how to present interview results in a thesis

Incorporating Interview Data

Introduction

When you incorporate original interview data into your writing, you are developing new ideas by using quotations and often sources that no one else has accessed. Drawing from interviews can liven up your writing, ground your big concepts within the specific circumstances of particular individuals, and introduce you to insights you might never have considered on your own. Additionally, interviews are an exciting way for you to provide a larger audience for people who might not otherwise have opportunities to share their stories, perceptions, and experiences.

There are lots of good reasons to incorporate original interview data into your writing. But doing so also involves making many, specific writing decisions. On this page we explore some of those decisions by considering: 1) the process by which interview data is gathered; 2) models for interview incorporation; and 3) identification of ways that writing with interview data can be like writing with information from any other source (as well as some of the unique writerly considerations that interviews raise).

Contents       Before You Write       Different Models of Incorporating Interview Data       Summarizing, Paraphrasing, or Quoting       Referring to your Interviewees       Using Verbatim or Non-Verbatim

Before You Write

Of course, before you can incorporate interview data into your writing, you need to plan and conduct your interviews and begin to analyze your findings.

Interviewing is a common form of research and information gathering in many different fields and across many different genres. In order to develop and actualize a plan for why interviews will help you answer the questions you’re asking, whom you’ll interview, and what you’ll ask these subjects, you’ll want to consult a range of resources. Talk with your instructor, mentor, or advisor about common ways of approaching interviews for this assignment or in this discipline. Additionally, many undergraduate textbooks about research in the social sciences and humanities offer introductions to interviewing. We’ve listed a few great resources to help you learn more.

For comprehensive introductions to research methods used in the writing research that include some information about interviews, consider:

• Jackie Grutsch McKinney’s book Strategies for Writing Center Research —especially pages 55-69. While Grutsch McKinney’s is focused on writing center research, her close consideration of the different ways to structure interviews as well as how to plan and conduct them can be applied to all interview contexts. Additionally, her treatment of data analysis in chapter 8 provides a step-by-step guide for coding qualitative data—one of the approaches you might use to make sense of what your interview data means.
• Joyce Kinkead’s Researching Writing: An Introduction to Research Methods —especially pages 37-39. This is a potential textbook for that could be used for a class specifically about the formal study of writing. However, its direct and specific information about interviewing is applicable for any social science researcher preparing to use interviews for research.

These resources focus more specifically on qualitative research methods in particular and interviewing in particular:

• Robert Bogdan and Sari Knopp Biklen’s Qualitative Research for Education: An Introduction to Theories and Methods —especially pages 103-109. Bogdan and Knopp Biklen’s treatment of interview practices provides a brief overview of how to approach and implement this research methodology.
• Irving Seidman’s Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences —especially pages 78-94. This entire book explores interview practices, logistics, and applications, but chapter six in particular usefully details particular interviewing techniques and provides transcripted examples of some of these strategic techniques in action.

The rest of the information on this page assumes that you have learned to develop and implement your interview plan, that you’ve analyzed the information you’ve gathered, and that you’re now ready to start weaving that information into your writing.

Different Models for Incorporating Interview Data

You can use interview data in many different ways. Most often, you will probably be making an argumentative or analytical point and illustrating and supporting it with evidence from your interviews. For example, in the following passage from the book Booty Capitalism: The Politics of Banking in the Philippines , Paul D. Hutchcroft, a political science professor at Australian National University, begins with an original claim, follows that with a quotation from an interview subject that exemplifies that claim, and then offers additional commentary on that issue. Note how the quotation from the interview both connects the concepts of banking and politics and introduces the prism metaphor that Hutchcroft continues into the next sentence.

The major focus of this [book] is two arenas that offer particular insights into the nature of relations between state and oligarchy in the banking system: bank supervision and selective credit allocation. “Banking,” observes one former bank president, “is a prism through which to understand power politics in the Philippines.” A study of the banking system highlights larger patterns at work within the political economy: how a predatory oligarchy extracts privilege from a patrimonial state, and how developmental policy objectives are continually choked out by a clamor of particularistic demands made by those who currently enjoy proximity to the political machinery. (7)

Generalizing about a Trend or Theme

Using information from an interview to support your claim is the primary purpose for incorporating interview data into your writing, but how you do this may change according to your specific intent. In what follows, we explore different models for weaving interview data into your writing and provide examples of what this looks like.

It is important to consider the politicization of the nationality responses in context. On the whole, the vast majority of republican executives did not try to influence the process, and the nationality question was a non-issue in the predominantly ethnically Russian regions. In my regional interviews I found that in the oblasts and krays, there were almost no reports of difficulty with the nationality question. Officials in those areas reported that respondents who were not ethnically Russian had no difficulty citing a different nationality. This finding corresponded with my observations of the enumeration process in Moscow. There were sporadic cases of respondents in ethnically mixed marriages registering one child as of one parent’s ethnic group and the other child as of the other parent’s ethnic group. However, this is a conceptual issue rather than a problem of politicization. ( 367-8 )

Quoting to Illustrate a Trend or Theme

Sometimes interviewees say things that are so strikingly similar that it is useful to draw attention to these complementary concepts and word choices by putting them together. In the following passage, Jane Calvert, a professor at the University of Edinburgh, and Joan Fujimura, a sociology professor at UW-Madison, use this strategy while writing about scientists’ responses to the new and developing field of systems biology. Note that these authors carefully tie quotations to specific anonymized interviewees through parenthetical citations.

In another US university, the decision to build an interdisciplinary research centre was top-down, initiated by university and funding administrators and initially opposed by most campus laboratory scientists. The building of new interdisciplinary structures is challenging for the existing disciplinary “fiefdoms” (Biologist19) and “silos” (Biologist9 and Biologist12) “where people feel protected and safe” (Biologist19) because they are not required to step outside of their “comfort zones” (Biologist7).

Putting Two Sources in Conversation with Each Other

Sometimes writers can use one interviewee to contribute to or complicate what another interviewee says. The following paragraph from Hutchcroft’s Booty Capitalism shows this practice at work. In addition to bringing two sources together, in this passage Hutchcroft also strategically incorporates paratextual insight gained from the interview process into his analysis. He uses the former governor’s laughter to showcase an attitude that directly contrasts with what the former bank supervisor says.

Even when the Central Bank has acted against those who milked their banks, former bank owners have been known to use personal connections, even up to the Supreme Court, to confound Central Bank discipline. Former Governor Jaime Laya noted that even martial law “didn’t seem to stop the lawsuits against Central Bank personnel.” He actually laughed as he told me how the Central Bank legal office has “never won a case.” But the former head of the bank supervision sector, who has herself been sued, doesn’t find it a laughing matter: “Why only in this country,” she exclaimed, “do the regulators go to the jail, and the bankers go scot-free?” (9)

Providing a Profile/Telling a Story

Sometimes your writing needs to focus on your interview subject as a full and complex individual. In order to analyze an issue, you need to write about this individual’s background, family, and previous experiences. In this situation, you’ll weave together information you gained from your interviews with quotations from this person. This kind of writing is common when you are using interviews to develop ethnographis case studies. In the following example of this technique, Kate Vieira, a professor in the Department of Curriculum and Instruction at UW-Madison, tells the story of Jocélia, an undocumented Brazilian immigrant living in Massachusetts:

Jocélia, a 22 year-old Brazilian woman who grew up in a favela (shanty town) in Brazil, came to the United States to study and to earn money. When her visa expired and the small sum she had borrowed from cousins ran out, she quit her ESL classes and began to work illegally. When I met her in 2008, she had been in the United States for 4 years, had managed to buy a house for her mother in Brazil, and had plans to buy another one for herself and a car. To earn money as an undocumented worker, she held down two jobs: one from 3 p.m. to midnight and another from 5 a.m. to afternoon. One evening, exhausted from having not slept in days, she nodded off as she drove home from work, resulting in a serious accident that led her to a friend’s house in South Mills and to a Catholic retreat. When I came here, I was not a youth who had fun. I only worked, and this made me a little frustrated, you know? Sad, lonely, understand? And nobody could change my mind. I had to work . . . But the Lord showed me something different, that I can’t live only for work . . . So I went there [to the retreat] and I really felt that the Lord touched us. It was a very good experience . . . (444)

Attending to Language

As explored in greater depth in the discussion about verbatim transcription , sometimes you want to analyze or consider the language an individual uses or the implications of certain kinds of words or even pauses. For example, in the following passage, Beth Godbee, a writing and rhetoric professor at Marquette University, meticulously considers the implications of her subjects’ specific words and phrasing. Although this example is taken from Godbee’s analysis of a conversation she recorded between a writer Susan and a writing center tutor Kim as opposed to a direct interview she conducted, the attention she pays to language could just as well be applied to information from an interview.

Kim reinforces Susan’s qualifications: “You’re gonna—you’re the specialist in this area. You know these kids; you see what know the effects are, and maybe where some change could be made” (lines 558-561). Here Kim revises her projection of Susan as a “specialist” in the future tense (“gonna,” as in “you’re going to be”) to a statement of her current position (“you’re,” or “you are now”). By repairing her speech mid-utterance, Kim emphasizes Susan’s current status and qualifications to write, thereby reframing her institutional power to assert her right to speak. (185)

Summarizing, Paraphrasing, or Quoting

As the above examples show, interview data is incorporated into writing through summaries, paraphrases, or quotations. In some ways this makes working with interviews just like working from any other kind of outside. As you choose between summarizing, paraphrasing, or quoting, a guiding question for you to consider is: What is most important about this information?

• Is it the overall story it tells or the general perspective it provides? Then summarizing might be the best option.
• Is it the particular take on a complex issue? Then paraphrasing that idea in your own words to make it as understandable as possible might be best.
• Is it the memorability, specificity, or authenticity of the language the source used? Then probably go with a quote, but be sure to contextualize this quotation by providing necessary background and commentary.

Of course, in working with interview data, you might go with all three incorporation strategies by, for example, summarizing early in a paragraph to provide an overall sense of what this source is saying, paraphrasing a key idea or two, and then including a poignant quotation that exemplifies the argument you are making. For more information about quoting and paraphrasing outside sources in your writing, check out our resources on this issue .

Referring to your Interviewees

In certain writing situations, you are expected to identify the people you interviewed by using their real names. This is often the case in journalistic writing as well as when you have consulted with an expert on an issue. But, even in these writing contexts, you must receive permission from them to associate their words and insights with their names by clearly establishing whether or not they are talking with you “on the record.”

However, when you are conducting interviews for academic research, you are frequently expected to use pseudonyms so that your subjects’ responses are confidential. Protecting your subjects’ privacy should be your primary priority. They are giving you access to personal experiences and trusting you with their individual insights and observations; you must honor that trust by anonymizing their identities so that readers can’t figure out who your subjects were. Developing a research methodology that keeps all of your data confidential is an important part of the IRB (Internal Review Board) process, and in order to receive permission to do research at your institution you’ll need a plan that outlines how you’ll achieve confidentiality. Part of that plan will involve using different names for your subjects. But selecting pseudonyms is a bigger issue than just choosing different names at random.

Ruth Allen and Janine L. Wiles, Social and Community Health scholars at the University of Auckland, have closely considered the many issues surrounding pseudonym selection in connection to their original psychological and health-related research. They advocate that researchers think critically about this process and even bring their subjects into these discussions of identity and confidentiality. You need to be thoughtful about what aspects of your subjects’ true identities you are communicating or obscuring through the pseudonyms you use. The following questions are adapted from ones Allen and Wiles recommend researchers ask themselves when preparing to use pseudonyms for participants:

• Does the researcher or the participant choose the pseudonym? How does this issue get talked about with the participants?
• Is it important, valuable, or expected to use first name or also include last names and/or titles (i.e., Cara, Mr. Terrance, Dr. Jean Nichols)?
• Within the context of this writing, should the names to be associated with a specific gender, ethnicity, and/or culture? Should those nominal identity markers align with the participants’ actual identities?
• Do pseudonyms need to be selected for other people, places, activities, and organizations mentioned in the interview? And if so, who makes those choices?

How you answer these questions should be informed by your specific context. For example, in relation to that fourth question, if a participant is talking supportively about a small on-campus organization that you want to bring attention to through your writing, it might make sense to refer to this organization by name even though its size might make it harder to disguise your participant’s identity. However, if your interviewee is speaking critically about a large, multi-national corporation where she works, you might want to develop a pseudonym for that company in order to protect this individual as much as possible.

Using Verbatim or Non-Verbatim

When you are conducting interviews, you are engaging people in very focused conversation. But when we converse, we say “like” a lot and “um” and “ah.” We start sentences and then interrupt ourselves and never return to complete those earlier thoughts. Conversation is never as direct and naturally coherent as writing can be. As a result, when you’re representing other people’s speech, you need to decide if you’ll be employing what is called “verbatim transcription” or “non-verbatim transcription.”

In “verbatim transcriptions,” you write out what people say exactly as they say it. You include all the filler words, false starts, and grammatical inconsistencies. You may even choose to include coughs and laughs. Scholars have traditionally upheld verbatim accounts as being accurate depictions of the interview process, but as Blake Poland pointed out, “much of the emotional context of the interview as well as nonverbal communication are not captured at all well in audiotape records, so that the audiotape itself is not strictly a verbatim record of the interview” (291). “Non-verbatim transcriptions,” (sometimes called “intelligent transcription”) respond to this acknowledged gap between the complexities of real conversation and the limitations of writing by encouraging writers to focus on the primary substance of participants’ quotes. In “non-verbatim transcriptions, you eliminate the unnecessary utterances like “er,” “well,” and “you know” and just include the foundational meaning of the interviewees’ words.” For example:

Verbatim Transcription : Well, you see, I was [pause] the problem, as I saw it, was more of a, a matter of representation, you know? How can I, like, be the one that’s just out there just declaring the way things are when I’ve not even, like, you know, experiencing the whole process for myself? Non-verbatim Transcription : The problem, as I saw it, was more a matter of representation. How can I be the one that’s out there declaring the way things are when I’ve not even experienced the whole process for myself?

The choice to use verbatim or non-verbatim transcription in quoting your participants should be informed by intentional considerations you are making as a writer. There are good reasons to use either forms. As Mahesh Kumar has identified in a blog post for the Transcription Certification Institute, verbatim transcription is useful for showcasing the thought process by which interview participants develop their ideas. False starts and self-corrections track down how someone is thinking about an issue in real time, and some fillers can be useful expressions of personality. Additionally, some linguistics research and conversation analysis methodologies expect highly structured, verbatim transcriptions that even account for pauses and simultaneous dialogue. However, quotations presented through non-verbatim transcriptions are clearer and easier to read and enable you to present your interview subjects as articulate (Poland 292). Whether you go with verbatim or non-verbatim transcription, make sure that you are being consistent with this choice across your article, paper, report, or essay. Also, if it’s common in the genre you are writing to discuss your methodology choices, it may be useful to clarify which transcription form you have chosen to use and why this was an appropriate choice.

Works Cited

Allen, Ruth E.S., and Janine L. Wiles. “A Rose by Any Other Name: Participants Choosing Research Pseudonyms.” Qualitative Research in Psychology , Dec. 2015. Research Gate , doi: 10.1080/14780887.2015.1133746.

Bogdan, Robert C., and Sari Knopp Biklen. Qualitative Research for Education: An Introduction to Theories and Methods . 5 th ed., Pearson, 2007.

Calvert, Jane, and Joan H. Fujimura. “Calculating Life? Duelling Discourses in Interdisciplinary Systems Biology.” Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , vol. 42, no. 2l, 2011. Science Direct , https://doi.org/10.1016/j.shpsc.2010.11.022 .

Godbee, Beth. “Toward Explaining the Transformative Power of Talk About, Around, and for Writing.” Research in the Teaching of English , vol. 47, no. 2, 2012, pp. 171-97.

Grutsch McKinney, Jackie. Strategies for Writing Center Research . Parlor Press, 2016.

Herrera, Yoshiko M. “The 2002 Russian Census: Institutional Reform at Goskomstar.”  Post-Soviet Affairs , vol, 20, no. 4, 2004, pp. 350-86.

Hutchcroft, Paul D. Booty Capitalism: The Politics of Banking in the Philippines , Ateneo de Manila University Press, 1998.

Kinkead, Joyce. Researching Writing: An Introduction to Research Methods . University Press of Colorado, 2015.

Kumar, Mahesh. “Verbatim Vs Non-Verbatim Transcription: Differences, Requirements, & Jobs.” Transcription Certification Institute , 5 December 2017. Accessed online 19 June 2017. https://blog.transcriptioncertificationinstitute.org/verbatim-vs-non-verbatim-transcription-differences-requirements-jobs/.

Poland, Blake D. “Transcription Quality as an Aspect of Rigor in Qualitative Research.” Qualitative Inquiry , no. 1, vol. 3, 1995, pp. 290-310.

Seidman, Irving. Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences . 3 rd ed., Teachers College Press, 2006.

Vieira, Kate. “Undocumented in a Documentary Society: Textual Borders and Transnational Religious Literacies.”  Written Communication , vol 28, no. 4, 2011, pp. 436-61.

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Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

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Chapter 10: Qualitative Data Collection & Analysis Methods

10.5 Analysis of Qualitative Interview Data

Analysis of qualitative interview data typically begins with a set of transcripts of the interviews conducted. Obtaining said transcripts requires either having taken exceptionally good notes during an interview or, preferably, recorded the interview and then transcribed it. To transcribe an interview means to create a complete, written copy of the recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. In general, it is best to aim for a verbatim transcription, i.e., one that reports word for word exactly what was said in the recorded interview. If possible, it is also best to include nonverbal responses in the written transcription of an interview (if the interview is completed face-to-face, or some other form of visual contact is maintained, such as with Skype). Gestures made by respondents should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by respondents.

If you have the time, it is best to transcribe your interviews yourself. If the researcher who conducted the interviews transcribes them herself, that person will also be able to record associated nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. Interviewees may roll their eyes, wipe tears from their face, and even make obscene gestures that speak volumes about their feelings; however, such non-verbal gestures cannot be recorded, and being able to remember and record in writing these details as it relates to the transcribing of interviews is invaluable.

Overall, the goal of analysis is to reach some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. Analysis of qualitative interview data often works inductively (Glaser & Strauss, 1967; Patton, 2001). To move from the specific observations an interviewer collects to identifying patterns across those observations, qualitative interviewers will often begin by reading through transcripts of their interviews and trying to identify codes. A code is a shorthand representation of some more complex set of issues or ideas. The process of identifying codes in one’s qualitative data is often referred to as coding . Coding involves identifying themes across interview data by reading and re-reading (and re-reading again) interview transcripts, until the researcher has a clear idea about what sorts of themes come up across the interviews. Coding helps to achieve the goal of data management and data reduction (Palys & Atchison, 2014, p. 304).

Coding can be inductive or deductive. Deductive coding is the approach used by research analysts who have a well-specified or pre-defined set of interests (Palys & Atchison, 2014, P. 304). The process of deductive coding begins with the analyst utilizing those specific or pre-defined interests to identify “relevant” passages, quotes, images, scenes, etc., to develop a set of preliminary codes (often referred to as descriptive coding ). From there, the analyst elaborates on these preliminary codes, making finer distinctions within each coding category (known as interpretative coding ). Pattern coding is another step an analyst might take as different associations become apparent. For example, if you are studying at-risk behaviours in youth, and you discover that the various behaviours have different characteristics and meanings depending upon the social context (e.g., school, family, work) in which the various behaviours occur, you have identified a pattern (Palys & Atchison, 2014, p. 304).

In contrast, inductive coding begins with the identification of general themes and ideas that emerge as the researcher reads through the data. This process is also referred to as open coding (Palys & Atchison, 2014, p. 305), because it will probably require multiple analyses. As you read through your transcripts, it is likely that you will begin to see some commonalities across the categories or themes that you’ve jotted down (Saylor Academy, 2012). The open coding process can go one of two ways: either the researcher elaborates on a category by making finer, and then even finer distinctions, or the researcher starts with a very specific descriptive category that is subsequently collapsed into another category (Palys & Atchison, 2014, p. 305). In other words, the development and elaboration of codes arise out of the material that is being examined.

The next step for the research analyst is to begin more specific coding, which is known as focused or axial coding . Focused coding involves collapsing or narrowing themes and categories identified in open coding by reading through the notes you made while conducting open coding, identifying themes or categories that seem to be related, and perhaps merging some. Then give each collapsed/merged theme or category a name (or code) and identify passages of data that fit each named category or theme. To identify passages of data that represent your emerging codes, you will need to read through your transcripts several times. You might also write up brief definitions or descriptions of each code. Defining codes is a way of giving meaning to your data, and developing a way to talk about your findings and what your data means (Saylor Academy, 2012).

As tedious and laborious as it might seem to read through hundreds of pages of transcripts multiple times, sometimes getting started with the coding process is actually the hardest part. If you find yourself struggling to identify themes at the open coding stage, ask yourself some questions about your data. The answers should give you a clue about what sorts of themes or categories you are reading (Saylor Academy, 2012). (Lofland and Lofland,1995, p. 2001) identify a set of questions that are useful when coding qualitative data. They suggest asking the following:

• Of what topic, unit, or aspect is this an instance?
• What question about a topic does this item of data suggest?
• What sort of answer to a question about a topic does this item of data suggest (i.e., what proposition is suggested)?

Asking yourself these questions about the passages of data that you are reading can help you begin to identify and name potential themes and categories.

Table 10.3 “ Interview coding” example is drawn from research undertaken by Saylor Academy (Saylor Academy, 2012) where she presents two codes that emerged from her inductive analysis of transcripts from her interviews with child-free adults. Table 10.3 also includes a brief description of each code and a few (of many) interview excerpts from which each code was developed.

Table 10.3 Interview coding

Just as quantitative researchers rely on the assistance of special computer programs designed to help sort through and analyze their data, so, do qualitative researchers. Where quantitative researchers have SPSS and MicroCase (and many others), qualitative researchers have programs such as NVivo ( http://www.qsrinternational.com ) and Atlasti ( http://www.atlasti.com ). These are programs specifically designed to assist qualitative researchers to organize, manage, sort, and analyze large amounts of qualitative data. The programs allow researchers to import interview transcripts contained in an electronic file and then label or code passages, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

How to Write an Impressive Thesis Results Section

After collecting and analyzing your research data, it’s time to write the results section. This article explains how to write and organize the thesis results section, the differences in reporting qualitative and quantitative data, the differences in the thesis results section across different fields, and the best practices for tables and figures.

What is the thesis results section?

The thesis results section factually and concisely describes what was observed and measured during the study but does not interpret the findings. It presents the findings in a logical order.

What should the thesis results section include?

• Include all relevant results as text, tables, or figures
• Report the results of subject recruitment and data collection
• For qualitative research, present the data from all statistical analyses, whether or not the results are significant
• For quantitative research, present the data by coding or categorizing themes and topics
• Present all secondary findings (e.g., subgroup analyses)
• Include all results, even if they do not fit in with your assumptions or support your hypothesis

What should the thesis results section not include?

• If the study involves the thematic analysis of an interview, don’t include complete transcripts of all interviews. Instead, add these as appendices
• Don’t present raw data. These may be included in appendices
• Don’t include background information (this should be in the introduction section )
• Don’t speculate on the meaning of results that do not support your hypothesis. This will be addressed later in the discussion and conclusion sections.
• Don’t repeat results that have been presented in tables and figures. Only highlight the pertinent points or elaborate on specific aspects

How should the thesis results section be organized?

The opening paragraph of the thesis results section should briefly restate the thesis question. Then, present the results objectively as text, figures, or tables.

Quantitative research presents the results from experiments and  statistical tests , usually in the form of tables and figures (graphs, diagrams, and images), with any pertinent findings emphasized in the text. The results are structured around the thesis question. Demographic data are usually presented first in this section.

For each statistical test used, the following information must be mentioned:

• The type of analysis used (e.g., Mann–Whitney U test or multiple regression analysis)
• A concise summary of each result, including  descriptive statistics   (e.g., means, medians, and modes) and  inferential statistics   (e.g., correlation, regression, and  p  values) and whether the results are significant
• Any trends or differences identified through comparisons
• How the findings relate to your research and if they support or contradict your hypothesis

Qualitative research   presents results around key themes or topics identified from your data analysis and explains how these themes evolved. The data are usually presented as text because it is hard to present the findings as figures.

For each theme presented, describe:

• General trends or patterns observed
• Significant or representative responses
• Relevant quotations from your study subjects

Relevant characteristics about your study subjects

Differences among the results section in different fields of research

Nevertheless, results should be presented logically across all disciplines and reflect the thesis question and any hypotheses that were tested.

The presentation of results varies considerably across disciplines. For example, a thesis documenting how a particular population interprets a specific event and a thesis investigating customer service may both have collected data using interviews and analyzed it using similar methods. Still, the presentation of the results will vastly differ because they are answering different thesis questions. A science thesis may have used experiments to generate data, and these would be presented differently again, probably involving statistics. Nevertheless, results should be presented logically across all disciplines and reflect the thesis question and any  hypotheses that were tested.

Differences between reporting thesis results in the Sciences and the Humanities and Social Sciences (HSS) domains

In the Sciences domain (qualitative and experimental research), the results and discussion sections are considered separate entities, and the results from experiments and statistical tests are presented. In the HSS domain (qualitative research), the results and discussion sections may be combined.

There are two approaches to presenting results in the HSS field:

• If you want to highlight important findings, first present a synopsis of the results and then explain the key findings.
• If you have multiple results of equal significance, present one result and explain it. Then present another result and explain that, and so on. Conclude with an overall synopsis.

Best practices for using tables and figures

The use of figures and tables is highly encouraged because they provide a standalone overview of the research findings that are much easier to understand than wading through dry text mentioning one result after another. The text in the results section should not repeat the information presented in figures and tables. Instead, it should focus on the pertinent findings or elaborate on specific points.

Some popular software programs that can be used for the analysis and presentation of statistical data include  Statistical Package for the Social Sciences (SPSS ) ,  R software ,  MATLAB , Microsoft Excel,  Statistical Analysis Software (SAS) ,  GraphPad Prism , and  Minitab .

The easiest way to construct tables is to use the  Table function in Microsoft Word . Microsoft Excel can also be used; however, Word is the easier option.

General guidelines for figures and tables

• Figures and tables must be interpretable independent from the text
• Number tables and figures consecutively (in separate lists) in the order in which they are mentioned in the text
• All tables and figures must be cited in the text
• Provide clear, descriptive titles for all figures and tables
• Include a legend to concisely describe what is presented in the figure or table

Figure guidelines

• Label figures so that the reader can easily understand what is being shown
• Use a consistent font type and font size for all labels in figure panels
• All abbreviations used in the figure artwork should be defined in the figure legend

Table guidelines

• All table columns should have a heading abbreviation used in tables should be defined in the table footnotes
• All numbers and text presented in tables must correlate with the data presented in the manuscript body

Quantitative results example : Figure 3 presents the characteristics of unemployed subjects and their rate of criminal convictions. A statistically significant association was observed between unemployed people <20 years old, the male sex, and no household income.

Qualitative results example: Table 5 shows the themes identified during the face-to-face interviews about the application that we developed to anonymously report corruption in the workplace. There was positive feedback on the app layout and ease of use. Concerns that emerged from the interviews included breaches of confidentiality and the inability to report incidents because of unstable cellphone network coverage.

Table 5. Themes and selected quotes from the evaluation of our app designed to anonymously report workplace corruption.

Tips for writing the thesis results section

• Do not state that a difference was present between the two groups unless this can be supported by a significant  p-value .
• Present the findings only . Do not comment or speculate on their interpretation.
• Every result included  must have a corresponding method in the methods section. Conversely, all methods  must have associated results presented in the results section.
• Do not explain commonly used methods. Instead, cite a reference.
• Be consistent with the units of measurement used in your thesis study. If you start with kg, then use the same unit all throughout your thesis. Also, be consistent with the capitalization of units of measurement. For example, use either “ml” or “mL” for milliliters, but not both.
• Never manipulate measurement outcomes, even if the result is unexpected. Remain objective.

Results vs. discussion vs. conclusion

Results are presented in three sections of your thesis: the results, discussion, and conclusion.

• In the results section, the data are presented simply and objectively. No speculation or interpretation is given.
• In the discussion section, the meaning of the results is interpreted and put into context (e.g., compared with other findings in the literature ), and its importance is assigned.
• In the conclusion section, the results and the main conclusions are summarized.

A thesis is the most crucial document that you will write during your academic studies. For professional thesis editing and thesis proofreading services , visit Enago Thesis Editing for more information.

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Review Checklist

Have you  completed all data collection procedures and analyzed all results ?

Have you  included all results relevant to your thesis question, even if they do not support your hypothesis?

Have you reported the results  objectively , with no interpretation or speculation?

For quantitative research, have you included both  descriptive and  inferential statistical results and stated whether they support or contradict your hypothesis?

Have you used  tables and figures to present all results?

In your thesis body, have you presented only the pertinent results and elaborated on specific aspects that were presented in the tables and figures?

Are all tables and figures  correctly labeled and cited in numerical order in the text?

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How to Analyze Interview Transcripts in Qualitative Research

Rev › Blog › Transcription Blog › How to Analyze Interview Transcripts in Qualitative Research

Studies take time, accuracy, and a drive to provide excellent information, and qualitative research is a critical part of any successful study. You may be wondering how qualitative data adds to a paper or report, given that it’s not the hard “science” we often see highlighted the most often.

How Do You Analyze Qualitative Interviews?

There are two main approaches to qualitative analysis: inductive and deductive . What’s more, there are two types of inductive qualitative analysis to choose from. These are called thematic content analysis and narrative analysis, both of which call for an unstructured approach to research.

Inductive Methods of Analyzing Interview Transcripts

A thematic content analysis begins with weeding out biases and establishing your overarching impressions of the data. Rather than approaching your data with a predetermined framework, identify common themes as you search the materials organically. Your goal is to find common patterns across the data set.

A narrative analysis involves making sense of your interview respondents’ individual stories. Use this type of qualitative data analysis to highlight important aspects of their stories that will best resonate with your readers. And, highlight critical points you have found in other areas of your research.

Deductive Approach to Qualitative Analysis

Deductive analysis , on the other hand, requires a structured or predetermined approach. In this case, the researcher will build categories in advance of their analysis. Then, they’ll map connections in the data to those specific categories.

Each of these qualitative analysis methods lends its benefits to the research effort. Inductive analyses will produce more nuanced findings. Meanwhile, deductive analyses allow the researcher to point to key themes essential to their research.

Successful qualitative research hinges on the accuracy of your data. This can be harder to achieve than with quantitative research. It’s easy to lose important facts and meaning as you transition qualitative data from the source to your published content. This makes transcription a vital tool in maintaining integrity and relaying information in an unbiased way that’s useful for readers and adds appropriate context to the journal or study.

How to Transcribe a Qualitative Interview

Accurate transcription begins early in the interview process, even before you start interviewing. Here are the steps to transcribing a qualitative interview.

1. Collect Feedback for Qualitative Research

There are dozens of ways to gather qualitative data. Recording and accurately transcribing interviews is among the best methods to avoid inaccuracies and data loss, and researchers should consider this approach over simply taking notes firsthand.

Make sure you have a reliable way to record, whether the interview takes place in person, over the phone, or as part of a video call. Depending on the interview method, you may record a video or an audio-only format. Here are some tips depending on where the interview takes place:

• These apps can also be used for over-the-phone interviews.
• For video interviews , we recommend taking advantage of one of our transcription integrations , such as Zoom. Rev also has an API available for those who want to streamline their workflow even further by integrating Rev directly into their processes and platforms.

2. Organize Your Research Recordings

You should ensure that your audio or video files are easy to save, compile, and share. To do this, be sure to adopt easy-to-remember naming conventions as well to ensure they stay organized. An example of a naming convention that is simple to remember and recreate includes “Date.LastNameofSource.Topic”.

3. Transcribe All the Interviews and Focus Group Recordings

The next critical step is transcription. Done manually, this is a long and tedious process that can add hours, days, or even months to your report-writing process. There are dozens of pitfalls when performing transcriptions manually as well, as it can be hard to pick up words spoken in a heavy dialect or quiet tone. You also want to avoid having to transcribe all the “umms” and “ems” that occur when a source is speaking naturally.

Rev provides a variety of transcription services that take the tedium and guesswork out of the research process. You can choose to edit out all of the “umms,” while ensuring that heavy accents or muffled voices are picked up by the recording service.

You can order transcripts from Rev with both audio and video recordings. Once you’ve received your professional transcripts from Rev, you can begin your qualitative analysis.

The 6 Steps of Qualitative Interview Data Analysis

Among qualitative interview data analysis methods, thematic content analysis is perhaps the most common and effective method. It can also be one of the most trustworthy , increasing the traceability and verification of an analysis when done correctly. The following are the six main steps of a successful thematic analysis of your transcripts.

1. Read the Transcripts

By now, you will have accessed your transcript files as digital files in the cloud or have downloaded them to your computer for offline viewing. Start by browsing through your transcripts and making notes of your first impressions. You will be able to identify common themes. This will help you with your final summation of the data.

Next, read through each transcript carefully. Evidence of themes will become stronger, helping you to hone in on important insights.

You must identify bias during this step as well. Biases can appear in the data, among the interviewees, and even within your objectives and methodologies. According to SAGE Publishing , researchers should “acknowledge preconceived notions and actively work to neutralize them” at this early step.

2. Annotate the Transcripts

Annotation is the process of labeling relevant words, phrases, sentences, or sections with codes. These codes help identify important qualitative data types and patterns. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant.  Annotations will help you organize your data for dissemination .

Be generous with your annotations—don’t hold back. You will have an opportunity to eliminate or consolidate them later. It’s best to do more here, so you don’t have to come back to find more opportunities later.

3. Conceptualize the Data

Conceptualizing qualitative data is the process of aligning data with critical themes you will use in your published content. You will have identified many of these themes during your initial review of the transcripts.

To conceptualize,  create categories and subcategories  by grouping the codes you created during annotation. You may eliminate or combine certain codes rather than using all the codes you created. Keep only the codes you deem relevant to your analysis.

4. Segment the Data

Segmentation is the process of positioning and  connecting your categories . This allows you to establish the bulk of your data cohesively. Start by labeling your categories and then describe the connections between them.

You can use these descriptions to improve your final published content.

• Create a spreadsheet  to easily compile your data.
• Then, use the columns to structure important variables of your data analysis using codes as tools for reference.
• Create a separate tab for the front of the document that contains a coding table. This glossary contains important codes used in the segmentation process. This will help you and others quickly identify what the codes are referring to.

5. Analyze the Segments

You’re now ready to take a  deep dive into your data segments . Start by determining if there is a hierarchy among your categories. Determine if one is more important than the other, or draw a figure to summarize the results. At this stage, you may also want to align qualitative data with any quantitative data you collected.

6. Write the Results

Your analysis of the content is complete—you’re ready to transition your findings into the real body of your content. Use your insights to build and verify theories, answer key questions in your field, and back aims and objectives. Describe your categories and how they are connected using a neutral, objective voice.

Although you will pull heavily from your own research, be sure to publish content in the context of your field. Interpret your results in light of relevant studies, theories, and concepts related to your study.

Why Use Interviews for Qualitative Data

Unlike quantitative data, which is certainly important, a qualitative analysis adds color to academic and business reports. It offers perspective and can make a report more readable, add context, and inspire thoughtful discussion beyond the report.

As we’ve observed, transcribing qualitative interviews is crucial to getting less measurable data from direct sources. They allow researchers to provide relatable stories and perspectives and even quote important contributors directly. Lots of qualitative data from interviews enables authors to avoid embellishment and maintain the integrity of their content as well.

So, how do you conduct interview data analysis on qualitative data to pull key insights and strengthen your reports? Transcribing interviews is one of the most useful tools available for this task.

As a researcher, you need to make the most of recorded interviews . Interview transcripts allow you to use the best qualitative analysis methods. Plus, you can focus only on tasks that add value to your research effort.

Transcription is Essential to Qualitative Research Analysis

Qualitative data is often elusive to researchers. Transcripts allow you to capture original, nuanced responses from your respondents. You get their response naturally using their own words—not a summarized version in your notes.

You can also go back to the original transcript at any time to see what was said as you gain new context. The editable digital transcript files are incredibly easy to work with, saving you time and giving you speaker tags, time marks, and other tools to ensure you can find what you need within a transcript quickly.

When creating a report, accuracy matters, but efficiency matters, as well. Rev offers a seamless way of doing the transcription for you, saving you time and allowing you to focus on high-quality work instead. Consider Rev as your transcription service provider for qualitative research analysis — try Rev’s AI or Human Transcription services today.

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Qualitative Research 101: Interviewing

5 Common Mistakes To Avoid When Undertaking Interviews

By: David Phair (PhD) and Kerryn Warren (PhD) | March 2022

Undertaking interviews is potentially the most important step in the qualitative research process. If you don’t collect useful, useable data in your interviews, you’ll struggle through the rest of your dissertation or thesis.  Having helped numerous students with their research over the years, we’ve noticed some common interviewing mistakes that first-time researchers make. In this post, we’ll discuss five costly interview-related mistakes and outline useful strategies to avoid making these.

Overview: 5 Interviewing Mistakes

• Not having a clear interview strategy /plan
• Not having good interview techniques /skills
• Not securing a suitable location and equipment
• Not having a basic risk management plan
• Not keeping your “ golden thread ” front of mind

1. Not having a clear interview strategy

The first common mistake that we’ll look at is that of starting the interviewing process without having first come up with a clear interview strategy or plan of action. While it’s natural to be keen to get started engaging with your interviewees, a lack of planning can result in a mess of data and inconsistency between interviews.

There are several design choices to decide on and plan for before you start interviewing anyone. Some of the most important questions you need to ask yourself before conducting interviews include:

• What are the guiding research aims and research questions of my study?
• Will I use a structured, semi-structured or unstructured interview approach?
• How will I record the interviews (audio or video)?
• Who will be interviewed and by whom ?
• What ethics and data law considerations do I need to adhere to?
• How will I analyze my data? 

Let’s take a quick look at some of these.

The core objective of the interviewing process is to generate useful data that will help you address your overall research aims. Therefore, your interviews need to be conducted in a way that directly links to your research aims, objectives and research questions (i.e. your “golden thread”). This means that you need to carefully consider the questions you’ll ask to ensure that they align with and feed into your golden thread. If any question doesn’t align with this, you may want to consider scrapping it.

Another important design choice is whether you’ll use an unstructured, semi-structured or structured interview approach . For semi-structured interviews, you will have a list of questions that you plan to ask and these questions will be open-ended in nature. You’ll also allow the discussion to digress from the core question set if something interesting comes up. This means that the type of information generated might differ a fair amount between interviews.

Contrasted to this, a structured approach to interviews is more rigid, where a specific set of closed questions is developed and asked for each interviewee in exactly the same order. Closed questions have a limited set of answers, that are often single-word answers. Therefore, you need to think about what you’re trying to achieve with your research project (i.e. your research aims) and decided on which approach would be best suited in your case.

It is also important to plan ahead with regards to who will be interviewed and how. You need to think about how you will approach the possible interviewees to get their cooperation, who will conduct the interviews, when to conduct the interviews and how to record the interviews. For each of these decisions, it’s also essential to make sure that all ethical considerations and data protection laws are taken into account.

Finally, you should think through how you plan to analyze the data (i.e., your qualitative analysis method) generated by the interviews. Different types of analysis rely on different types of data, so you need to ensure you’re asking the right types of questions and correctly guiding your respondents.

Simply put, you need to have a plan of action regarding the specifics of your interview approach before you start collecting data. If not, you’ll end up drifting in your approach from interview to interview, which will result in inconsistent, unusable data.

2. Not having good interview technique

While you’re generally not expected to become you to be an expert interviewer for a dissertation or thesis, it is important to practice good interview technique and develop basic interviewing skills .

Let’s go through some basics that will help the process along.

Firstly, before the interview , make sure you know your interview questions well and have a clear idea of what you want from the interview. Naturally, the specificity of your questions will depend on whether you’re taking a structured, semi-structured or unstructured approach, but you still need a consistent starting point . Ideally, you should develop an interview guide beforehand (more on this later) that details your core question and links these to the research aims, objectives and research questions.

Before you undertake any interviews, it’s a good idea to do a few mock interviews with friends or family members. This will help you get comfortable with the interviewer role, prepare for potentially unexpected answers and give you a good idea of how long the interview will take to conduct. In the interviewing process, you’re likely to encounter two kinds of challenging interviewees ; the two-word respondent and the respondent who meanders and babbles. Therefore, you should prepare yourself for both and come up with a plan to respond to each in a way that will allow the interview to continue productively.

To begin the formal interview , provide the person you are interviewing with an overview of your research. This will help to calm their nerves (and yours) and contextualize the interaction. Ultimately, you want the interviewee to feel comfortable and be willing to be open and honest with you, so it’s useful to start in a more casual, relaxed fashion and allow them to ask any questions they may have. From there, you can ease them into the rest of the questions.

As the interview progresses , avoid asking leading questions (i.e., questions that assume something about the interviewee or their response). Make sure that you speak clearly and slowly , using plain language and being ready to paraphrase questions if the person you are interviewing misunderstands. Be particularly careful with interviewing English second language speakers to ensure that you’re both on the same page.

Engage with the interviewee by listening to them carefully and acknowledging that you are listening to them by smiling or nodding. Show them that you’re interested in what they’re saying and thank them for their openness as appropriate. This will also encourage your interviewee to respond openly.

Need a helping hand?

3. Not securing a suitable location and quality equipment

Where you conduct your interviews and the equipment you use to record them both play an important role in how the process unfolds. Therefore, you need to think carefully about each of these variables before you start interviewing.

Poor location: A bad location can result in the quality of your interviews being compromised, interrupted, or cancelled. If you are conducting physical interviews, you’ll need a location that is quiet, safe, and welcoming . It’s very important that your location of choice is not prone to interruptions (the workplace office is generally problematic, for example) and has suitable facilities (such as water, a bathroom, and snacks).

If you are conducting online interviews , you need to consider a few other factors. Importantly, you need to make sure that both you and your respondent have access to a good, stable internet connection and electricity. Always check before the time that both of you know how to use the relevant software and it’s accessible (sometimes meeting platforms are blocked by workplace policies or firewalls). It’s also good to have alternatives in place (such as WhatsApp, Zoom, or Teams) to cater for these types of issues.

Poor equipment: Using poor-quality recording equipment or using equipment incorrectly means that you will have trouble transcribing, coding, and analyzing your interviews. This can be a major issue , as some of your interview data may go completely to waste if not recorded well. So, make sure that you use good-quality recording equipment and that you know how to use it correctly.

To avoid issues, you should always conduct test recordings before every interview to ensure that you can use the relevant equipment properly. It’s also a good idea to spot check each recording afterwards, just to make sure it was recorded as planned. If your equipment uses batteries, be sure to always carry a spare set.

4. Not having a basic risk management plan

Many possible issues can arise during the interview process. Not planning for these issues can mean that you are left with compromised data that might not be useful to you. Therefore, it’s important to map out some sort of risk management plan ahead of time, considering the potential risks, how you’ll minimize their probability and how you’ll manage them if they materialize.

Common potential issues related to the actual interview include cancellations (people pulling out), delays (such as getting stuck in traffic), language and accent differences (especially in the case of poor internet connections), issues with internet connections and power supply. Other issues can also occur in the interview itself. For example, the interviewee could drift off-topic, or you might encounter an interviewee who does not say much at all.

You can prepare for these potential issues by considering possible worst-case scenarios and preparing a response for each scenario. For instance, it is important to plan a backup date just in case your interviewee cannot make it to the first meeting you scheduled with them. It’s also a good idea to factor in a 30-minute gap between your interviews for the instances where someone might be late, or an interview runs overtime for other reasons. Make sure that you also plan backup questions that could be used to bring a respondent back on topic if they start rambling, or questions to encourage those who are saying too little.

In general, it’s best practice to plan to conduct more interviews than you think you need (this is called oversampling ). Doing so will allow you some room for error if there are interviews that don’t go as planned, or if some interviewees withdraw. If you need 10 interviews, it is a good idea to plan for 15. Likely, a few will cancel , delay, or not produce useful data.

5. Not keeping your golden thread front of mind

We touched on this a little earlier, but it is a key point that should be central to your entire research process. You don’t want to end up with pages and pages of data after conducting your interviews and realize that it is not useful to your research aims . Your research aims, objectives and research questions – i.e., your golden thread – should influence every design decision and should guide the interview process at all times. 

A useful way to avoid this mistake is by developing an interview guide before you begin interviewing your respondents. An interview guide is a document that contains all of your questions with notes on how each of the interview questions is linked to the research question(s) of your study. You can also include your research aims and objectives here for a more comprehensive linkage. 

You can easily create an interview guide by drawing up a table with one column containing your core interview questions . Then add another column with your research questions , another with expectations that you may have in light of the relevant literature and another with backup or follow-up questions . As mentioned, you can also bring in your research aims and objectives to help you connect them all together. If you’d like, you can download a copy of our free interview guide here .

Recap: Qualitative Interview Mistakes

In this post, we’ve discussed 5 common costly mistakes that are easy to make in the process of planning and conducting qualitative interviews.

To recap, these include:

If you have any questions about these interviewing mistakes, drop a comment below. Alternatively, if you’re interested in getting 1-on-1 help with your thesis or dissertation , check out our dissertation coaching service or book a free initial consultation with one of our friendly Grad Coaches.

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6 Easy Steps in Learning How to Analyze Interview Data in Qualitative Studies

It’s understandable if you find qualitative data analysis quite challenging. Unlike quantitative methodologies, qualitative data analysis doesn’t follow a systematic, rigid, and formulaic process. Once you decide to analyze interview transcripts in qualitative research, the work is slightly more intuitive than tangible.

We won’t sugarcoat things and say that qualitative analysis is an easy task. However, there are practical steps you can take to avoid turning the entire process into something chaotic. In most cases, you’ll start by having accurate interview transcripts that you need to read over and over again. 

This article will guide teach you how to  transcribe and analyze interview data  for qualitative research.

Why It’s Important to Analyze Interview Data in Qualitative Research

Quantitative data may offer rigor to your research. However, it is qualitative analysis that will add color to your business or academic report. By taking this approach, you can add context to your data and make it more readable. It will inspire a more meaningful discussion that goes beyond the hard numbers in your report.

Most of the time, interviews allow you to acquire subjective data from direct sources. As a researcher, you’ll get relatable perspectives and stories. You can also get important quotes from subject matter experts. Interviews provide qualitative data that strengthens the integrity and eliminates embellishment in their research.

Now, how do you acquire the information you need for qualitative data? Well, transcribing interviews provides you with the best resources for qualitative analysis. You can add life to your research by interviewing the following sources:

• Business leaders
• Scientists and investors
• Trial or study participants
• Journal and research authors

You can include sources that may contribute to the quality of the research. Now, whenever you conduct an interview, you should use the right methods to ensure you’ll get the information you need. You must maximize your recorded interviews by transcribing them accurately. 

How to Get an Accurate Qualitative Interview Transcript

It’s more challenging to get the information you need in a qualitative interview. However, by taking a systematic approach, it will be easier for you to gather the data you need. Here are some steps to follow:

1. Gather Qualitative Data

Qualitative data can be collected through various means. For one, you can record the interview and take advantage of legal-grade transcription services. Taking this approach will help you avoid data loss and inaccuracies. This method is ideal for focus group discussions and customer interviews. With this process, respondents can be more open in answering the questions.

You may have above-average note-taking skills, but that’s usually unreliable in qualitative research. Whether you’re conducting the interview over the phone, on a video conference call, or in person, you can collect accurate data by recording the conversation. You can either record the interview in an audio-only or video format.

2. Compile Your Interview Recordings Neatly

Your interview video or audio files should be easy to find, identify, and share. Use file-naming conventions that are easy to recall and organize. Ideally, you must include the date of the interview, the source’s last name, and the discussion topic. 

No matter what file naming convention you choose, you need to be consistent in the format. This way, it’s easier for you to find the necessary files later when you analyze interview data.

3. Transcribe the Focus Group and Interview Recordings

The next step is to transcribe the recordings. Now, there are plenty of tools online that can automate the process. However, remember that you are working on qualitative research, and you need an accurate transcription.

So, your best bet here is to use 100% human-generated transcription services. GoTranscript offers verbatim results, allowing you to get legal-level accuracy for your qualitative interview recordings. If you’re following tight deadlines, you can opt for a rush order. This way, you’ll get your files within your requested turnaround time and begin your qualitative analysis.

What Are the Methods Used to Analyze Interview Data in Qualitative Research?

When it comes to qualitative data analysis, you can choose from two primary approaches – inductive and deductive. If you opt for the former, you can take an unstructured approach to your research by conducting narrative or thematic content analysis.

Inductive Method for Analyzing Qualitative Interview Transcripts

• Thematic content analysis – This method starts by identifying your overarching impressions of the information and weeding out biases. Instead of establishing a framework for approaching the data, you search the materials organically and look for common themes. You aim to discover the recurring patterns across the data set.
• Narrative content analysis – This method requires you to make sense of the individual stories of your interview respondents. It is the ideal approach for identifying important aspects of the data that resonate well with your readers. 

Deductive Method for Analyzing Qualitative Interview Transcripts

Deductive data analysis is a more systematic or structured approach. Before you begin to analyze interview data, you need to develop categories. During the process, you’ll connect information from the data to those pre-determined categories.

Both approaches to data analysis bring benefits to your research. If you want more nuanced findings, the inductive method is the ideal option. Now, if you want to point to key themes that are crucial to your research, opt for deductive analysis.

6 Practical Steps in Performing Qualitative Analysis of Interview Transcripts

Most of the time, researchers opt for thematic content analysis. Many find it to be the most effective and trustworthy method. The approach makes it easier for anyone to verify and trace the findings of the analysis. Here are six practical steps for conducting a thematic analysis of your interview transcripts:

Step 1: Go Through the Transcripts Carefully

At this point, you likely have a digital copy of your transcript files. You may have them stored locally on your computer or in the cloud. Begin by skimming through the transcripts, and along the process, write down your first impressions. This makes to easier to analyze interview data.

Taking this approach will help you identify common patterns in the data. Your quick notes will also be instrumental in helping you come up with a final summation of the data.

Now, you need to read every transcript carefully. After multiple run-throughs, the common themes will become more visible. As a result, you will be able to come up with crucial and significant insights. 

During this process, you should also identify biases. Even within your methodologies and objectives, biases will surface in the data and among the interviewees.  Sage Publishing  recommends that as early as this step, you should actively neutralize the preconceived notions you identify. This way, you can take an objective approach when you begin to analyze interview data.

Step 2: Begin Annotating the Transcripts

Once you get to this step, you need to label the key phrases, terms, sentences, or section codes on the transcript. Annotating your transcripts allows you to identify important qualitative data patterns and types. 

The labels can highlight the varying interviewee opinions, concepts, and data differences. This process makes it easier for you to organize your data set for dissemination. As a result, you’re cutting down the time it takes to analyze interview data.

Step 3: Align the Data with Important Themes

This step requires you to conceptualize the qualitative data by aligning it with important themes for your final content. At this phase, you will have identified the common patterns from the initial transcript reviews.

When conceptualizing the data, you need to group the codes from the annotation to create categories and subcategories. You can combine or eliminate certain codes instead of using all of them. You should identify and retain the codes that are relevant to your analysis. 

Step 4: Position and Connect Your Categories

Now, you need to segment the data by positioning and connecting your categories. During this step, you will be able to cohesively establish most of your data. You can begin by labeling the categories, and describing how they connect to each other. The descriptions will be instrumental in optimizing your final output. Here are the steps you can take:

• Compile your data conveniently by using a spreadsheet.
• Structure the data’s critical variables by labeling the columns with proper reference codes.
• The document containing the coding table should be on a separate tab. This tab functions as a glossary containing critical codes for the segmentation process. As a result, you can quickly identify what the codes are referring to.

Most researchers prefer doing all these steps electronically for convenience. However, some people prefer having a printed copy so they can highlight segments using pens. In any case, you should save a copy of the transcript files. You can print them out when you need to.

Step 5: Perform a Deep Analysis of the Data Segments

Once you’ve segmented the data, it’s time to perform a deep analysis. You can begin by identifying the categories’ hierarchy. You need to decide if one category is more important than the other. You can also summarize the results by drawing a figure. This stage should also let you align the qualitative data with your quantitative data.

Step 6: Write Your Findings

Once you’ve performed a deep analysis of your data, you’re ready to write the results into a body of content. Establish and verify theories by using your data insights. You can also answer key questions that your field may ask, and don’t forget to adequately support your research goals. Use an objective, neutral voice in describing how your categories connect to each other.

Naturally, you will use your own research and pull insights heavily from it. However, make sure to consider the context of your field when writing your results. Interpret your findings alongside relevant theories, studies, and concepts when you analyze interview data.

Accurate Transcription Is Necessary in Qualitative Research Analysis

Researchers often find qualitative data analysis challenging. As such, it is important to have transcripts that capture the nuanced, unadulterated responses from interviewees. You can get their organic responses because they are using their own words and not the shortened version from your notes.

What’s great about having a transcript is that you can go back to it whenever you gain find new context. However, to ensure a reasonable level of objectivity, you must ensure that your transcript is accurate.

With GoTranscript, you can have a convenient yet efficient way of transcribing your qualitative interviews. You can save time and focus on more important aspects of your research.

The Quintessence of Basic and Clinical Research and Scientific Publishing pp 717–731 Cite as

How to Present Results in a Research Paper

• Aparna Mukherjee 4 ,
• Gunjan Kumar 4 &
• Rakesh Lodha 5  
• First Online: 01 October 2023

663 Accesses

The results section is the core of a research manuscript where the study data and analyses are presented in an organized, uncluttered manner such that the reader can easily understand and interpret the findings. This section is completely factual; there is no place for opinions or explanations from the authors. The results should correspond to the objectives of the study in an orderly manner. Self-explanatory tables and figures add value to this section and make data presentation more convenient and appealing. The results presented in this section should have a link with both the preceding methods section and the following discussion section. A well-written, articulate results section lends clarity and credibility to the research paper and the study as a whole. This chapter provides an overview and important pointers to effective drafting of the results section in a research manuscript and also in theses.

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Acknowledgments

The book chapter is derived in part from our article “Mukherjee A, Lodha R. Writing the Results. Indian Pediatr. 2016 May 8;53(5):409-15.” We thank the Editor-in-Chief of the journal “Indian Pediatrics” for the permission for the same.

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Mukherjee, A., Kumar, G., Lodha, R. (2023). How to Present Results in a Research Paper. In: Jagadeesh, G., Balakumar, P., Senatore, F. (eds) The Quintessence of Basic and Clinical Research and Scientific Publishing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1284-1_44

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Writing your Dissertation:  Results and Discussion

When writing a dissertation or thesis, the results and discussion sections can be both the most interesting as well as the most challenging sections to write.

You may choose to write these sections separately, or combine them into a single chapter, depending on your university’s guidelines and your own preferences.

There are advantages to both approaches.

Writing the results and discussion as separate sections allows you to focus first on what results you obtained and set out clearly what happened in your experiments and/or investigations without worrying about their implications.This can focus your mind on what the results actually show and help you to sort them in your head.

However, many people find it easier to combine the results with their implications as the two are closely connected.

Check your university’s requirements carefully before combining the results and discussions sections as some specify that they must be kept separate.

Results Section

The Results section should set out your key experimental results, including any statistical analysis and whether or not the results of these are significant.

You should cover any literature supporting your interpretation of significance. It does not have to include everything you did, particularly for a doctorate dissertation. However, for an undergraduate or master's thesis, you will probably find that you need to include most of your work.

You should write your results section in the past tense: you are describing what you have done in the past.

Every result included MUST have a method set out in the methods section. Check back to make sure that you have included all the relevant methods.

Conversely, every method should also have some results given so, if you choose to exclude certain experiments from the results, make sure that you remove mention of the method as well.

If you are unsure whether to include certain results, go back to your research questions and decide whether the results are relevant to them. It doesn’t matter whether they are supportive or not, it’s about relevance. If they are relevant, you should include them.

Having decided what to include, next decide what order to use. You could choose chronological, which should follow the methods, or in order from most to least important in the answering of your research questions, or by research question and/or hypothesis.

You also need to consider how best to present your results: tables, figures, graphs, or text. Try to use a variety of different methods of presentation, and consider your reader: 20 pages of dense tables are hard to understand, as are five pages of graphs, but a single table and well-chosen graph that illustrate your overall findings will make things much clearer.

Make sure that each table and figure has a number and a title. Number tables and figures in separate lists, but consecutively by the order in which you mention them in the text. If you have more than about two or three, it’s often helpful to provide lists of tables and figures alongside the table of contents at the start of your dissertation.

Summarise your results in the text, drawing on the figures and tables to illustrate your points.

The text and figures should be complementary, not repeat the same information. You should refer to every table or figure in the text. Any that you don’t feel the need to refer to can safely be moved to an appendix, or even removed.

Make sure that you including information about the size and direction of any changes, including percentage change if appropriate. Statistical tests should include details of p values or confidence intervals and limits.

While you don’t need to include all your primary evidence in this section, you should as a matter of good practice make it available in an appendix, to which you should refer at the relevant point.

For example:

Details of all the interview participants can be found in Appendix A, with transcripts of each interview in Appendix B.

You will, almost inevitably, find that you need to include some slight discussion of your results during this section. This discussion should evaluate the quality of the results and their reliability, but not stray too far into discussion of how far your results support your hypothesis and/or answer your research questions, as that is for the discussion section.

See our pages: Analysing Qualitative Data and Simple Statistical Analysis for more information on analysing your results.

Discussion Section

This section has four purposes, it should:

• Interpret and explain your results
• Answer your research question
• Justify your approach
• Critically evaluate your study

The discussion section therefore needs to review your findings in the context of the literature and the existing knowledge about the subject.

You also need to demonstrate that you understand the limitations of your research and the implications of your findings for policy and practice. This section should be written in the present tense.

The Discussion section needs to follow from your results and relate back to your literature review . Make sure that everything you discuss is covered in the results section.

Some universities require a separate section on recommendations for policy and practice and/or for future research, while others allow you to include this in your discussion, so check the guidelines carefully.

Starting the Task

Most people are likely to write this section best by preparing an outline, setting out the broad thrust of the argument, and how your results support it.

You may find techniques like mind mapping are helpful in making a first outline; check out our page: Creative Thinking for some ideas about how to think through your ideas. You should start by referring back to your research questions, discuss your results, then set them into the context of the literature, and then into broader theory.

This is likely to be one of the longest sections of your dissertation, and it’s a good idea to break it down into chunks with sub-headings to help your reader to navigate through the detail.

Fleshing Out the Detail

Once you have your outline in front of you, you can start to map out how your results fit into the outline.

This will help you to see whether your results are over-focused in one area, which is why writing up your research as you go along can be a helpful process. For each theme or area, you should discuss how the results help to answer your research question, and whether the results are consistent with your expectations and the literature.

The Importance of Understanding Differences

If your results are controversial and/or unexpected, you should set them fully in context and explain why you think that you obtained them.

Your explanations may include issues such as a non-representative sample for convenience purposes, a response rate skewed towards those with a particular experience, or your own involvement as a participant for sociological research.

You do not need to be apologetic about these, because you made a choice about them, which you should have justified in the methodology section. However, you do need to evaluate your own results against others’ findings, especially if they are different. A full understanding of the limitations of your research is part of a good discussion section.

At this stage, you may want to revisit your literature review, unless you submitted it as a separate submission earlier, and revise it to draw out those studies which have proven more relevant.

Conclude by summarising the implications of your findings in brief, and explain why they are important for researchers and in practice, and provide some suggestions for further work.

You may also wish to make some recommendations for practice. As before, this may be a separate section, or included in your discussion.

The results and discussion, including conclusion and recommendations, are probably the most substantial sections of your dissertation. Once completed, you can begin to relax slightly: you are on to the last stages of writing!

Continue to: Dissertation: Conclusion and Extras Writing your Methodology

See also: Writing a Literature Review Writing a Research Proposal Academic Referencing What Is the Importance of Using a Plagiarism Checker to Check Your Thesis?

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Research Tips and Infromation

How to Write the Results Section of your Dissertation or Thesis?

Introduction

Organizing your results, providing context, presenting the data in results section, describing statistical analysis, reporting the findings in results section, supporting the findings, visual representation in results section.

As you progress on your journey towards completing your PhD or Post Graduate dissertation, one of the most critical sections that holds immense significance is the results section.

Results section serves as the pinnacle of your research, where you unveil the outcomes of your exhaustive efforts and shed light on the answers to your research questions. In this blog post, we will delve into the intricacies of the results section and explore how to effectively present and interpret your findings to leave a lasting impact.

Whether you’re conducting research in the field of biology, psychology, computer science, or any other discipline, the results section is where your data takes center stage. It is a space where you showcase your meticulous analysis, statistical methods, and the discoveries you’ve made along the way. By understanding the key components and best practices for constructing a compelling results section, you can present your findings in a manner that resonates with both your academic peers and the wider research community.

In this comprehensive guide, we will walk you through the fundamental elements of the results section, from organizing your data to choosing the appropriate visual representations. We will explore the importance of clear and concise reporting, emphasizing the significance of providing contextual information and highlighting any unexpected or groundbreaking discoveries.

Furthermore, we will discuss strategies for effectively interpreting your results, discussing their implications, and connecting them back to your research objectives. By mastering these skills, you will be able to demonstrate the significance of your work, contribute to the existing body of knowledge, and potentially pave the way for further research in your field.

Throughout the blog post, I will provide concrete examples from various disciplines to illustrate the implementation of these techniques. Additionally, I will offer valuable tips on avoiding common pitfalls, ensuring the accuracy and reliability of your results, and seeking feedback from your advisors or peers to enhance the quality of your analysis.

If you are in paucity of time, not confident of your writing skills and in a hurry to complete the writing task then you can think of hiring a research consultant that solves all your problems. Please visit my article on Hiring a Research consultant for your PhD tasks for further details.

Organizing the results of your study in a logical and coherent manner is crucial for effectively communicating your findings. By presenting your results in an organized structure, you enhance the clarity and readability of your dissertation. Here are some key considerations for organizing your results:

• Research studies often involve complex algorithms, software implementations, experimental data, and performance metrics. It is essential to organize these diverse elements in a cohesive manner to make it easier for readers to follow your research. A well-structured results section enables readers to understand the progression of your experiments and the relationship between different findings.
• Begin by reminding readers of the research questions or hypotheses that guided your study. This alignment helps establish a clear connection between the objectives of your research and the subsequent presentation of results. For example, if your research question focuses on evaluating the efficiency of a new sorting algorithm, you would present the experimental data, performance metrics, and comparative analyses specific to that algorithm in relation to the research question.
• Subsubsection 1.1: Experimental Setup
• Subsubsection 1.2: Experimental Results and Analysis
• Subsubsection 2.1: Performance Metrics
• Subsubsection 2.2: Comparative Results and Discussion

Remember to tailor the organization of your results section to the specific requirements of your research. The key is to provide a logical flow and structure that enables readers to easily comprehend and interpret your findings.

Providing context for the results of your study is essential to help readers understand the significance and implications of your findings. By offering background information and study design details, you establish a foundation upon which the results can be properly interpreted. Here are some key considerations for providing context:

• Before delving into the results, it is important to provide readers with relevant background information about the topic or problem being addressed. This may include a literature review of existing research, theories, or methodologies in the field. By doing so, you situate your work within the broader landscape of and demonstrate its relevance. Additionally, explain the design of your study, such as the specific algorithms, software frameworks, datasets, or hardware setups used. This ensures that readers understand the context in which your results were obtained.
• Provide a brief overview of the current state-of-the-art in image recognition algorithms and their limitations.
• Explain the specific challenges or gaps in the existing methods that motivated your research.
• Describe the design of your study, including the choice of machine learning techniques, datasets used for training and evaluation, preprocessing steps, and any hardware or software configurations.

By providing context, you allow readers to understand the background, motivation, and methodology behind your research. This sets the stage for better comprehension and interpretation of your results. Contextualizing your findings, as it helps establish the relevance, novelty, and potential impact of your research within the larger field.

Presenting data in a clear and organized manner is crucial for effectively communicating your results. The way you present your data can greatly impact the reader’s understanding and interpretation of your findings. Here are some key considerations for presenting data:

• Presenting performance metrics of different algorithms using a table to allow for easy comparison.
• Using a line graph to depict the improvement in accuracy over training iterations in a machine learning model.
• Employing a bar chart to compare the execution times of different algorithms on a specific dataset.
• Clear labelling and formatting of your data ensure that readers can easily understand and interpret the information presented. Label each table, figure, chart, or graph with a concise and descriptive title. Ensure that axes, legends, and labels are clearly labelled and units of measurement are specified. Use appropriate fonts, colours, and styles to enhance readability. Consider providing captions or footnotes to provide additional context or explanations where necessary.
• In the text, refer to a specific table presenting the accuracy results of different algorithms and explain how these results support your research hypothesis or contribute to the field.
• Discuss a figure showing the relationship between the number of training examples and the performance of a machine learning model, emphasizing its implications for scalability and generalization.

By presenting data in a visually appealing and well-organized manner, you enhance the clarity and accessibility of your results. Proper labelling, formatting, and referring to each table or figure in the text help readers navigate the information and grasp its significance. Remember to choose the most appropriate format for your data and use visuals to support and reinforce your findings.

The inclusion of statistical analyses in the results section is crucial for providing objective and quantitative evidence to support your findings. Statistical analyses help you draw meaningful conclusions from your data and determine the significance of observed results. Here are some key considerations for describing statistical analyses:

• Statistical analyses play a vital role in determining the reliability and significance of your findings. They provide a systematic and objective framework for interpreting the data and testing hypotheses. Discuss the importance of including statistical analyses in the results section to demonstrate the rigour and validity of your research.
• Describe using a t-test to compare the means of two groups in a user study, as it is appropriate for assessing the statistical significance of differences.
• Explain employing logistic regression to model the relationship between independent variables and a binary outcome in a predictive analytics study.
• Report the p-value as 0.032, indicating a statistically significant difference between the two groups at the 0.05 significance level.
• Interpret an effect size of 0.40 as a medium-sized effect, highlighting its practical importance in the context of the research.

By describing the statistical analyses conducted, explaining the rationale behind the chosen tests, and accurately presenting the statistical values and interpretations, you strengthen the validity and reliability of your findings. Statistical analyses provide an objective framework for drawing conclusions from your data and lend credibility to your research in the computer science domain.

Reporting the findings of your research in an objective, concise, and clear manner is essential for effectively communicating your results. Here are some key considerations for reporting the findings:

• Summarize the key findings of a machine learning study by stating that “the proposed algorithm achieved an average accuracy of 85% on the test dataset, outperforming existing state-of-the-art methods by 10%.”
• For a research question about the impact of different programming languages on software performance, present specific metrics such as execution time or memory usage for each language, along with a comparison and interpretation of the results.
• Instead of using overly technical language, communicate the results in a more accessible way: “The experimental results showed a significant correlation between the number of training samples and the accuracy of the model, indicating that a larger training dataset leads to improved prediction performance.”

By guiding readers on summarizing the results objectively and concisely, addressing each research question or hypothesis, and using clear and concise language, you ensure that your findings are communicated effectively. This approach allows readers to understand the core contributions of your research and how they align with the research questions or hypotheses you set out to investigate.

Providing strong evidence from the data to support your findings, addressing unexpected or contradictory results, and discussing limitations and potential explanations are essential components of reporting research findings. Here are some key considerations for supporting the findings:

• Present empirical evidence from a user study, such as participant feedback or performance metrics, to support the usability and effectiveness of a proposed user interface design.
• If a software system performed unexpectedly poorly in certain scenarios, discuss potential factors such as data bias, implementation issues, or limitations of the evaluation methodology that could have influenced the results.
• Acknowledge limitations such as a small sample size, limited dataset availability, or computational constraints that might affect the generalizability or robustness of the results.
• Discuss potential explanations for unexpected results, such as issues with data quality, algorithmic complexity, or model assumptions.

By providing evidence from the data to support the findings, addressing unexpected or contradictory results, and discussing limitations and potential explanations, you demonstrate a rigorous and reflective approach to your research in the computer science domain. This allows readers to assess the strength and reliability of your findings and gain a deeper understanding of the nuances and implications of your work.

Using visual representations, such as tables, graphs, and figures, alongside the text can greatly enhance the understanding and impact of your findings. Here are some key considerations for visual representation:

Visual representations offer several benefits in presenting research findings. They provide a concise and intuitive way to convey complex information, trends, and patterns. Visuals can help readers grasp key insights at a glance, enhance the overall readability of the document, and make the findings more memorable. Visual representations also facilitate effective comparisons, highlight important relationships, and aid in storytelling. Example:

When creating visual representations, consider the following tips to ensure clarity and effectiveness: a. Choose the appropriate visual format: Select the most suitable format, such as tables, line graphs, scatter plots, or heatmaps, based on the nature of the data and the message you want to convey.

b. Simplify and declutter: Avoid overwhelming the visuals with excessive data points, labels, or unnecessary decorations. Keep the design clean and focused on conveying the essential information.

c. Label and title clearly: Provide descriptive and informative titles for tables, graphs, and figures. Label the axes, data points, or components clearly to facilitate understanding.

d. Use colors and visual cues purposefully: Utilize colors and visual cues to highlight important information or differentiate between categories. Ensure that the chosen colors are distinguishable and accessible. e. Provide legends and captions: Include legends to explain symbols, colors, or abbreviations used in the visuals. Provide informative captions or annotations to guide readers in interpreting the visuals accurately. Example:

By incorporating clear and effective visual representations alongside the text, you enhance the presentation and understanding of your research findings in the computer science domain. Well-designed tables, graphs, and figures can simplify complex information, facilitate comparisons, and enhance the visual appeal of your dissertation. Remember to choose appropriate formats, keep the visuals uncluttered, label clearly, and use colors and visual cues purposefully to maximize their impact.

Writing the results section of a dissertation or thesis is a critical task that requires careful attention to detail, organization, and effective communication. Throughout this blog post, we have explored key elements to consider when crafting this section.

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How To Present Interview Findings In A Dissertation

Table of Contents:

Dissertation Results & Findings Chapter (Qualitative) . Learn to write down the outcomes chapter (also known as findings chapter) for the qualitative dissertation or thesis. Step-by-step guide + examples.

The concluding summary is essential since it summarises your key findings and lays the reason for discussion chapter. Bear in mind that some readers may skip straight to this (in the introduction section), so make certain that it may be read and understood well in isolation.

• Section 1: Introduction
• Section 2: Body
• Section 3: Concluding summary

How do I write 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.

Writing up your results in a thesis or dissertation – Once you’ve finished collecting and analyzing your data, you can begin writing up the results. This is where you report the main findings of your.

For each theme, make general observations about what the data showed. For example, you might mention recurring points of agreement or disagreement, patterns and trends, and individual responses that were particularly significant to your research question. You can clarify and support these points with direct quotations, and report relevant demographic information about participants.

• Tables and figures
• What can proofreading do for your paper?
• Is this article helpful?

Discussing your findings

Your dissertation’s discussion should tell a story, say experts. What do your data say?

Steven David, PhD, who successfully defended his dissertation in clinical geropsychology at the University of Southern California last May, found this point to be particularly difficult. When he defended his master’s thesis, his committee told him his conclusions went too far out on a limb. He used more restraint with his dissertation and his committee thought he wasn’t positive enough.

You’ve conducted your research, analyzed your findings and written your results. You’re tired and the last thing you want to do is keep writing. Yet, arguably the most difficult part of writing your dissertation awaits: your discussion, the place where you sew up the various threads of your research into a cohesive narrative. This is not the time to hurry through just because the end is in sight, say experts and students alike. Rather, it’s the time to pull back and take a fresh look at your work.

Reporting and discussing your findings

A claim like this, which implies that the statement is true in every case, cannot be supported with evidence. Claims should therefore be specific and precise, and the level of certainty must match the level of evidence. There are many methods used in academic writing to qualify a claim:

• Finding your focus
• What have you learnt from your data?
• Three kinds of story: macrostructures for a thesis
• The big picture
• What to include
• Reporting conventions
• Quantitative data
• Dealing with “Problems”
• Past or present tense?
• Qualitative data

Using cautious language

This page deals with the central part of the thesis, where you present the data that forms the basis of your investigation, shaped by the way you have thought about it. In other words, you tell your readers the story that has emerged from your findings. The form of your chapters should be consistent with this story and its components. Contents: Find the story in your dataPresent your findingsDiscuss your findingsUsing cautious languageFind the story in your dataFor many kinds of research, the main work of interpretation cannot be done until most of the data has been collected and analysed. For others, the data already exists (in the form of archival documents or literary texts, for example), and the work of interpreting it begins much earlier in the research process. Whatever kind of research you are doing, there comes a moment when your head is full of ideas that have emerged from your analysis. Ideally, you will have written them down as they came to you. Now you have to convert that mass of material and ideas into a written text that will make sense to a reader, and do justice to your findings.

Writing Chapter 4 – In reporting qualitative data in Chapter 4, you discuss the themes or categories that emerged from the word, phrases and sentences you collected from interviews, observations or document analysis. …

Chenail (1995) akins writing-up qualitative data to “the Tarzan Process, because I think of the quotes as vines in the jungle. As I maneuver myself from one quote to the next, I imagine myself as Tarzan swinging from one vine to another. It’s a great way to travel and a fun way to conceptualize the data re-presentation process”.

5. How to report qualitative research findings?

Interviews can be presented in a number of ways, there is no specific format to follow. However, alike other research methods, justification and methodology of the study should be provided. The research process should be fully transparent so that any researcher can reproduce it. In addition, it should be comprehensible to the reader.

KCE practice is to translate quotes only for publications in international scientific journals, but not for KCE reports. Although KCE reports are written in English, inserted quotes are in Dutch or French to stay close to the original meaning. The authors should pay attention to the readability of the text and make sure that the text without quotes is comprehensive to English speaking readers.

Ideally, quotes are anonymous and are accompanied by a pseudonym or description of the respondents. For example, in a research about normal birth, this could be: (Midwife, 36 years). There are however exceptions the rule of anonymity, e.g. stakeholder interviews, in which the identity of the respondent is important for the interpretation of the findings. In that case the respondent should self-evidently be informed and his agreement is needed in order to proceed.

(Guide) How to Present Qualitative Research Findings in PowerPoint? – As a researcher, it is quite pointless to do the research if we are unable to share the findings with our audience appropriately! Using PowerPoint is one of the best ways to present research outcomes. But, how does one present qualitative research findings using PowerPoint? In order to present the qualitative research findings using PowerPoint, …

NOTE – It is important to note that although the following structure is highly effective for most research findings presentation, it has been generalized in order to serve a wide range of research projects. You may want to take a look at points that are very specific to the nature of your research project and include them at your discretion.

• Objective of the Research
• Key Parameters Considered for Measurement
• Research Methodology Adopted
• Research Outcomes (Presenting Descriptive Analysis)
• Summary and Learnings from the Research
• Inclusions and Exclusions (if any)
• Conclusion of the Research
• Tip 1 – Use Visuals
• Tip 2 – Tell a Story (Don’t Show Just Data!)
• Tip 3 – Include Quotes of the Participants
• Use Quotes in their Unedited Form

LibGuides: Dissertations and research projects: Qualitative research

Online study guides for every stage of your research project, from planning to writing up. Also includes advice on writing a remote dissertation while social distancing measures are in place.

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:

• 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?
• How do I choose the ‘right’ approach?
• Introduction
• Literature review
• Methodology
• 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.
• Alternative structure
• Presenting qualitative data

The Role of the Qualitative Researcher

In the following, we’ll explore how the researcher conducting qualitative research becomes responsible for maintaining the rigor and credibility of various aspects of the research. In a way, this is analogous to the role statistics, validated and reliable instruments, and standardized measures and methods play in quantitative research.

The first step: is to self-assess your competence. Assume you do not have competence in each of the skill areas unless you have demonstrated it to someone who knows. If you perform interviews of clients, for example, but have never been taught to do interviews for research, assume you do not have the competence until a researcher who uses interviews tells you that you do.

• Integrity of the Research is the Issue
• Role of Researcher: Monitoring and Reducing Bias
• Role of Researcher: Developing Competence in Methods
• Role of Researcher: Presenting the Findings

The researcher’s ideas—about the study, her knowledge, about the topic from the literature review, hopes for the study, and simply human distractibility—crop up constantly and can distort what she hears. Confirmation bias—(the name for this) afflicts quantitative researchers, too, but more often when they are analyzing data and seeing what they are disposed to see. Qualitative researchers, whose human brains are trained to find meaning in everything, encounter confirmation bias in every interaction with both participants and data.

How to Write the Findings of a Dissertation

Not sure how to write the findings of a dissertation. Here are some comprehensive guidelines for you to learn to write a flawless findings chapter.

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis or research questions as set out in the introduction chapter of the dissertation.

• Use of Figures and Tables in Dissertation Findings
• How to Report Quantitative Findings
• Paired Samples
• Paired Samples Correlation
• Paired Samples Test
• How to Report Qualitative Findings
• Table of content
• Looking for quantitative dissertation help?
• Research Prospect to the rescue then!

What to Avoid in Dissertation Findings Chapter

There were 217 participants who completed both the pretest and posttest, and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals there is a statistically significant difference between the mean scores of the pretest and posttest scales scores from the Teachers Discovering Computers course. The pretest mean was 29. 00 with a standard deviation of 7. 65, while the posttest means was 26. 50 with a standard deviation of 9. 74 (Table 1). These results yield a significance level of . 000, thereby indicating a strong treatment effect (see Table 3). With the correlation between the two scores being . 448, little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

How do you present findings in a dissertation?

Introducing your findings

• Reminding the reader of what you set out to do.
• A brief description of how you intend approaching the write up of the results.
• Placing the research in context.
• Letting the reader know where they can find the research instruments (i.e. the Appendix)

How do you present qualitative findings?

The first is to simply report key findings under each main theme or category, using appropriate verbatim quotes to illustrate those findings. This is then accompanied by a linking, separate discussion chapter in which the findings are discussed in relation to existing research (as in quantitative studies).

How do you analyze interview data for a dissertation?

Steps in a Thematic Analysis

• Familiarize yourself with your data.
• Assign preliminary codes to your data in order to describe the content.
• Search for patterns or themes in your codes across the different interviews.
• Review themes.
• Define and name themes.
• Produce your report.

Jun 5, 2020.

How do you reference a dissertation interview?

If you would like to include a personal interview as part of your APA reference list, then include the interviewee , the date of the interview, and the type of interview. Interview Citation Structure: Last name, F. (Year, Month date).

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Live video of man who set himself on fire outside court proves challenging for news organizations

In this image taken from video, bystanders react after witnessing a man who lit himself on fire was extinguished, Friday, April 19, 2024, in a park outside Manhattan criminal court in New York. Emergency crews rushed away a person on a stretcher after the fire was extinguished outside the courthouse where jury selection was taking place in former President Donald Trump’s hush money criminal case. (AP Photo)

New York Police officers inspect a backpack left at the scene where a man lit himself on fire in a park outside Manhattan criminal court, Friday, April 19, 2024, in New York. Emergency crews rushed away a person on a stretcher after fire was extinguished outside the Manhattan courthouse where jury selection was taking place in former President Donald Trump’s hush money criminal case. (AP Photo/Mary Altaffer)

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NEW YORK (AP) — Video cameras stationed outside the Manhattan courthouse where former President Donald Trump is on trial caught the gruesome scene Friday of a man who lit himself on fire and the aftermath as authorities tried to rescue him.

CNN, Fox News Channel and MSNBC were all on the air with reporters talking about the seating of a jury when the incident happened and other news agencies, including The Associated Press, were livestreaming from outside the courthouse. The man, who distributed pamphlets before dousing himself in an accelerant and setting himself on fire, was taken to a hospital where he later died.

The incident tested how quickly the networks could react, and how they decided what would be too disturbing for their viewers to see.

With narration from Laura Coates, CNN had the most extensive view of the scene. Coates, who at first incorrectly said it was a shooting situation, then narrated as the man was visible onscreen, enveloped in flames.

“You can smell burning flesh,” Coates, an anchor and CNN’s chief legal analyst, said as she stood at the scene with reporter Evan Perez.

The camera switched back and forth between Coates and what was happening in the park. Five minutes after the incident started, CNN posted the onscreen message “Warning: Graphic Content.”

Coates later said she couldn’t “overstate the emotional response of watching a human being engulfed in flames and to watch his body be lifted into a gurney.” She described it as an “emotional and unbelievably disturbing moment here.”

Fox’s cameras caught the scene briefly as reporter Eric Shawn talked, then the network switched to a courtroom sketch of Trump on trial.

“We deeply apologize for what has happened,” Shawn said.

On MSNBC, reporter Yasmin Vossoughian narrated the scene. The network showed smoke in the park, but no picture where the body was visible.

“I could see the outline of his body inside the flames,” Vossoughian said, “which was so terrifying to see. As he went to the ground his knees hit the ground first.”

The AP had a camera with an unnarrated live shot stationed outside the courthouse, shown on YouTube and APNews.com. The cameras caught an extensive view, with the man lighting himself afire and later writhing on the ground before a police officer tried to douse the flames with a jacket.

The AP later removed its live feed from its YouTube channel and replaced it with a new one because of the graphic nature of the content.

The news agency distributed carefully edited clips to its video clients — not showing the moment the man lit himself on fire, for example, said executive producer Tom Williams.

Julien Gorbach, a University of Hawaii at Manoa associate professor of journalism, said news organizations didn’t face much of a dilemma about whether to show the footage because there was little for the public to gain by seeing images of a man lighting himself on fire.

The episode highlights how fast information travels and the importance of critical thinking, Gorbach said.

“It outpaces our ability to a) sort out the facts, and b) do the kind of methodical, critical thinking that we need to do so that we understand the truth of what actually this incident was all about,” Gorbach said.

The location of the incident may have prompted some to think the self-immolation was related to the trial.

Gorbach, who was listening to MSNBC on satellite radio when it happened, said the coverage he heard was careful to question whether there was any connection to the trial. It also raised the possibility the man may have wanted to get media attention.

News organizations can’t suppress the news just so the public doesn’t get confused, he said. Word would get out regardless as non-journalists post accounts online.

“So it’s really a test of us as a public,” he said.

Associated Press writer Audrey McAvoy in Honolulu contributed to this report.