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Why NVivo Is the Leading Choice for Qualitative Data Analysis Among Researchers
Qualitative data analysis can be a daunting task, especially when dealing with large sets of data. This is where NVivo comes in handy. NVivo is a software package designed to assist researchers in analyzing qualitative data. In this article, we will discuss why NVivo is the leading choice for qualitative data analysis among researchers.
What is NVivo?
NVivo is a software tool developed by QSR International that helps researchers organize and analyze their qualitative data. The software provides a range of features and tools that assist researchers in managing complex data sets, including text, audio, video, and images.
Features of NVivo
One of the key features of NVivo is its ability to handle different types of data formats. The software can handle text-based documents such as emails, interviews, focus group transcripts, and surveys. It also supports multimedia files such as videos and audio recordings.
Another feature that makes NVivo stand out is its coding capabilities. The software allows users to code their data using different methods such as thematic or content analysis. This feature streamlines the process of identifying patterns or themes within the data set.
NVivo also has advanced search capabilities that allow users to search for specific keywords or phrases within their data set quickly. Additionally, it has visualization tools that enable users to create graphs and charts to present their findings visually.
Benefits of Using NVivo
The benefits of using NVivo are numerous. Firstly, it saves time by automating many aspects of the research process; this includes transcribing audio recordings and coding text-based documents.
Secondly, it increases accuracy by reducing errors associated with manual transcription or coding processes; this means that researchers can trust their results more confidently.
Thirdly, it enables collaboration between team members working on a project from different locations; this feature allows individuals to work on the same project simultaneously, increasing productivity.
Lastly, NVivo provides a range of support resources. This includes online tutorials, webinars, and user forums that connect users with other researchers who use the software.
In conclusion, NVivo is an essential tool for researchers looking to analyze qualitative data. Its features and capabilities make it the leading choice for handling complex data sets across a range of disciplines. The benefits of using NVivo include increased accuracy, time-saving automation, collaboration capabilities, and access to support resources. With NVivo, researchers can analyze their data more efficiently and effectively than ever before.
This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.
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Writing your PhD: Qualitative Research
This course is intended for PhD students in their final year.
This 7-week course is intended for final year PhD students working on a thesis based on qualitative research, who are in the later stages of their PhD. In semester 1, this course is only available online. In semester 2 it will be available both in-person and online. Students work on various aspects of what is involved in producing such a thesis, and receive feedback from their tutor on extracts from their writing.
Course Content and Unit Aims
Structure and Introduction
- To explore the features of qualitative research writing.
- To examine ways of structuring a qualitative thesis.
- To consider the content, structure and language of a typical introduction.
- To provide practice in writing an introduction.
The Literature Review
- To discuss the purposes and principles behind a qualitative literature review.
- To explore the typical content and organisation of a literature review.
- To raise awareness of citation practices, including direct quotation, paraphrase, summary, and the use of reporting verbs.
- To provide practice in writing a section of a literature review.
- To discuss the content and structure of a qualitative Methodology chapter.
- To explore the use of language in a qualitative Methodology chapter.
- To provide practice in writing the “setting” /”context” section of a Methodology chapter.
Your data chapters
- To discuss the typical elements of a qualitative data chapter.
- To analyse the structure and language of a sample data chapter from a qualitative PhD.
- To explore the language used to express caution when interpreting results.
- To provide practice in writing a data discussion.
The final chapter
- To discuss the typical components of the final chapter of a qualitative thesis.
- To raise awareness of the kind of language typically used in these elements.
- To practice avoiding repetition in written texts.
- To analyse extracts from final chapters of qualitative theses.
- To provide practice in writing a section of a final chapter.
The first few pages
- To discuss the characteristics of a successful thesis title.
- To analyse sample thesis abstracts, contents pages and acknowledgements.
- To practise writing your abstract and title.
- Online - Wednesday 11:00 - 12:00
Teaching Methods and Learning Outcomes
At home, you will listen to a brief introductory lecture and work through a series of tasks which involve analysing sample extracts from University of Edinburgh doctoral theses; expanding your repertoire of useful academic English expressions; drafting short pieces of writing. You will then write a short assignment. In order to benefit fully from the course, you can expect to spend an additional 2 hours per week of independent study, including the written assignment .
You should send the assignment to your tutor, who will respond with feedback on your writing, including comments on style, the use of sources, organisation, and linguistic accuracy. Your tutor will also meet you for a synchronous online class once per week. All materials are provided.
You will also have the opportunity for a one-to-one on-line tutorial with your teacher, to discuss any remaining questions you may have.
By the end of the course, you should have a better understanding of:
- ways of structuring the chapters of your thesis.
- appropriate language for the different chapters or sections, and how to use that language accurately
- any specific language areas that you will need to work on further
PhD students in the later stages of their PhD.
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Home >> Blog >> Tips on writing a qualitative dissertation or thesis, from Braun & Clarke – Part 2
Tips on writing a qualitative dissertation or thesis, from Braun & Clarke – Part 2
Here we delve in to qualitative-centric methodology, analysis, discussion and conclusions.
Part 1 of this blog covers qualitative centric writing, introductions and research questions.
We prefer the title methodology – rather than method – for the section in which you discuss the rationale for your design choices and your procedures.
Method ology signals a theoretically-embedded research practice. To us, “method” evokes or allows a theoretically-divorced description of the tools used, and how they were used. Make sure you discuss your philosophical – ontological and epistemological – assumptions, even if only very briefly, ideally citing relevant references to anchor your definitions. This is especially important if you’re using reflexive TA as it’s a theoretically flexible method (or something close to a method), rather than a theoretically bound and delimited methodology.
Insiders, outsiders and reflexivity
Reflexivity is also a really important part of the methodology section. If you’re an insider researcher (a member of the group you are researching), take a dive into the methodological literature on insider researchers. This paper by Nikki Hayfield and Caroline Huxley is a great place to start exploring this literature. The insider/outsider dynamic is an excellent tool for getting into reflexivity, and thinking about how you as a researcher have (inadvertently) shaped your research.
Don’t think in terms of “bias” – which implies a distortion of objective knowledge and something ideally avoided. Think in terms of subjectivity – which we and many others identify as an inevitable component of qualitative research. Reflexivity is the practice that enables you to reflect on how you shaped your research. We say “how” – not whether – because you definitely did shape your research!
Sample or saturation?
Think carefully about using the concept of saturation to justify the size of your participant group or dataset. This is most often called a sample, language we’ve used a lot. But in our most recent writing, we try to avoid the term “sample” after a reviewer commented that this too is a hangover from quantitative research.
But why not saturation? Saturation is not a theoretically neutral concept – it is arguably underpinned by positivist and realist assumptions. We argue against its use as a criterion for determining the size of a dataset/participant group in reflexive TA. There are other good options out there such as information power .
Know your design choices and write about them
As a general good practice tip for writing your methodology… engage with the methodological literature related to your design choices – there will be some. For instance, instead of writing things like “I had to do video call interviews because of COVID”, draw on the video call interviews methodological literature to provide a more robust rationale for, and discussion of your use of, video call interviews.
Check out Collecting Qualitative Data – a book we edited with Debra Gray – for an excellent chapter on video call interviews by Paul Hanna and Shadreck Mwale.
You may have been encouraged to cite more than one source on TA – we often encounter students who have been told to not just cite Braun and Clarke. If so, proceed carefully. There is a wide variety of approaches to TA, with some characteristics in common, but also with some important differences in the conceptualisation of key constructs and analytic procedures.
The diversity within the TA family of methods is poorly understood – those recommending that students cite more than one source often don’t appreciate that diversity. Avoid mixing and matching incompatible approaches – our approach and that of Boyatzis are very different, for example. The paper we mentioned earlier – Conceptual and Design Thinking in Thematic Analysis – provides an introduction to the diversity across TA approaches.
Justifying your analytic method/ology on its own terms
Another piece of advice students are often given is to explain why they didn’t use other similar method/ologies as a way of providing a rationale for their choice of analytic approach. We don’t agree with that approach, in general. One risk is that you discuss implausible alternatives and/or the alternatives are not very well explained.
In a recent paper we compared and contrasted reflexive TA with other widely used qualitative approaches, such as interpretive phenomenological analysis and grounded theory, and discussed similarities and differences. The paper will help you to develop a robust rationale for your selected method/ology without having to deploy a “other method/ologies I considered and rejected” type approach. It will help you to understand what is distinctive about your method/ology and when it is an appropriate choice.
Write about what you actually did
If you use reflexive TA, don’t just provide a generic description of the process – the six phases. Instead, make sure you tell the reader what you actually did , how you actually engaged with the process. We should mention here as an aside that we have recently renamed the six phases, and explained our rationale for doing so , so you may like to demonstrate to the reader that you are up-to-date with developments in reflexive TA by using the latest iteration of the six phases . For a doctoral thesis, examiners knowledgeable about qualitative methods might expect this.
A final tip is to avoid quantitative style subheadings in this section – things like “materials” and “procedure” – and instead used subheadings more aligned with qualitative approaches. Subheadings like “interviews” (or “focus groups” or whatever method you use to collect data), “participants and recruitment”, “data analysis” and so on work better. And as a general rule, organise any subheadings and content so that you discuss rationale (why you did something) before implementation and practicalities (how you did something).
We prefer the heading analysis rather than “findings” as the latter implies the researcher discovered their analysis – that themes (for instance) existed fully formed in the data and the researcher found and then exported them into their report.
You don’t need to aim for ‘surprise reveals’
Start this section with an overview of your analytic structure – such as with a simple list, table, or thematic map of themes. A problem we commonly encounter in a report is not really understanding what the themes are, or how the analysis will flow. An overview at the start solves this problem! Think of it as signposting for the reader. Remember, a qualitative centric report is not about big surprise reveals, so much as contextualised information and interpretation, so giving your reader signposting that helps them understand what you’ve done, really helps.
Some tips on reflexive TA especially:
Topic summaries vs themes
These interconnect to the practice of how the analysis has been done. If you’re using reflexive TA, it’s really important not to confuse topic summaries with themes . The former present summaries of what participant said in relation to a particular topic area (perhaps mapping closely onto a particular data collection question), whereas themes are patterns of shared meaning underpinned by central concept.
See Conceptual and Design Thinking for Thematic Analysis for a discussion of the differences between topic summaries and shared meaning themes. Avoid one-word theme names – such names aren’t very informative for the reader and can suggest topic summaries are being reported.
We generally advise against having lots of theme levels and subthemes. Keep in mind that themes are ideally complex, rich multifaceted stories – it’s difficult to report lots of them and do them justice (unless you have a luxuriously long word count).
Subthemes are useful to highlight a facet of the central concept of your theme, but a complex thematic structure is not in keeping with the ethos of reflexive TA. Other TA methods are better suited to this type of thematic structure such as template analysis or framework analysis .
Analysis and discussion – better off together or apart?
In a qualitative centric style of reporting, it’s normative to integrate or combine the “results and discussion”. This style of presenting an analysis seems to be particularly challenging for more positivist qualitative researchers (as noted earlier, we and our students are often asked to separate out the results and discussions). Having these sections separate suggests that it’s conceptually possible and practically and theoretically desirable to separate out “the results” and your interpretation of them. But for qualitative research embedded within qualitative values, that’s simply not the case, and can be questioned and challenged.
Moreover, integration allows you to contextualise your themes and analytic observations in relation to existing literature, to relevant theory and wider contexts, and to draw on existing literature and theory to deepen your analytic engagement with your data (can an existing study or a theoretical framework help you to really get to grips with an aspect of the data?).
Even in a dissertation/thesis with an integrated “results and discussion”, you will still need a general discussion/conclusion in which you reflect on your study and consider future research.
This is the part of the report where researchers often note that they may have influenced the analysis because of their positioning or practices. As we mentioned earlier, it’s better to discuss and reflect on how you shaped your research rather than treat researcher influence as contamination.
Avoid evaluating qualitative research in quantitative terms
Another thing to avoid in this section is bemoaning the lack of generalisability of your small participant group or dataset. Bemoaning a lack of generalisability implicitly invokes a conceptualisation of generalisability as statistical probabilistic generalisability; it falls into the trap of evaluating qualitative research in quantitative terms. This paper by Brett Smith provides an excellent discussion of qualitative conceptualisations of generalisability.
Make ‘future research’ an evidence-based discussion
When you make suggestions for future research, don’t turn on the “random ideas generator”! Any suggestions you make should arise from your research – intriguing things that the data hinted at but you didn’t have enough or the right kind of data to fully explore; suggestions that could address the limitations of your study.
If you make the classic suggestion that similar research needs to be conducted with different groups of people, try to make this an evidence-based discussion rather than a generic point: explain why things might be different (and similar) for these groups. This paper on gay fathers gives you an idea of what this looks like in practice.
Finally, end your report with a conclusion that leaves the reader with a clear “take-home message” about your research. Don’t introduce new material in the conclusion!
Victoria Clarke and Virginia Braun’s forthcoming book is Thematic Analysis: A Practical Guide . They have websites on thematic analysis and the story completion method . You can find them both on Twitter – @drvicclarke and @ginnybraun – where they tweet regularly about qualitative research.
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About Victoria Clarke
Victoria is an Associate Professor in Qualitative and Critical Psychology at the University of the West of England, Bristol, UK. You can find her on Twitter - @drvicclarke - regularly tweeting about qualitative research.
View all posts by Victoria Clarke
About Virginia Braun
Virginia is a Professor in Psychology at The University of Auckland, Aotearoa New Zealand. You can find her on Twitter - @ginnybraun – (re)tweeting about qualitative research and other issues.
View all posts by Virginia Braun
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Tips for a qualitative dissertation
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.
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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:
- 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.
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.
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.
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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