenomenon – no study can capture all dimensions and nuances of situated social experiences, such as medical students’ negotiations of professional dilemmas in the clinical workplace. Therefore, two other important criteria of rigour relate to the adequacy and authenticity of the sampled experiences. Did the research focus on the appropriate participants and/or situations? Was the size and scope of the sample adequate to represent the scope of the phenomenon?( 36 ) Was the data collected an authentic reflection of the phenomenon in question? Qualitative researchers should thoughtfully combine different perspectives, methods and data sources (a process called ‘triangulation’) to intensify the richness of their representation.( 37 ) We should endeavour to draw on data in our written reports such that we provide what sociologist Geertz has termed a sufficiently ‘thick’ description( 38 ) for readers to judge the authenticity of our portrayal of the studied phenomenon.

Qualitative analysis embraces subjectivity: what the researcher ‘sees’ in the data is a product both of what participants told or showed us and of what we were oriented to make of those stories and situations. To some degree, a rhetorician will always see rhetoric and a systems engineer will always see systems. To fulfil the rigour criteria of trustworthiness, qualitative analysis should also be systematic and held to a principle of trustworthiness, which dictates that we should clearly describe: (a) what was done by whom during the inductive, comparative analytical process; (b) how the perspectives of multiple coders were negotiated; (c) how and when theoretical lenses were brought to bear in the iterative process of data collection and analysis; and (d) how discrepant instances in the data – those that fell outside the dominant thematic patterns – were handled.

Finally, the ultimate measure of quality in qualitative research is the resonance of the final product to those who live the social experience under study.( 4 ) As qualitative researchers presenting our work at conferences, we know we have met this bar if our audiences laugh, nod or scowl at the right moments, and if their response at the end is “ You nailed it. That’s my world. But you’ve given me a new way to look at it ”. The situatedness of qualitative research means that its transferability to other contexts is always a matter of the listener/reader’s judgement, based on their consideration of the similarities and differences between the research context and their own. Thus, there is a necessity for qualitative research to sufficiently describe its context, so that consumers of the work have the necessary information to gauge transferability. Ultimately, though, transferability remains an open question, requiring further inquiry to explore the explanatory power of one study’s insights in a new setting.

WHAT ELSE IS THERE TO KNOW?

This overview of qualitative research in medical education is not exhaustive. We have been purposefully selective, discussing in depth some common methodologies and methods, and leaving aside others. We have also passed over important issues such as qualitative research ethics, sampling and writing. There is much, much more for readers to know! Our selectivity notwithstanding, we hope that this paper will provide an accessible introduction to some qualitative essentials for readers who are new to this research domain, and that it may serve as a useful resource for more experienced readers, particularly those who are doing a qualitative research project and would like a better sense of where their work sits within the broader field of qualitative approaches.

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Phd research methods: qualitative research.

In a nutshell, qualitative research is the method of using particular case studies and small, focused samples. Any broader ‘conclusions’ drawn using these methods are treated as ‘informed assertions’.

So just what kind of uses would qualitative research have? Well, let’s take the example of market research – you can test a product on a small focus group and from their response you can make changes to the product you are attempting to market. Or, perhaps, let’s look at psychology – many famous psychological theories begin with an initial case study – ever heard of the Little Albert Experiment? This uses just one child to show classical conditioning in humans.

But what are the different types of qualitative research methods? After all, we’ve already listed two that seem quite distinct.

The different types of qualitative research methods

Each of these methods has its advantages and disadvantages; being a participant increases the chance of seeing things as they really are, rather than as shown to an outsider, but it can also increase personal bias and distract from the role as researcher. With non-participatory observation, you are much less likely to experience bias, but you may miss some of the vital things you are looking out for.

#2 Questionnaires Questionnaires require much less involvement on the researcher’s part than some of these other methods (for example, observation) which means they can spend more time on the setup and results. These can be useful when there are particular responses desired (such as answers to ‘how do you feel about this product’) but does limit what data you end up with (as it’s only what you ask). As with any method, this too has advantages and disadvantages – you can ensure you’re getting the relevant data, but you are also opening up the possibility of biasing the subject in your questions.

#3 Other Written Materials In this case, it’s not handing out documents to be filled in, but looking at ones already written. This can be any kind of document, depending on your goal. This is particularly useful for subjects such as anthropology, where you can learn much about a group by their writings about themselves. That said, it can be used in many other places, psychology studies may often get you to keep a diary, for instance. This qualitative research method is useful because you can learn a lot from a variety of settings, and, if the materials are pre-created, then you don't have the problem of your presence changing how the group acts. However, interpreting them can be difficult, especially if they’re from a different culture or in another language.

#4 Sampling Sampling is the method of picking out a small group to focus on out of a larger one. Say we were studying students at a university, we cannot study all of them, so we may choose around 50 to focus on. Sampling can be complex as you want to ensure that your chosen sample is diverse meaning they can’t all be the same age, gender, ethnicity, orientation, and so on, unless of course one of these is the factor you’re studying. Sampling has the advantage of making it easier to study the group in question, and as you can manually choose a sample it does have the advantage of being able to fine tune the variables. Flaws, however, include the fact that a sample is just a sample, and may not be as representative as you hoped, especially if it’s a sample that people volunteer for as this will heavily bias the results towards the type of people who volunteer for studies.

#5 Interviews Interviews are a great way of talking one on one, or to small groups of people. This method is often useful when starting a project, to find out general trends or to get a feel for what you’re studying. It can also be useful later on, when wanting to ask specific things. However, if you are using interviews, you’ll have to be very aware of your methods and you need to be in control of everything. No leading questions, or closed off body language here.

The good thing about interviews is the chance to really ask questions, to get to know the person you’re talking to, and to observe the answers, rather than just reading them. The downside is the possibility of you influencing the answers accidentally.

These are the main methods used in qualitative research, and you’ll have seen them or variants thereof used in most social science textbooks.

Useful links:

Quantitative Research

Qualitative Vs Quantitative Research

Social Sciences

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PHD, Qualitative Research & Evaluation Methodologies

If you have a deep interest in the methodological, theoretical, and ethical procedures and challenges inherent to social science research and evaluation, this is a program for you. Our graduates contribute to the field of qualitative inquiry.

Degree Type: Doctoral

Degree Program Code: PHD_QREM

Degree Program Summary:

The organization of the QREM degree program recognizes the wide variety of specialties in which students might develop research agendas. Therefore, our mission is to build students’ capacities to contribute methodological expertise to collaborative research efforts through real-world opportunities in which they develop and practice the skills needed in their area of specialization. Through their research in a variety of substantive fields, students will contribute to the development of innovative theory and methods employed in research and evaluation methodologies. As a result of the QREM program experience, students will a have broad knowledge of research methods along with specific expertise in a focused methodology, which they can use to pursue careers as research methodologists and evaluation specialists in higher education as well as corporate and non-profit agencies.

Program of Study: Ph.D. in Qualitative Research and Evaluation Methodologies

• Core Coursework – 18 hours
• Area of Specialization – 12 hours
• Research Seminar – 3 hours
• Elective Coursework in Research Methods – 12 hours
• Cognate in Area of Interest – 9 hours
• Doctoral Directed Study Hours – Minimum of 3 hours
• Dissertation Hours – Minimum of 3 hours
• Total – 60 hours

The purpose of the Ph.D. degree program in Qualitative Research and Evaluation Methodologies (QREM) at The University of Georgia is to prepare researchers and research methodologists to study and develop methods for conducting empirical and conceptual social science research in education and other social science fields. The graduate experience in the QREM degree program consists of focused coursework and individual supervised research with faculty mentors leading to expertise in one of two methodological areas of emphasis: (1) qualitative research; or (2) program evaluation. All students are expected to complete an initial study of the traditions commonly used in the social sciences—qualitative, quantitative, mixed, and evaluation methods. Additionally, students study the philosophical, theoretical, and conceptual aspects of an array of research designs through a course entitled “The History and Philosophy of Social Science Research Methods,” as well as develop proficiency in writing for a variety of audiences in “Writing and Reporting Academic Research and Evaluation.”

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Ph.D. in Qualitative and Quantitative Research Methodology

Qualitative and quantitative research methodology, (formerly ph.d. in inquiry methodology).

This unique program enables students to focus on quantitative research, qualitative research, or an integrated program of study.

The flexible curriculum enables you to delve deeply into your chosen area of interest, from statistical modeling to ethnography, from discourse and narrative analysis to psychometrics and assessment.

Yet our program is rigorous enough to ensure that all graduates are able to meaningfully contribute to the study of social and behavioral research.

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The Graduate Studies Office will accept unofficial transcripts and self-reported test scores for admission reviews. Any admission made with these documents would be conditioned on receipt of official documents, which should be provided as soon as possible.

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• Bachelor’s degree from an accredited institution
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• Minimum 79 TOEFL score or minimum 6.5 IELTS score or minimum 115 Duolingo score (international students only)

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Program Requirements

• Ph.D. in Qualitative and Quantitative Research Methodology – (formerly Ph.D. in Inquiry Methodology) Requirements

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2023-2024 Academic Year

2024-2025 academic year.

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Qualifying Examination

At the completion of course work and before the dissertation, doctoral students specializing in Inquiry Methodology will need to pass a qualifying exam in the form of portfolio of work and an oral examination. This examination is tailored to the student's program of study. All students with a minor in education must also take a minor qualifying examination. Some departments outside of the School of Education waive the minor qualifying examination, under certain conditions.

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As a student you will have the opportunity to focus on methodology through theory and practice that cuts across a divide in qualitative and quantitative methods.

We are dedicated to advancing the understanding of social inquiry, especially with respect to the field of education, and we imagine these possibilities to be necessarily inclusive of methods typically disenfranchised from one another.

This 90-credit hour degree program requires students to spend at least two consecutive semesters on campus. Up to 30 credit hours may be transferred from another institution.

A 12-credit hour minor is also available to doctoral students majoring in other disciplines.

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7 Writing a proposal

• Published: May 2018
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When researchers plan to undertake qualitative research with a pilot or full RCT they write a proposal to apply for funding, seek ethical approval, or as part of their PhD studies. These proposals can be published in journals. Guidance for writing a proposal for the qualitative research undertaken with RCTs has been published, and there is existing guidance for writing proposals in related areas such as mixed methods research. In this chapter, existing guidance is introduced and built upon to offer comprehensive and detailed guidance for writing a proposal for the qualitative research undertaken with an RCT. There are challenges to writing these proposals and these are discussed and potential solutions proposed.

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Designing a Research Proposal in Qualitative Research

• First Online: 27 October 2022

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• Md. Ismail Hossain 4 ,
• Nafiul Mehedi 4 &
• Iftakhar Ahmad 4  

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The chapter discusses designing a research proposal in qualitative research. The main objective is to outline the major components of a qualitative research proposal with example(s) so that the students and novice scholars easily get an understanding of a qualitative proposal. The chapter highlights the major components of a qualitative research proposal and discusses the steps involved in designing a proposal. In each step, an example is given with some essential tips. Following these steps and tips, a novice researcher can easily prepare a qualitative research proposal. Readers, especially undergraduate and master’s students, might use this as a guideline while preparing a thesis proposal. After reading this chapter, they can easily prepare a qualitative proposal.

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Md. Ismail Hossain, Nafiul Mehedi & Iftakhar Ahmad

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Hossain, M.I., Mehedi, N., Ahmad, I. (2022). Designing a Research Proposal in Qualitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_18

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Doctor of Philosophy in Education

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The Harvard Ph.D. in Education trains cutting-edge researchers who work across disciplines to generate knowledge and translate discoveries into transformative policy and practice.

Offered jointly by the Harvard Graduate School of Education and the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences, the Ph.D. in Education provides you with full access to the extraordinary resources of Harvard University and prepares you to assume meaningful roles as university faculty, researchers, senior-level education leaders, and policymakers.

As a Ph.D. candidate, you will collaborate with scholars across all Harvard graduate schools on original interdisciplinary research. In the process, you will help forge new fields of inquiry that will impact the way we teach and learn. The program’s required coursework will develop your knowledge of education and your expertise in a range of quantitative and qualitative methods needed to conduct high-quality research. Guided by the goal of making a transformative impact on education research, policy, and practice, you will focus on independent research in various domains, including human development, learning and teaching, policy analysis and evaluation, institutions and society, and instructional practice.   

Curriculum Information

The Ph.D. in Education requires five years of full-time study to complete. You will choose your individual coursework and design your original research in close consultation with your HGSE faculty adviser and dissertation committee. The requirements listed below include the three Ph.D. concentrations: Culture, Institutions, and Society; Education Policy and Program Evaluation; and Human Development, Learning and Teaching . 

We invite you to review an example course list, which is provided in two formats — one as the full list by course number and one by broad course category . These lists are subject to modification. 

Ph.D. Concentrations and Examples

Summary of Ph.D. Program

Doctoral Colloquia  In year one and two you are required to attend. The colloquia convenes weekly and features presentations of work-in-progress and completed work by Harvard faculty, faculty and researchers from outside Harvard, and Harvard doctoral students. Ph.D. students present once in the colloquia over the course of their career.

Research Apprenticeship The Research Apprenticeship is designed to provide ongoing training and mentoring to develop your research skills throughout the entire program.

Teaching Fellowships The Teaching Fellowship is an opportunity to enhance students' teaching skills, promote learning consolidation, and provide opportunities to collaborate with faculty on pedagogical development.

Comprehensive Exams  The Written Exam (year 2, spring) tests you on both general and concentration-specific knowledge. The Oral Exam (year 3, fall/winter) tests your command of your chosen field of study and your ability to design, develop, and implement an original research project.

Dissertation  Based on your original research, the dissertation process consists of three parts: the Dissertation Proposal, the writing, and an oral defense before the members of your dissertation committee.

Culture, Institutions, and Society (CIS) Concentration

In CIS, you will examine the broader cultural, institutional, organizational, and social contexts relevant to education across the lifespan. What is the value and purpose of education? How do cultural, institutional, and social factors shape educational processes and outcomes? How effective are social movements and community action in education reform? How do we measure stratification and institutional inequality? In CIS, your work will be informed by theories and methods from sociology, history, political science, organizational behavior and management, philosophy, and anthropology. You can examine contexts as diverse as classrooms, families, neighborhoods, schools, colleges and universities, religious institutions, nonprofits, government agencies, and more.

Education Policy and Program Evaluation (EPPE) Concentration

In EPPE, you will research the design, implementation, and evaluation of education policy affecting early childhood, K–12, and postsecondary education in the U.S. and internationally. You will evaluate and assess individual programs and policies related to critical issues like access to education, teacher effectiveness, school finance, testing and accountability systems, school choice, financial aid, college enrollment and persistence, and more. Your work will be informed by theories and methods from economics, political science, public policy, and sociology, history, philosophy, and statistics. This concentration shares some themes with CIS, but your work with EPPE will focus on public policy and large-scale reforms.

Human Development, Learning and Teaching (HDLT) Concentration

In HDLT, you will work to advance the role of scientific research in education policy, reform, and practice. New discoveries in the science of learning and development — the integration of biological, cognitive, and social processes; the relationships between technology and learning; or the factors that influence individual variations in learning — are transforming the practice of teaching and learning in both formal and informal settings. Whether studying behavioral, cognitive, or social-emotional development in children or the design of learning technologies to maximize understanding, you will gain a strong background in human development, the science of learning, and sociocultural factors that explain variation in learning and developmental pathways. Your research will be informed by theories and methods from psychology, cognitive science, sociology and linguistics, philosophy, the biological sciences and mathematics, and organizational behavior.

Program Faculty

The most remarkable thing about the Ph.D. in Education is open access to faculty from all Harvard graduate and professional schools, including the Harvard Graduate School of Education, the Faculty of Arts and Sciences, the Harvard Kennedy School, the Harvard Law School, Harvard Medical School, and the Harvard School of Public Health. Learn about the full Ph.D. Faculty.

Jarvis R. Givens

Jarvis Givens studies the history of American education, African American history, and the relationship between race and power in schools.

Paul L. Harris

Paul Harris is interested in the early development of cognition, emotion, and imagination in children.

Meira Levinson

Meira Levinson is a normative political philosopher who works at the intersection of civic education, youth empowerment, racial justice, and educational ethics. 

Luke W. Miratrix

Luke Miratrix is a statistician who explores how to best use modern statistical methods in applied social science contexts.

Eric Taylor

Eric Taylor studies the economics of education, with a particular interest in employer-employee interactions between schools and teachers — hiring and firing decisions, job design, training, and performance evaluation.

Paola Uccelli

Paola Ucelli studies socio-cultural and individual differences in the language development of multilingual and monolingual students.

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Dissertations.

The following is a complete listing of successful Ph.D. in Education dissertations to-date. Dissertations from November 2014 onward are publicly available in the Digital Access to Scholarship at Harvard (DASH) , the online repository for Harvard scholarship.

• 2022 Graduate Dissertations (265 KB pdf)
• 2021 Graduate Dissertations (177 KB pdf)
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• 2019 Graduate Dissertations (68.3 KB pdf)

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An opt-in listing of current Ph.D. students with information about their interests, research, personal web pages, and contact information:

Doctor of Philosophy in Education Student Directory

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Program Highlights

Explore examples of the Doctor of Philosophy in Education experience and the impact its community is making on the field:

The Human Element of Data and AI

Gahyun Callie Sung's journey to HGSE and the LIT Lab is reflected in her research into data and using AI to improve student outcomes

Improving the Teacher Workforce

With her research work, doctoral marshal Mary Laski, Ph.D.'24, is trying to make teaching in K–12 schools more sustainable and attractive