sample of qualitative research thesis

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Tips for a qualitative dissertation

Veronika Williams

17 October 2017

Tips for students

This blog is part of a series for Evidence-Based Health Care MSc students undertaking their dissertations.

Undertaking an MSc dissertation in Evidence-Based Health Care (EBHC) may be your first hands-on experience of doing qualitative research. I chatted to Dr. Veronika Williams, an experienced qualitative researcher, and tutor on the EBHC programme, to find out her top tips for producing a high-quality qualitative EBHC thesis.

1) Make the switch from a quantitative to a qualitative mindset

It’s not just about replacing numbers with words. Doing qualitative research requires you to adopt a different way of seeing and interpreting the world around you. Veronika asks her students to reflect on positivist and interpretivist approaches: If you come from a scientific or medical background, positivism is often the unacknowledged status quo. Be open to considering there are alternative ways to generate and understand knowledge.

2) Reflect on your role

Quantitative research strives to produce “clean” data unbiased by the context in which it was generated.  With qualitative methods, this is neither possible nor desirable.  Students should reflect on how their background and personal views shape the way they collect and analyse their data. This will not only add to the transparency of your work but will also help you interpret your findings.

3)  Don’t forget the theory

Qualitative researchers use theories as a lens through which they understand the world around them. Veronika suggests that students consider the theoretical underpinning to their own research at the earliest stages. You can read an article about why theories are useful in qualitative research  here.

4) Think about depth rather than breadth

Qualitative research is all about developing a deep and insightful understanding of the phenomenon/ concept you are studying. Be realistic about what you can achieve given the time constraints of an MSc.  Veronika suggests that collecting and analysing a smaller dataset well is preferable to producing a superficial, rushed analysis of a larger dataset.

5) Blur the boundaries between data collection, analysis and writing up

Veronika strongly recommends keeping a research diary or using memos to jot down your ideas as your research progresses. Not only do these add to your audit trail, these entries will help contribute to your first draft and the process of moving towards theoretical thinking. Qualitative researchers move back and forward between their dataset and manuscript as their ideas develop. This enriches their understanding and allows emerging theories to be explored.

6) Move beyond the descriptive

When analysing interviews, for example, it can be tempting to think that having coded your transcripts you are nearly there. This is not the case!  You need to move beyond the descriptive codes to conceptual themes and theoretical thinking in order to produce a high-quality thesis.  Veronika warns against falling into the pitfall of thinking writing up is, “Two interviews said this whilst three interviewees said that”.

7) It’s not just about the average experience

When analysing your data, consider the outliers or negative cases, for example, those that found the intervention unacceptable.  Although in the minority, these respondents will often provide more meaningful insight into the phenomenon or concept you are trying to study.

8) Bounce ideas

Veronika recommends sharing your emerging ideas and findings with someone else, maybe with a different background or perspective. This isn’t about getting to the “right answer” rather it offers you the chance to refine your thinking.  Be sure, though, to fully acknowledge their contribution in your thesis.

9) Be selective

In can be a challenge to meet the dissertation word limit.  It won’t be possible to present all the themes generated by your dataset so focus! Use quotes from across your dataset that best encapsulate the themes you are presenting.  Display additional data in the appendix.  For example, Veronika suggests illustrating how you moved from your coding framework to your themes.

10) Don’t panic!

There will be a stage during analysis and write up when it seems undoable.  Unlike quantitative researchers who begin analysis with a clear plan, qualitative research is more of a journey. Everything will fall into place by the end.  Be sure, though, to allow yourself enough time to make sense of the rich data qualitative research generates.

Related course:

Qualitative research methods.

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Dissertations and research projects

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• Planning your research

Developing a theoretical framework

Reflecting on your position, extended literature reviews, presenting qualitative data.

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• Writing up your research project
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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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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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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

Prevent plagiarism. Run a free check.

To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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Qualitative vs. Quantitative: Key Differences in Research Types

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Let's say you want to learn how a group will vote in an election. You face a classic decision of gathering qualitative vs. quantitative data.

With one method, you can ask voters open-ended questions that encourage them to share how they feel, what issues matter to them and the reasons they will vote in a specific way. With the other, you can ask closed-ended questions, giving respondents a list of options. You will then turn that information into statistics.

Neither method is more right than the other, but they serve different purposes. Learn more about the key differences between qualitative and quantitative research and how you can use them.

What Is Qualitative Research?

What is quantitative research, qualitative vs. quantitative research: 3 key differences, benefits of combining qualitative and quantitative research.

Qualitative research aims to explore and understand the depth, context and nuances of human experiences, behaviors and phenomena. This methodological approach emphasizes gathering rich, nonnumerical information through methods such as interviews, focus groups , observations and content analysis.

In qualitative research, the emphasis is on uncovering patterns and meanings within a specific social or cultural context. Researchers delve into the subjective aspects of human behavior , opinions and emotions.

This approach is particularly valuable for exploring complex and multifaceted issues, providing a deeper understanding of the intricacies involved.

Common qualitative research methods include open-ended interviews, where participants can express their thoughts freely, and thematic analysis, which involves identifying recurring themes in the data.

Examples of How to Use Qualitative Research

The flexibility of qualitative research allows researchers to adapt their methods based on emerging insights, fostering a more organic and holistic exploration of the research topic. This is a widely used method in social sciences, psychology and market research.

Here are just a few ways you can use qualitative research.

• To understand the people who make up a community : If you want to learn more about a community, you can talk to them or observe them to learn more about their customs, norms and values.
• To examine people's experiences within the healthcare system : While you can certainly look at statistics to gauge if someone feels positively or negatively about their healthcare experiences, you may not gain a deep understanding of why they feel that way. For example, if a nurse went above and beyond for a patient, they might say they are content with the care they received. But if medical professional after medical professional dismissed a person over several years, they will have more negative comments.
• To explore the effectiveness of your marketing campaign : Marketing is a field that typically collects statistical data, but it can also benefit from qualitative research. For example, if you have a successful campaign, you can interview people to learn what resonated with them and why. If you learn they liked the humor because it shows you don't take yourself too seriously, you can try to replicate that feeling in future campaigns.

Types of Qualitative Data Collection

Qualitative data captures the qualities, characteristics or attributes of a subject. It can take various forms, including:

• Audio data : Recordings of interviews, discussions or any other auditory information. This can be useful when dealing with events from the past. Setting up a recording device also allows a researcher to stay in the moment without having to jot down notes.
• Observational data : With this type of qualitative data analysis, you can record behavior, events or interactions.
• Textual data : Use verbal or written information gathered through interviews, open-ended surveys or focus groups to learn more about a topic.
• Visual data : You can learn new information through images, photographs, videos or other visual materials.

Quantitative research is a systematic empirical investigation that involves the collection and analysis of numerical data. This approach seeks to understand, explain or predict phenomena by gathering quantifiable information and applying statistical methods for analysis.

Unlike qualitative research, which focuses on nonnumerical, descriptive data, quantitative research data involves measurements, counts and statistical techniques to draw objective conclusions.

Examples of How to Use Quantitative Research

Quantitative research focuses on statistical analysis. Here are a few ways you can employ quantitative research methods.

• Studying the employment rates of a city : Through this research you can gauge whether any patterns exist over a given time period.
• Seeing how air pollution has affected a neighborhood : If the creation of a highway led to more air pollution in a neighborhood, you can collect data to learn about the health impacts on the area's residents. For example, you can see what percentage of people developed respiratory issues after moving to the neighborhood.

Types of Quantitative Data

Quantitative data refers to numerical information you can measure and count. Here are a few statistics you can use.

• Heights, yards, volume and more : You can use different measurements to gain insight on different types of research, such as learning the average distance workers are willing to travel for work or figuring out the average height of a ballerina.
• Temperature : Measure in either degrees Celsius or Fahrenheit. Or, if you're looking for the coldest place in the universe , you may measure in Kelvins.
• Sales figures : With this information, you can look at a store's performance over time, compare one company to another or learn what the average amount of sales is in a specific industry.

Quantitative and qualitative research methods are both valid and useful ways to collect data. Here are a few ways that they differ.

• Data collection method : Quantitative research uses standardized instruments, such as surveys, experiments or structured observations, to gather numerical data. Qualitative research uses open-ended methods like interviews, focus groups or content analysis.
• Nature of data : Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data.
• Sampling : Quantitative research involves larger sample sizes to ensure statistical validity and generalizability of findings to a population. With qualitative research, it's better to work with a smaller sample size to gain in-depth insights into specific contexts or experiences.

You can simultaneously study qualitative and quantitative data. This method , known as mixed methods research, offers several benefits, including:

• A comprehensive understanding : Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability.
• Contextualization : Qualitative data helps contextualize quantitative findings by providing explanations into the why and how behind statistical patterns. This deeper understanding contributes to more informed interpretations of quantitative results.
• Triangulation : Triangulation involves using multiple methods to validate or corroborate findings. Combining qualitative and quantitative data allows researchers to cross-verify results, enhancing the overall validity and reliability of the study.

This article was created in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor.

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Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

Fullstory's DXI platform combines the quantitative insights of product analytics with picture-perfect session replay for complete context that helps you answer questions, understand issues, and uncover customer opportunities.

Start a free 14-day trial to see how Fullstory can help you combine your most invaluable quantitative and qualitative insights and eliminate blind spots.

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Our team of experts is committed to introducing people to important topics surrounding analytics, digital experience intelligence, product development, and more.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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• Open access
• Published: 04 June 2024

Migraine self-management at work: a qualitative study

• Clara Knauf 1 ,
• Katherina Heinrichs 2 ,
• Rolf Süllwold 3 ,
• Andrea Icks 4 &
• Adrian Loerbroks 1  

Journal of Occupational Medicine and Toxicology volume  19 , Article number:  22 ( 2024 ) Cite this article

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Metrics details

Migraine is common and can be highly disabling. Adequate migraine self-management (SM) can mitigate the potentially adverse health effects of migraine. However, adequate SM can be challenging to implement in everyday life, for instance, at the workplace. We aimed to explore how migraine SM is carried out at work and which occupational factors may determine effective implementation according to employees with migraine. We also explored the potential impact of the COVID-19 pandemic and the associated re-arrangement of working conditions on migraine SM at work.

We conducted semi-structured qualitative interviews (08/2020–01/2021). Participants were eligible if they have worked for at least six months with a minimum of twenty hours per week and with a migraine diagnosis. The interviews were transcribed and content-analyzed using MAXQDA.

Migraine SM was perceived to be influenced by social interactions at work (e.g., in terms of understanding vs. stigmatization), the level of job decision latitude (JDL, i.e., the extent to which one is able to influence work processes, e.g., when working from home), and workplace design (e.g., in terms of opportunities to withdraw from work). During the COVID-19 pandemic, especially increased JDL appeared to favorably influence migraine SM.

Conclusions

Migraine SM at work is fostered or complicated by various psychosocial working conditions. By considering these facilitators and barriers, more migraine-friendly workplaces can be created.

Migraine is associated with a considerable burden in terms of its prevalence, related disability, and cost. The prevalence is especially high among people between the age of 36 and 46, who are in the midst of their occupational life [ 1 ]. It has been estimated that the European Union loses € 111 billion annually due to migraine [ 2 ]. As much as 93% of this amount is considered to be attributable to indirect cost, i.e., cost resulting from lost and reduced productivity at work [ 2 ].

To successfully manage their condition, individuals with migraine need to acquire and apply various skills (i.e., self-management [SM]). Five major areas of migraine self-management activities have been identified in prior studies: (1) utilizing the healthcare system, (2) taking medication adequately, (3) using alternative therapies (such as osteopathy, herbal, and homeopathic remedies), (4) requesting social support, and (5) self-care (e.g., trigger detection and avoidance, stress management, and a healthy lifestyle) [ 3 , 4 , 5 ].

Migraine SM can be hampered or facilitated by external factors in everyday life, for instance, at the workplace. With regard to the latter, the European Federation of Neurological Associations (EFNA) conducted a survey in 2020 inquiring individuals with migraine or other types of headaches ( n  = 167) from 20 European countries what their company should do to help them cope better with the effects of their health condition [ 6 ]. Proposed areas of improvement included (but were not limited to): more understanding by managers, more opportunities to work from home, private workspace, less social interaction, part-time work, and noise-cancelling headphones [ 6 ]. In the same vein, Scaratti et al. [ 7 ] used an online questionnaire to examine the needs of headache patients in Europe ( n  = 103) related to staying at or returning to work. Here, too, physical environment adaptions (such as single offices, rest rooms), work-related aspects (e.g., longer, and flexible breaks), and support at work (among other things, social support by supervisors and human resources) were mentioned [ 7 ].

While there is thus confirmatory preliminary evidence regarding headache SM, there is still a need to examine in depth if and how working conditions may affect successful implementation of specifically migraine SM (rather than headache management in general) at work. Migraine cannot be equated with headache, as headache is only one symptom of migraine, which is also usually accompanied by other symptoms such as nausea, vomiting, or sensitivity to light, and thus likely to lead to more impairment. We aimed to gain detailed insights into the types of working conditions that may facilitate or hamper the ability to self-manage migraine. Work arrangements have changed swiftly and dramatically for many employees during the COVID-19 pandemic and we therefore also sought to explore the potential effects of these re-arrangements on SM strategies.

We conducted a qualitative study using semi-structured interviews. Our report adheres to the Consolidated Criteria for Reporting Qualitative Research (COREQ) statement [ 8 ].

The primary researcher (CK) was a medical student at the time of our study. Qualitative research skills are usually not acquired during medical studies in Germany. CK conducted the study to obtain a German Doctor of Medicine (Dr. med.) degree, which is comparable (in terms of its scope and quality) with a master thesis rather than with a PhD degree. The general choice of self-management at work as the research topic was due to the foci of the research group headed by AL, who acted as CK’s thesis supervisor. It was CK’s wish to relate her research project to migraine, as she had several migraine patients in her personal environment. CK familiarized herself extensively with the methodology of qualitative research (especially through self-studies and online tutorials) and in particular with the coding process. Prior to the interviews and the analysis, CK was trained (e.g., in interviewing techniques) by KH who is an experienced qualitative researcher [ 9 , 10 ] (female, degrees in psychology and public health). Also, detailed feedback and opportunities for reflection were provided during the actual interviewing and subsequent analysis (see below).

Study population

We sought to recruit participants who met the following inclusion criteria: (1) participant-report that migraine has ever been diagnosed by a physician and (2) employment with the migraine diagnosis for at least six months with a minimum of 20 working hours per week. Study participants were recruited via three different pathways: an inpatient rehabilitation clinic for headache disorders (Berolina Klinik, Löhne, Germany), migraine self-help groups from different regions in Germany, and private contacts of members of the study team. In the run-up to the interviews all potential participants received information on the objectives of the study, the professional background of the interviewer (CK) and the inclusion criteria (either via a flyer or from CK in person). This information was presented again later at the start of the interviews. It was also ensured at the beginning of the interviews that the inclusion criteria were met. All participants provided written informed consent prior to the interviews. In line with participants’ preferences, interviews were either conducted face-to-face or by telephone. Face-to-face interviews took place in the clinic or at the participants’ homes. We did not gather any information from non-participants, that is, from those who were exposed to our recruitment efforts (e.g., members of the self-help groups), but decided not to participate. Our study was approved by the Institutional Review Board of the Faculty of Medicine of the University of Düsseldorf, Germany (# 2019 − 627).

Data collection

Based on previous qualitative research by our group [ 9 , 11 ], we designed a topic guide [see Additional File 1 ]. After three interviews, the topic guide was discussed and adjusted by the study team. In particular, we then decided to add one question about migraine SM at work specifically during the COVID-19 pandemic, as this was an issue frequently referred to in the first interviews. Prior to the interviews, participants were asked to complete a standardized questionnaire collecting information on socio-demographics and the health status (see Results – Table  1 ).

Data collection took place from August 2020 to January 2021. All interviews were conducted in German by one researcher (CK), who took field notes during the interviews. Besides the interviewer and the participant, no one was present. Follow-up interviews were not conducted. All interviews were digitally recorded and transcribed. CK received detailed feedback on the way she conducted the interviews after three interviews from AL, who is an experienced qualitative researcher [ 9 , 10 , 11 , 13 ] (male, degrees in epidemiology and health sciences). These three interviews were initially supposed to serve as test interviews, but as they contained valuable information we decided to include these interviews in the analysis. There were also no major concerns from AL regarding CK’s interview style except that CK should be even more careful not to ask too closed questions. Data collection was terminated when thematic saturation was reached, which implies that no new information was expected to be delivered by additional interviews [ 14 ]. To verify that saturation had been reached, the data analysis already started during data collection. As the interviews and the analysis were carried out by the same person (CK), she was able to pay close attention to when no new aspects were mentioned in the interviews. The interviews were then terminated after consultation with AL. Study participants were not given access to the transcripts, nor could they provide feedback on the findings.

Qualitative data analysis

Transcripts were content-analyzed [ 15 ] using the software MAXQDA 2020. The coding was based closely on the topic guide and the research questions of the study. Consequently, the questions of our topic guide [see Additional File 1 ] served as main categories (deductive coding). For example, the question ‘Are there conditions at work that help you deal with your migraine?’ served as one main category, labelled ‘Facilitators of migraine self-management’. Subcategories were then developed based on the interview content (inductive coding). After CK had coded five interviews, the coding framework was carefully reviewed by two experienced qualitative researchers (KH and AL). Once everyone approved the initial coding framework, CK applied the code system to all interviews and expanded it by adding further categories. After this first round of coding was completed, AL reviewed the codes again. For instance, AL checked the structure of the coding tree level by level to see whether the codes may be overlapping or seemed ordered logically (e.g., according to the same criteria) within each level. He read all text passages included into each code to explore whether the respective text passages relate to the same phenomenon (and thus can be grouped into a single code) or whether codes may be merged or could be further sub-divided. Based on this, he suggested changes and other inductive categories and discussed them with CK. Based on this discussion, CK re-coded all interviews. Afterwards, the codes were re-discussed with AL and the framework was marginally adjusted. Finally, CK carried out a third and final round of coding. The analysis was carried out using German-language transcripts. The quotes presented in this paper were translated from German into English by a researcher who is familiar with health research and has a Master’s degree in English studies (see acknowledgments).

Description of the sample

In total, 24 interviews were conducted with a mean duration of 31.8 min (range: 17.6–55.3, standard deviation [SD] = 11.4). Twelve interviews were carried out face-to-face and twelve by telephone. Ten participants were recruited in the rehabilitation clinic, nine through self-help groups, and five were private contacts of study team members. Table  1 shows characteristics of our sample: our study population was mainly female (88%), and the mean age was 49.5 years (SD = 9.0). More than half of the participants ( n  = 14) were classified as having a job that was mainly characterized by cognitive or psychosocial demands (e.g., librarian, social worker, pastoral counsellor). The remainder of participants had jobs with mixed requirements (e.g., teacher, nurse, shop assistant). The mean time since the diagnosis of migraine was 20.6 years and varied from five to 40 years (SD = 8.5). On average, participants reported to have had 10.0 days of migraine during the last month, but the variation was considerable (range: 2–26, SD = 6.6).

Qualitative interviews

A broad range of psychosocial facilitators and barriers of migraine SM at work emerged from our data which are described in the following. Our results indicated that the COVID-19 pandemic affected workplace SM both favorably and adversely.

All quotes referenced below can be found in the appendix [see Additional File 2 ]. If there is an interest in the shared migraine self-management strategies at work, a description of these can also be found in the appendix [see Additional File 3 ]. These are not explained in more detail below however, as the specification of the strategies is beyond the scope of our main research questions.

Which psychosocial working conditions influence migraine self-management at work?

Facilitators : The following working conditions were perceived to be helpful in managing migraine at work: (1) high social support, (2) high job decision latitude (JDL; i.e., the degree of an employee’s control over tasks and how and when they are addressed), and (3) a suitable workplace design.

Receiving social support at work from colleagues, supervisors, or in form of company or government policies was reported. Support by colleagues included the understanding for the illness, especially from colleagues with the same disease. It was frequently expressed that some colleagues could – without words – sense when the individual with migraine was not feeling well. Relevant support-related activities by colleagues that made migraine SM easier included support in avoiding triggers (e.g., by ensuring a good air supply), encouragement to withdraw during acute migraine attacks, and taking over tasks (quote 1). According to the participants, social support by supervisors was effective by creating flexible arrangements regarding tasks, working times, and locations, for example the option to work from home (quote 2). Overall, it seemed that understanding for the disease and the social support from colleagues and supervisors facilitated the SM strategy communication. In Germany, people with chronic illness (including migraine) can apply for a so-called “degree of disability”. This entails entitlement to – amongst other things – more holidays and better protection against dismissal from the job. This legal possibility was perceived as helpful, also in the way that these official degrees simplified the justification of the disease and certified its seriousness (quote 3). Furthermore, one participant shared that she has approached the staff council (in her case the teachers’ council). By disclosing her migraine in front of the council, she gained the understanding of her colleagues (quote 4).

In addition to social support, the study participants described a high JDL – that is, a high degree of control over their tasks – as beneficial. Influence on the order of tasks was perceived as allowing for flexibility in planning and carrying out SM during migraine attacks. Control over the type and number of tasks (e.g., working independently instead of attending meetings [quote 5] or the possibility to avoid screen work during the acute attack [quote 6]) were relevant in managing migraine attacks. Another important factor in terms of JDL was the possibility of working from home. The latter provided the opportunity to organize the working day according to one’s own preferences and to take flexible breaks. For example, one participant reported that one had the option of starting one’s working day later at home if one had a headache in the morning (quote 7). The improved opportunities of stopping work at home was also considered to be beneficial because, according to one participant, the threshold to stop working when experiencing complaints is lower when one works at home than at the workplace (quote 8).

A suitable workplace design, which referred mainly to a single rather than a multi-person office, was also experienced as helpful. In a single office, one had the opportunity to retreat and control air supply and light. As many individuals with migraine are sensitive to light during an attack, this can be beneficial (quote 9). In addition to the office situation, the provision of appropriate work equipment such as flicker-free screens, noise-cancelling headphones, and height-adjustable desks was also reported to have a positive effect on one’s migraine. The latter had been reported to reduce cramping in the shoulders and neck and thereby easing headaches (quote 10).

Barriers : The reported barriers represented in certain respects the opposites of the above-mentioned facilitators. Yet as these were explored separately and several aspects were not overlapping, they are described independently. The following aspects were mentioned: (1) poor social interactions, (2) unfavorable working time arrangements, (3) unfavorable workplace arrangements, and (4) other working conditions.

Poor social interactions included interactions with colleagues, supervisors, and service users. Several participants felt that migraine as a disease was often not taken seriously by others and stigmatized at their workplace. For example, this led to migraine being dismissed as a trifle or lack of understanding for staying at home in case of complaints (quote 11). One participant also emphasized the lack of empathy at the workplace: if one was present at work, one was expected to be fully functioning (quote 12). In this context, participants also found it bothering that migraine is “an invisible condition” (quote 13). During contact with service users (e.g., customers, patients, clients), it was reported to be disturbing that there were often high expectations that could not be met during a migraine attack and the associated impairments (quote 14).

Some workplace and working time arrangements were also considered as detrimental. In workplaces where migraine triggers were present (such as heavy noise, little air supply, bright light), migraine SM and especially the preventive strategy of trigger avoidance was reported to be negatively affected. For example, one participant reported the problem of sharing an office and not being able to adapt it to one’s own need, for example not having control over room temperature (quote 15). Visual display unit (VDU) work was also described to be a migraine trigger (quote 16). Another mentioned problem in workplace design was the lack of opportunities to retreat – physically (e.g., due to lack of break rooms, open-plan offices) and mentally (in terms of being permanently approachable). One participant, for example, shared that it was very difficult to deal with migraine if one always had to be approachable on business trips and thus has no possibility to retreat (quote 17). The lack of opportunities to retreat from challenging situations was believed to worsen symptoms and delay recovery from an attack. In terms of unfavorable working time arrangements, irregularity was mentioned as it implies an interruption of one’s usual circadian rhythm, which may trigger a migraine attack. This could be unscheduled client appointments due to public traffic (quote 18), shift work or exceptional weekend work (quote 19), but also business trips (including school trips as a teacher), missing or insufficient breaks, and time pressure at work (quote 20).

Other working conditions that were considered as barriers included, for example, a lack of staff and therefore a lack of replacement hampering one to go home when experiencing an acute migraine attack (quote 21) as well as poor contract conditions. Regarding the latter, one participant shared that she did not call in sick despite symptoms because then she did not get paid (quote 22).

It should also be mentioned that one study participant did not see any connection between migraine and the workplace and thus could not name any facilitators or barriers to SM (quote 23).

How was migraine self-management at work affected by the COVID-19 pandemic?

In Germany, the first two COVID-19-related lockdowns began in March 2020 (until May 2020) and in December 2020. As we gathered our data between August 2020 and January 2021, experiences during the COVID-19 pandemic were an important topic in the interviews.

One positive aspect for migraine SM during the pandemic was reported to be increased JDL. This was mainly due to new opportunities (and in some cases the obligation) to work remotely (quote 24) providing the advantage of more flexibility (e.g., the arrangement of breaks), a lower noise level, and an elimination of travel times. The fact that many employees took the opportunity to work from home also meant that the office was less busy and therefore more quiet (quote 25). This increased quietness also seemed to be beneficial for employees with migraine. One study participant reported that it was easier to close the office door to do a few stretching exercises (quote 26). The increased structuring of the working day and thus increased regularity as a facilitator for migraine SM (e.g., through stricter appointment policy [quote 27]), and the partial reduction of the workload also appeared to be positive. Furthermore, one study participant had more of a feeling of being needed in one’s work at a nursing home. The increased job satisfaction was reported to lower the frequency of migraine complaints (quote 28).

The stress caused by the additional hygienic measures, the mouth-to-nose covering, and the increased amount of screen work were perceived as negative for migraine SM. Coming in contact with COVID-19-positive people often necessitated use of additional stressful measures such as the application of hygiene or personal protective measures. This was, for example, reported by a study participant that worked as a nurse in a hospital (quote 29). The mouth-to-nose covering seemed to make it difficult not only to breathe but also to speak, which in turn was perceived to trigger migraine (quote 30). A final migraine trigger in the pandemic was the fear of the end of the pandemic and thus the loss of the possibility to work from home (quote 31).

Summary of main findings

According to our participants migraine SM at work is affected by social interactions (e.g., understanding as a facilitator vs. stigmatization as a barrier), the extent of JDL (e.g., in terms of working hours and localization) as well as the workplace design (e.g., regarding opportunities to retreat or to avoid VDU work). During the COVID-19 pandemic, it was considered positive for migraine SM that the daily structure was associated with more predictability and planning (e.g., through stricter appointment scheduling). It was also emphasized that there were more opportunities to work from home and thus better conditions for appropriate migraine SM through more JDL. A negative aspect associated with the COVID-19 pandemic was increased work-related screen time. Participants also shared that the novel hygiene measures (e.g., after contact with a COVID-19-positive person) and the mouth-to-nose covering triggered migraine complaints.

Findings in light of earlier research

In terms of barriers and facilitators, our findings are in line with prior research. A qualitative study on migraine and chronic daily headache management by Peters et al. [ 4 ] highlighted the importance of social support. The authors concluded that social support, especially from peers with the same conditions, can lead to better understanding from colleagues. The lack of social support and the feeling of stigmatization was an important aspect in another qualitative study by Heidari et al. [ 16 ], focusing on common themes of migraine patients. In that study one participant reported going to work despite migraine, because the supervisor seemed not to take migraine seriously [ 16 ]. One study – in accordance with our findings – linked stigmatization to migraine being an invisible disease, limiting the understanding for the condition [ 17 ]. Other factors influencing migraine SM that emerged from our study were factors related to workplace design. This is in keeping with findings from a cross-sectional study on the burden and impact of migraine on work productivity and quality of life that also addressed job-related migraine triggers and coping strategies: Looking at computer screens for too long was one of the two most frequently mentioned migraine triggers at the workplace [ 18 ]. Having control of light, noise and smells was under the top five coping strategies [ 18 ].

In the context of the EFNA study [ 6 ], individuals with migraine and other headache type patients were asked what their company should do to help them cope better with their condition. The wishes mentioned included a greater understanding for the disease, less social interaction, the possibility to work from home, flexible working hours, and more opportunities to withdraw if needed [ 6 ]. These aspects overlap with the facilitators that emerged from our study, in particular regarding social support and JDL.

Regarding the COVID-19 pandemic, our results are consistent with findings from other studies that found a link between personal protective equipment, especially the wearing of masks, and a worsening of migraine [ 19 , 20 , 21 ]. There is also further evidence that the increased screen time, for example due to remote working or online lessons during the pandemic, served as a trigger for migraine, in particular in young adults and adolescents [ 22 , 23 ]. However, remote working during the pandemic was generally considered to have a positive impact on migraine (e.g., reduced migraine attack duration) [ 24 ]. In a qualitative study by Buse et al. [ 20 ], examining the general impact of the COVID-19 pandemic on patients with migraine, participants reported that working from home was associated with more control, e.g., over the work environment. This reflects the importance of JDL for migraine SM. Notably, some factors that were mentioned to influence SM in general (e.g., social support) played little or no role in the COVID-19 pandemic and associated SM. The pandemic served as a kind of natural experiment elucidating which factors – when modified – influence migraine and its SM. Based on this, it can be hypothesized that the facilitator “JDL,” which played an important role in general and during the pandemic, has the utmost relevance on migraine SM at work. To our knowledge, no previous qualitative study on migraine or chronic headache has yet highlighted the high relevance of JDL for SM.

Methodological considerations

The interviews were conducted face-to-face or by telephone, depending on the preference of the participants. This provided us with the opportunity to include participants nationwide and despite restrictions due to COVID-19. We did not notice considerable differences regarding the contents between the two interview modes, which is supported by earlier research [ 25 ]. To reduce a potential healthy worker bias (i.e., the assumption that the working population is healthier than the non-working population), we recruited migraine patients who had ever worked for six months with a diagnosis of migraine and not only patients who were currently working. This allowed us to include patients who might have had to leave their job due to severe migraine or who attempted to regain their workability through rehabilitation.

We relied on the patients’ report of being diagnosed with migraine by a physician, and we did not apply the International Classification of Headache Disease [ 26 ] for diagnoses. However, those participants who were recruited from the rehabilitation clinic for migraine ( n  = 10) had received a medical diagnosis of migraine and their condition was severe enough to threaten their employment status. Further, as we interviewed mainly patients from this rehabilitation clinic and from self-help groups, however, we cannot exclude the possibility of a selection bias: those patients interact with other individuals with migraine, a are usually well-informed about their condition, and their experiences and perspectives may differ to some extent from the broader patient population. Moreover, only three out of 24 participants were male. This may have limited the scope of views that emerged from male participants regarding migraine SM. Also, our study especially included patients who worked in a job with mainly cognitive and social demands. It is well conceivable that their experiences differ from that of migraine patients who work in a job with mainly physical demands (e.g., individuals working in transportation or farming). We were able to cover a broad distribution regarding the average number of migraine days per month (mean = 10.0, SD = 6.6, range: 2–26 days). These observations increase the confidence that we covered a large range of potential views and experiences.

Another methodological weakness is that the coding was carried out by only one single person who had no previous experience in coding (CK). The intense involvement of additional individuals in the coding process (e.g., more experienced coders, people with migraine, occupational physicians, and/or neurologists), would likely have led to a richer analysis and additional insights, but this was beyond the resources of our study (i.e., time and financial means).

Finally, due to the limited experience with qualitative research methods of the first author, who was also the primary analyst, and due to the fact that it was not feasible to substantially involve additional analysists in the coding, the depth of our analyses may have been limited. Accordingly, our study may be classified as a ‘topical survey’ with aspects of a ‘thematic survey’ – according to the classification of findings in qualitative studies suggested by Sandelowski & Barroso [ 27 ]. A topical survey stays close to the data collected and is primarily a description of it, whereas a thematic survey provides a higher level of transformation of data [ 27 ]. The purpose of thematic surveys were only achieved to a limited extent. However, Sandelowski & Barroso state that a topical survey is not necessarily inferior in terms of the quality of its value [ 27 ].

Implications for practice and research

Based on our findings, interventions could be devised to improve migraine SM at work. Regarding social support, it is important to reduce stigma of migraine to create a working environment in which patients feel comfortable to talk openly about their migraine without it being dismissed as a trifle. Our study thus calls attention to the fact that migraine healthcare professionals should offer support for improving patients’ social communication skills in the workplace leading to greater acceptance of the condition. One health care sector that seems particularly suitable for this endeavor is rehabilitation. Treatment in rehabilitation clinics in Germany involves patient education, which can help to raise awareness among migraine patients for the potential influence of their working conditions on their opportunities to manage their migraine at work. Also, patients can be empowered (e.g., by improving knowledge about legal frameworks and practicing communication skills) to modify their working conditions to some extent. Similar concepts are currently tested for other conditions than migraine [ 28 ].

To increase JDL, employers should try to give migraine patients as much freedom as possible. For example, for office jobs the possibility of expanding remote working should be explored. Here, the experience gained during the COVID-19 pandemic can be used. If working from home is not possible, care can be taken to create a migraine-friendly workplace, for example by providing single offices, noise-cancelling headphones, height-adjustable desks, and places of retreat. VDU work could also be designed to be as gentle as possible, e.g., by using flicker-free screens. If available and needed, occupational physicians should support all these interventions by educating workers with migraine and by serving as mediators between supervisors and employees with migraine.

All these interventions should be carefully developed and evaluated prior to their implementation in routine care. We believe that more preparatory research is needed. Firstly, as mentioned above, our analysis may be limited in depth. It therefore seems promising to carry out additional qualitative studies that involve analysts with more diverse professional backgrounds and employees with migraine as co-researchers. In the next step, the scope of the problem could be confirmed, and possible interventions may be explored. Quantitative research (e.g., surveys) would be suitable to examine, amongst others, the proportion of workers with migraine that find self-management at work to be challenging, to prioritize areas for intervention, and to examine what types of interventions would be acceptable to those receiving them and those potentially delivering them. Also, working life in the post-COVID-19 era has further evolved since our study to arrive at a “new normal” (e.g., allowing for more home office working hours than in the pre-COVID-era), which our study does not reflect, and which follow-up qualitative studies could explore. Furthermore, quantitative studies could test hypotheses that can be deduced from our qualitative study (e.g., “The ability to perform migraine SM at work is associated with the level of experienced JDL”). Such research could move beyond self-management as an outcome to include symptoms and occupational outcomes (e.g. workability, presenteeism and absenteeism) and may explore whether improved migraine SM at work curtails the considerable cost associated with migraine-related impairment.

Migraine SM at work is influenced positively and negatively by various occupational factors. By considering these facilitators and barriers, a more migraine-friendly workplace can be created to reduce a burden not only for patients but also for society. Further research is needed before interventions can be implemented.

Data availability

Data cannot be shared publicly because the transcripts may contain sensitive information. The data may be obtained from the corresponding author upon reasonable request and provided that legal frameworks are not violated and that responsibilities and confidentially have been clarified.

Abbreviations

Chronic migraine

• Self-management

Job decision latitude

European Federation of Neurological Associations

Visual display unit

Headache disorders. World Health Organization. 2016. https://www.who.int/news-room/fact-sheets/detail/headache-disorders . Accessed February 13, 2024.

Linde M, Gustavsson A, Stovner LJ, Steiner TJ, Barré J, Katsarava Z, Lainez JM, Lampl C, Lantéri-Minet M, Rastenyte D, de la Ruiz E, Tassorelli C, Andrée C. The cost of headache disorders in Europe: the Eurolight project. Eur J Neurol. 2012;19(5):703–11.

Article   CAS   PubMed   Google Scholar  

Peters M, Abu-Saad HH, Vydelingum V, Dowson A, Murphy M. Patients’ decision-making for migraine and chronic daily headache management. A qualitative study. Cephalalgia. 2003;23(8):833–41.

Peters M, Abu-Saad HH, Vydelingum V, Dowson A, Murphy M. Migraine and chronic daily headache management: a qualitative study of patients’ perceptions. Scand J Caring Sci. 2004;18(3):294–303.

Article   PubMed   Google Scholar  

Peters M, Abu-Saad HH, Robbins I, Vydelingum V, Dowson A, Murphy M. Patients’ management of migraine and chronic daily headache: a study of the members of the Migraine Action Association (United Kingdom). Headache. 2005;45(5):571–81.

Results of EFNA’s survey on stigma and neurological disorder. European Federation of Neurological Associations. 2020. https://www.efna.net/survey2020/ . Accessed January 13, 2023.

Scaratti C, Foitzek N, Grignani E, Leonardi M, Raggi A, Coenen M. The employment needs and the factors having a negative or positive impact on work of people with chronic diseases in Europe: the case of headache. Neurol Sci. 2018;39(Suppl 1):121–3.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Heinrichs K, Vu-Eickmann P, Hummel S, Gholami J, Loerbroks A. What are the perceived influences on asthma self-management at the workplace? A qualitative study. BMJ Open. 2018;8(8):e022126.

Article   PubMed   PubMed Central   Google Scholar  

Heinrichs K, Hermülheim V, Pilz González L, Loerbroks A. When in doubt … Career Indecision,Mental Wellbeing, and Consultation-seeking Behaviour-A qualitative interview study among students and counsellors. Int J Environ Res Public Health. 2021;18(23):12604.

Loerbroks A, Nguyen XQ, Vu-Eickmann P, Krichbaum M, Kulzer B, Icks A, Angerer P. Psychosocial working conditions and diabetes self-management at work: a qualitative study. Diabetes Res Clin Pract. 2018;140:129–38.

Kroenke K, Spitzer RL, Williams JBW, Löwe B. An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics. 2009;50(6):613–21.

PubMed   Google Scholar  

Scharf J, Angerer P, Müting G, Loerbroks A. Return to work after Common Mental disorders: a qualitative study exploring the expectations of the involved stakeholders. Int J Environ Res Public Health. 2020;17(18):6635.

Corbin JM, Strauss AL. Basics of qualitative research: Techniques and procedures for developing grounded theory. Fourth edition. SAGE Publications. 2014.

Mayring P. Qualitative Content Analysis [Qualitative Inhaltsanalyse: Grundlagen und Techniken. 12., überarb. Aufl.]. Weinheim, Beltz. 2015.

Heidari E, Rao D, Pfalzgraf AR, Lobo C, Giannetti V. An explorative study of common themes of patient experiences with migraine. Prim Care Companion CNS Disord. 2022;24(2):21m02939.

Basoglu Koseahmet F, Polat B, Gozubatik-Celik RG, Baytekin I, Soylu MG, Ceyhan Dirican A, Ozturk M. An invisible cause of disability: stigma in migraine and epilepsy. Neurol Sci. 2022;43(6):3831–8.

Haw NJ, Cabaluna IT, Kaw GE, Cortez JF, Chua MP, Guce K. A cross-sectional study on the burden and impact of migraine on work productivity and quality of life in selected workplaces in the Philippines. J Headache Pain. 2020;21(1):125.

Yuksel H, Kenar SG, Gursoy GT, Bektas H. The impacts of masks and disinfectants on migraine patients in the COVID-19 pandemic. J Clin Neurosci. 2022;97:87–92.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Buse DC, Gerstein MT, Houts CR, McGinley JS, Uzumcu AA, McCarrier KP, Cooke A, Touba NM, Nishida TK, Wirth RJ, Lipton RB. Impact of the COVID-19 pandemic on people living with migraine: results of the MiCOAS qualitative study. Headache. 2022;62(3):284–93.

Ong JJY, Bharatendu C, Goh Y, Tang JZY, Sooi KWX, Tan YL, Tan BYQ, Teoh HL, Ong ST, Allen DM, Sharma VK. Headaches Associated with Personal Protective Equipment - A cross-sectional study among Frontline Healthcare workers during COVID-19. Headache. 2020;60(5):864–77.

Wehbe AT, Costa TE, Abbas SA, Costa JE, Costa GE, Wehbe TW. The effects of the COVID-19 confinement on screen time, headaches, stress and Sleep disorders among adolescents: A Cross Sectional Study. Chronic Stress (Thousand Oaks). 2022;6:24705470221099836.

Abou Hashish EA, Baatiah NY, Bashaweeh AH, Kattan AM. The online learning experience and reported headaches associated with screen exposure time among Saudi health sciences students during the COVID-19 pandemic. BMC Med Educ. 2022;22(1):226.

Currò CT, Ciacciarelli A, Vitale C, Vinci ES, Toscano A, Vita G, Trimarchi G, Silvestri R, Autunno M. Chronic migraine in the first COVID-19 lockdown: the impact of sleep, remote working, and other life/psychological changes. Neurol Sci. 2021;42(11):4403–18.

Sturges JE, Hanrahan KJ. Comparing telephone and face-to-face qualitative interviewing: a research note. Qualitative Res. 2004;4(1):107–18.

Article   Google Scholar  

IHS Classification ICHD-3. International Headache Society. https://ichd-3.org/classification-outline/ . Accessed February 13, 2024.

Sandelowski M, Barroso J. Classifying the findings in qualitative studies. Qual Health Res. 2003;13(7):905–23.

Salandi J, Hayden MC, Heinrichs K, Limbach M, Schultz K, Schwarzl G, Neumeister W, Loerbroks A. Can an educational intervention in the context of inpatient pulmonary rehabilitation improve asthma self-management at work? A study protocol of a randomized controlled trial. BMC Pulm Med. 2024;24(1):40.

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Acknowledgements

The authors would like to thank Prof. Dr. Scott Stock Gissendanner for his support related to participant recruitment (Berolina Klinik, Löhne). For her translation of the quotes, we are grateful to Lisa Guthardt (Institute of Occupational, Social and Environmental Medicine, Faculty of Medicine, University of Düsseldorf). The authors would also like to thank the participants for taking their time to share their experiences with respect to working with migraine.

Open Access funding enabled and organized by Projekt DEAL. This research did not receive any funding.

Open Access funding enabled and organized by Projekt DEAL.

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Katherina Heinrichs

Berolina Klinik, Löhne, Germany

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Contributions

C.K., K.H., A.I. and A.L. contributed to the conception and design of the study. C.K. and R.S. contributed to the acquisition of the data. The interviews were conducted by C.K. C.K. performed the analysis and interpretation of the data supported by K.H. and A.L. C.K. drafted the manuscript and all authors revised it for intellectual content. All authors read and approved the final version of the manuscript. All authors agreed to be accountable for all aspects of the work.

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Correspondence to Adrian Loerbroks .

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This study was approved by the Institutional Review Board of the Faculty of Medicine of the University of Düsseldorf, Germany (no 2019 − 627). All participants provided written informed consent prior to the interviews.

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Knauf, C., Heinrichs, K., Süllwold, R. et al. Migraine self-management at work: a qualitative study. J Occup Med Toxicol 19 , 22 (2024). https://doi.org/10.1186/s12995-024-00421-w

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Received : 26 February 2024

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DOI : https://doi.org/10.1186/s12995-024-00421-w

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Chemistry Education Research and Practice

Reply to the ‘comment on “what resources do high school students activate to link energetic and structural changes in chemical reactions – a qualitative study”’ by k. s. taber, chem. educ. res. pract. , 2024, 25 , https://doi.org/10.1039/d3rp00232b.

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a Eberhard Karls Universität Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany E-mail: [email protected]

Our article “What resources do high school students activate to link energetic and structural changes in chemical reactions? – A qualitative study” was recently commented on by Keith Taber. In his comment he focuses on the dominant role of the octet rule in students' reasoning and suggests that students rely on an octet framework. In the first part of this response, Taber's argument about the pervasive inappropriate use of the octet rule is supported by empirical evidence. Re-analysis of the data confirms that students often seem to assume initial atomicity, use anthropomorphic language, and closely associate the octet rule with stability. These points make the octet rule a convenient answer for students to fill the “explanatory vacuum” often left in chemistry education, e.g. for explaining the driving force of reactions. In the second part, we discuss how these observations might be rationalized. Rather than a static misconception perspective, we suggest that student's application of the octet rule can be viewed from a dynamic, resource-oriented view of learning. Three examples are introduced to illustrate the variety in students’ applications of the octet rule. For a better understanding, more detailed research on how students really think and learn about the octet rule and energetics is necessary.

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B. Pölloth, D. Diekemper, C. Bosch and S. Schwarzer, Chem. Educ. Res. Pract. , 2024, Advance Article , DOI: 10.1039/D4RP00031E

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