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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• v.68(3); May-Jun 2015

Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

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Speaker 1: Hey guys, welcome to Grad Coach TV, where we demystify and simplify the oftentimes confusing world of academic research. My name's David, and today I'm chatting to one of our trusted coaches, Alexandra, about five common mistakes students make during their qualitative research analysis. This discussion is based on one of the many, many articles over at the Grad Coach blog. So, if you'd like to learn more about qualitative research analysis, head over to gradcoach.com forward slash blog. Also, if you're looking for a helping hand with your dissertation, thesis or research project, be sure to check out our one-on-one private coaching service, where we hold your hand throughout the research journey, step-by-step. For more information and to book a free consultation, head over to gradcoach.com. Hey, Alexandra, welcome back to the CoachCast. It's really great to have you back on board.

Speaker 2: Hey, David. always a pleasure to be here and happy to talk with you today. So today we are talking about

Speaker 1: five common mistakes students make about qualitative research analysis, and let us just dive into it. The first one that comes up quite frequently is a lack of alignment between the analysis and the golden thread. Alexandra, what am I getting at with this? Yes, so this idea

Speaker 2: of the golden thread, you will hear it in all walks of research, whether it is quantitative, mixed methods and qualitative so really what you want to do and consider for this golden thread are these three fundamental we'll call them puzzle pieces of the research aims the research objectives and the research questions so these are kind of the foundation of your qualitative research study and so how you consider these and you know what you're trying to do and answer and how you're going to do it will then help you determine what methodology you should choose that would be the most appropriate or suitable to answer those questions and this is not particularly easy because there are several different kinds of qualitative methodologies out there but it can have some some positive outcomes or some negative consequences depending on which methodology you choose to answer those aims objectives and questions of your golden thread so that's

Speaker 1: really helpful alexandra maybe you can give us an example or two of where there's alignment or a

Speaker 2: lack of alignment sure so two of the most common methodologies in qualitative research that we see at grad coach or elsewhere are case studies versus grounded theory and so the first thing to keep in mind with any study is that your the methodology that you choose should be the most suitable one to answer those golden thread notions of the aims objectives research questions not the other way around and so for example with the case study the case study should be used if in your golden thread ideas of the aims objectives and research questions you already have some sort of working knowledge of a group or an event and so you're using this case study methodology because it will appropriately answer those foundational aspects of the golden thread on the other hand let's say your research aims or objectives or questions are about something that you really have limited knowledge about or there's scarce research out there and you're wanting to kind of build up a framework or a theory in that case using a methodology like grounded theory would be more suitable. So you can see there with those two examples of case study versus grounded theory, these two methodology should be applied to answer different golden thread foundational aspects.

Speaker 1: That is really helpful, Alexandra. And I know it can seem a little bit overwhelming to think about getting this alignment right. In cases like this, do not necessarily just rely on your own judgment. It can be really helpful to get a friend or someone from your cohort just to take a look through and read of what you are working on. They will be able to help you identify where there is a lack of alignment. For instance, if you ask them to sort of give you the elevator pitch back of what you set out to do, and it is not lining up with your thinking, then maybe it is a good point to sort of identify where those lack of alignments are, and use that to help you sort of address that. But try and do this earlier rather than later. It's definitely going to make your life easier. So our second mistake is making use of a transcription program software without checking the transcripts. Alexandra, why is this such a problem? Yes. So first of all, you know, there

Speaker 2: are programs, an increasing number out there that are cost effective, mostly free, and for the most part accurate things like zoom transcription software otter ai atlas ti and these certainly have a lot of benefits for convenience sake and cost effectiveness however um that's not to say that these programs are perfect because with a lot of ai and other kinds of automated software it does lose that human element that can miss some of the more nuanced or minute pieces of information that are important. So for instance, in my own dissertation research, I had about 100 participants who all verbally reacted to a stimulus. And half of my participants were doing this in English and the other half in French. And each of these were about 30 minutes long, each participant 30 minutes now with qualitative research you know you have to have something to analyze and it's difficult to do that directly from the audio files so what you have to do is transcribe these from audio to text and so i was going through and i was doing these manually myself from about participant 80 i was beyond exhausted and so i decided to use one of these outside services or programs to kind of expedite this, kind of help me. And of course it was convenient. However, when I got the transcripts back, I noticed as I was going through the first few of them, some errors to content, to spelling, different words were showing up where other words had been said actually in the audio files. And as I was going along through the rest of them, I noticed that pretty much all 20 or so of these outside transcribed files had errors. So I ended up having to go back myself regardless and going through them again and fixing them. So this is all to kind of say that even though these programs can be very convenient and cost effective, there are some drawbacks. most of that has to do with kind of content, the words that they miss, spelling, punctuation, grammar, et cetera, et cetera. And you'll oftentimes definitely actually still have to go in and check these for quality and accuracy. This is why it's very important to kind of think about, even though these programs might be convenient, they're never going to replace kind of that human element of being able to really read and understand what's going on, make sure that it matches what was said in the audio files. And so one of the things that you can do if it's not yourself, you should check it yourself, but even go beyond that and ask someone else to check these transcripts for accuracy. Because either if you've used an outside service or program, or if you've done all the transcriptions by yourself, sometimes we miss things. Having someone else, an outside person, an actual person look at these and kind of make sure that they're accurate will not only help you catch potential errors, but in doing so, it kind of promotes the credibility of the transcripts because they're accurate, they're clear, they're actually what was said in the audio files and so sometimes what might be happen if you don't do this having that like human element it can diminish the credibility of the rest of your transcripts if they are accurate because the reader or your marker might say well this one was not accurate so maybe there's some flaws in the other ones as well but beyond that I mean other than the marking your transcriptions this is your this is really your raw data in qualitative analysis and so if you have errors or missing information in your transcripts that were there in the audio files this makes the coding and analysis flawed this puts things in misalignment and as such there's kind of a domino effect of repercussions that can happen if these things aren't transcribed

Speaker 1: accurately. I think that in the same way that in quantitative research your actual data is key to your analysis, it is the same for qualitative. So we really want to make sure we are doing due diligence to assess the quality of the work. That is not to say you cannot use services to help out. It will depend on your type of research as well. For instance, from a business perspective, you might be less interested in the specific nuance of how someone presented an idea compared to a language study. So in cases like that, there is a bit of a cost benefit to consider, but regardless of whether you are using a service or not, getting a second run through of it can be super helpful. And there are a range of services out there that you can use, both in terms of software or human run services. If you are interested in it, we even do it here at Grad Coach. So do take a look for the link down below. So our third mistake that frequently comes up is not specifying what type of coding you are doing in advance of actually jumping into the analysis. Alexandra, why do we need to be aware of what coding type we are using so early in the process?

Speaker 2: This goes back to the idea of making sure that all steps of your research align with the previous one and are justifiable in terms of it makes sense. There's a reason why you're doing what you're doing in the order that you're doing it. And coding is no exception to this. So the reason why coding is so important in qualitative research is that qualitative research is inherently kind of subjective. There is this inherent human interpretation that can happen. And so one of the reasons why it is so important to do coding appropriately is to kind of add the systematicity and the academic rigor to your research that is inherently not there. And so to kind of ensure this increased objectivity of something that is inherently subjective, doing this coding, you need to consider which kind of coding will be the most appropriate to answer your research goals that you've outlined prior, going back to that notion of the golden thread. And coding inherently kind of falls into two camps. There is inductive coding and deductive coding. So on the one hand, inductive coding is an approach where you are going into your data analysis and you are kind of, you're letting the themes and the codes emerge from the data. You don't have any preconceived notions, no existing ideas of what to expect. You're really letting the data, whether it comes from interviews or focus groups, you're letting the data from those transcripts emerge into these codes. And this is best for studies such as grounded theory approaches where you don't really have any idea of what to expect or anticipate. And you're really kind of trying to explore what is out there. You're letting these codes emerge directly from the data. On the other hand, deductive coding is another coding approach where you are actually, you have some ideas about what is out there, what you're looking for, what you hope your final findings to be. And for this coding approach, it's top down where prior to even collect the data, the interviews, focus groups what have you you have developed an initial set of codes into a code book whether you've put this in say Microsoft Excel or Microsoft Word or Google Sheets etc and you have kind of looked through the existing literature on your research topic and seen what what are the potential codes out there what are the themes you're looking for And then once you have collected your data and transcribed it, you're assigning pieces of that data to those codes that you've already created in advance. And you are not looking for new codes to emerge like you did in inductive. So all codes should go into something from your codebook.

Speaker 1: I think deductive coding is most commonly used where you have a theoretical framework that you're working within or a field that is really, really well researched. There, you're not going to be starting something new. Similarly, it's also become really popular to use a mixed approach of inductive and deductive. This is primarily starting deductively with a codebook and using that codebook to lead your coding and then develop further from that with an inductive approach. It is worth noting this is a fairly new way to go about coding, and so it is important that if you are choosing to go this way, that you can justify why it is appropriate and why it is useful relative to that golden thread, those research aims, objectives, and questions. Because you You don't want to be overcomplicating things or stepping too far out of your comfort zone just because it's novel. Rather, make sure it is what you need to do, where you need to do it.

Speaker 2: That's great advice, because sometimes as graduate students, we have this urge to do something novel or do it a different way. And that should not be your motivation or your justification to do something. So even though this this kind of new way is developing and coming and becoming increasingly popular, that doesn't mean that it's right for your study. So how you know it's right for your study is going back to that notion of the golden thread. And this idea extends even beyond inductive and deductive coding, because those are kind of your your starting idea of how you're going to code. Beyond that, there are additional specific approaches that you will use for your initial or your first set of coding versus your second set of coding. As an aside here, you should absolutely do more than one round of coding. Again, this will increase the systematicity, the rigor, and kind of the credibility, so to speak, of your data analysis. and so there are many different specific coding approaches but some of the the most common ones we'll name here are starting with your open coding and so for this one this kind of approach it's very loose it's very tentative as indicated from its name it's open and so this is more suitable when you're starting out other common approaches are things like in vivo coding and so with in vivo coding, this is actually using the participants own words in your analysis, not putting your interpretation of what they said or suggesting what they meant, but actually letting the participants own words do the talking, so to speak. And so this is typically most suitable to things where you're really interested in the perspectives or points of view or experiences of your participants and then the last one we'll mention but there are still plenty more is structural coding and so we use structural coding specifically well not specifically but commonly in cases where you say have conducted an interview or focus group discussion and you want to use those questions that you posed in the interview or the focus group kind of as headings all of the codes that go under one specific column for instance should be related to one specific question that was asked in the data collection and so this is really best if you are kind of looking for specific answers or codes or themes in response to one of your interview questions so or focus group questions so again there are still plenty more out there but these are some of the more common coding approaches.

Speaker 1: That's really helpful, Alexandra. And it can feel a little overwhelming that there are so many options to choose from. Don't worry, there are a ton of resources out there. Definitely take a look at any of your methodological textbooks from a qualitative perspective. You can take a look at methodology papers that have been published, YouTube tutorials, blog posts, you name it, it's out there. We even have some videos and some content about coding as well on the Grad Coach blog. Links to that will be down in the description below. But importantly, when you are considering these coding decisions, it is important to realize again what you are using them for. So look for that alignment, make sure it is on track, and then it will flow much smoother going forward as well. So our fourth common mistake is students downplay the importance of organization during both coding and analysis. How important is organization, Alexandra? It is so important. The reason why

Speaker 2: this is so important is that oftentimes we kind of assume that qualitative research and qualitative data cannot be structured. Of course, it's not as black and white or objective as quantitative research. And so what you need to do as a qualitative research is to kind of apply a framework that yourself that will promote this kind of objectivity, systematicity. And part of this relies on organization. And organization is important not only for the coding, but also the analysis. So part of the difficulty, but the importance of organizing is that sometimes the codes that you end up with after you've transcribed and done your, let's say, initial round of coding, you can end up with very high numbers of codes. For instance, I've seen some where it's upwards of 1000 codes. And so this number is very overwhelming, very large. and some of the ways to tackle this large amount of codes is one to make sure that you're organizing all of your codes in a spreadsheet of sorts whether it's excel or google sheets having them all in one place will then further facilitate you doing additional rounds of coding which we recommended previously and in doing so having these additional rounds of coding on your codes that are organized in one place, it will help you kind of whittle down these codes to the point where you have the codes that you need. There's none that are kind of superfluous or repeated, but it's very important to keep these organized in one place and to go through multiple rounds of coding. And this will make your life a whole lot easier and make sure that you have only the

Speaker 1: codes that you need and can justify. I think that's super helpful. It's also worth emphasizing that coding and organization it's a back and forth you're going to be moving from one to the next and back again and that's a good thing to do it enriches your analysis but it also allows your organization to inform your coding and your coding to inform your organizational structure and through that iterative process you're really going to develop the analysis so don't think I've coded it once, I'm done and dusted. Sorry to say it's a multiple approach. In terms of organization helping analysis, Alexandra, why is it also important to keep a track in that Google document

Speaker 2: or sheet of all your codes? Yeah, so this goes back to that notion we've repeated several times of the golden thread. So if you think of dominoes, for instance, you need to have your dominoes set up in such a way that if you knock one down, the rest go down. We can think of that, our qualitative research in such a way. And so if in the coding stage, everything has aligned with that golden thread and we move on to the analysis, the analysis will be further aligned with the coding, the transcription, the data collection, going back to the research questions, aims and objectives. And so having our codes organized in a sheet will then allow us to start to analyze our codes in a way that we can see themes and patterns emerging that are aligned with the codes, which will then add this rigor and systematicity of your study by having analysis that you know is based on very organized, solid foundations of your coding and your transcription. And so through this analysis, if we have our analysis organized, we can keep track of our patterns, our themes, and then going beyond that, actually, when we get to the point where we're writing our findings chapter, we have this set organization that will then kind of allow us to know how we're going to present these results because everything has been organized and justified up to that point.

Speaker 1: I think that's really helpful. It's also worth noting that having your codebook organized can be really helpful in sort of preventing you from getting stuck with your analysis or feeling like you're unsure of how to code because, you know, things are feeling uncertain. If you have an Excel sheet that you've developed before you start your coding process, you have it organized by the different rounds and you start bringing it from a large number of codes to the specific codes you are going to be using, that organization really helps make that process move forward. And it can be kind of cathartic to really work through that process, get it from a hundred transcripts of 30 minutes each down to some key findings. So our fifth and final mistake that we're covering today is not considering your researcher influence on your analysis. Alexandra, how do we affect our analysis and why is this something that we need to even think about?

Speaker 2: Yeah, so this kind of just goes back to the innate nature of qualitative research. It relies a lot on interpretation. It is subjective. It's not inherently black and white, such as quantitative research. And so the ways that this is kind of mitigated is through things like positionality and reflexivity. So these two concepts are becoming much more prominent and required in qualitative dissertations and theses. And so what these essentially mean is that you have your positionality, which are the underlying kind of beliefs, judgments, opinions, perceptions, all of those things that kind of make you you, the human elements. And so the way that you think about things might be different than the way someone else thinks about them. And so why we need to state our positionality in qualitative research is that it can impact our interpretation of the data, which then impacts the findings. And so, for example, in an example study where someone is exploring the perceptions of the tech industry of men versus women, a researcher who kind of identifies as a feminist versus one who identifies as more conservative or traditional, they might have underlying beliefs or assumptions about gender when it comes to the workplace or just in general. and so acknowledging that that you have these kind of underlying preferences or perspectives what have you it's important to acknowledge that because like i said it can have consequences for your analysis and your findings taking this a step further typically now we also have to to talk about our reflexivity in qualitative research and so essentially what this refers to is how our positionality affects our kind of interpretation so whereas positionality has to do more with the underlying assumptions reflexivity is taking those underlying assumptions and acknowledging how they might actually impact our interpretation and our findings and so the reason oftentimes why these are required now in qualitative studies is that this idea of you know validity and reliability we don't really use those in qualitative research we use more of these ideas of trustworthiness and that connects to our positionality and our reflexivity this reflexivity how it can impact you know it can impact the coding of your data the themes that you pull from the coding how you interpret it how you present it so in my example of the researcher who has more feminist underlying beliefs versus more traditional conservatives even if they're exploring the same phenomenon they can have vastly different interpretations and so acknowledging your positionality and indicating with your reflexivity how it might impact those steps of the research analysis can lend more credibility and more kind of trustworthiness to your your

Speaker 1: findings and ultimately your study. So that's really helpful to think about these aspects because we do need to consider how our positionality and our reflexivity might affect how we proceed with our analysis. There are potential opportunities for bias and if we're engaging in these behaviors we are able to a mitigate them during the analysis and in cases where you cannot mitigate it you can at least acknowledge it so other researchers can interpret that going forward but bias goes a little bit beyond just your positionality and reflexivity so Alexandra what other biases can come up because of research effect yes this idea of bias so going

Speaker 2: further beyond positionality and reflexivity it can be very easy to have biased interpretations and there are a few ways this can manifest so for instance spending too much time presenting the the findings from one particular participant in your study and neglecting those of the others and so one reason why this might happen is either you as the researcher totally agree personally with their perspective or even totally disagree and you want to to present that in um in some for some sort of reason um so it's very important to kind of mitigate that bias by presenting a balanced approach of all participants on the other hand there's also things like spending a lot of time presenting on one particular theme that emerged from your qualitative analysis and you know, kind of avoiding or neglecting the other ones. So this can happen where you found a theme that emerged from your analysis that was particularly interesting to you, whether it was novel, whether it confirmed what you thought, or even aligned with your personal beliefs. It's very important to make sure that you are giving enough attention to all the different themes that have emerged. And a third common bias that we see is that sometimes it can be easy to make claims or assumptions such as this means that or people should do this. So for instance, in my example of the tech industry and gender norms, making claims in your writing such as women in the tech industry felt that, or the way that the women in the tech industry talked means that, or the tech industry should do that. So making those kinds of grand sweeping claims that your qualitative findings mean some sort of big, big thing. We really have to try to avoid that in qualitative writing, despite it being tempting, especially if it aligns with our personal perspective. So, those are some common biases we see.

Speaker 1: I think that is super helpful to think through, particularly because biases are inherent to us. So, it is important to take that step back, to think about how you might interpret, interact with things, and then engage with that. One way to really go back to this is take a look at the data. We do not want to be making statements or assumptions that do not have support in the data. that is just gonna undermine your argument and your position as the researcher. So wherever possible, if you don't have data to support it, maybe consider not including it. If you do have data to support it, maybe just confirm with a second opinion, your supervisor or someone else, just to make sure that there's not bias coming in. But I think the most important part here is to think about the fact that we do have biases. And so as long as we're considering this, we're doing our due diligence as researchers.

Speaker 2: Yeah, and so one of the ways that you can also make sure that you are kind of following what you said you were going to do from the get-go is not to step out of your codes and your themes that you've established. The reason why this might be tempting to do, again, is going back to that fact that maybe you found something super interesting to you and you want to present it. What I would caution you towards is making sure that any findings that you're presenting fit or align with what your objectives, aims, and research questions were. Another reason why this might happen is because the dissertation or the thesis is such a long process, sometimes we can kind of get away from our original intent of our study. And so presenting these things that are outside of our codes or our themes, we think we can get away with but in reality this kind of minimizes the the rigor of of your findings and so even though you might find something very interesting like you said David be really careful make sure that you're still kind of staying within your codes within your themes and following that golden thread that you've been establishing throughout yeah you've

Speaker 1: probably heard it so much today but golden thread is key we want to make sure that we're maintaining alignment with our research. It is only going to improve the impact. So Alexandra, thank you so much for joining us today. It has been really great. There are some great insights here and thank you again for joining us on the CoachCasts. Always a pleasure, David. Thanks so much for

Speaker 2: having me and letting me kind of chat about these qualitative foibles.

Speaker 1: Alright, so that pretty much wraps up this episode of Grad Coach TV. Remember, if you are looking for more information about qualitative research analysis, be sure to check out our blog at gradcoach.com forward slash blog. There you can also get access to our free dissertation and thesis writing mini course that'll give you all the information you need to get started with your research journey. Also, if you're looking for a helping hand with your dissertation, thesis or research project, be sure to check out our one-on-one private coaching service where you can work with one of our friendly coaches, just like Alexandra. For more information and to book a free consultation, head over to gradcoach.com.

Diverse Qualitative Research Perspectives Festival set to inspire and inform

The North-West University's (NWU’s) Optentia research unit is gearing up to host the upcoming Diverse Qualitative Research Perspectives Festival from 3 to 6 September at The Roots in Potchefstroom.

According to Optentia's project manager Anjonet Jordaan, this festival aims to provide a dynamic and inclusive platform that encourages the exchange of innovative methodologies and transformative practices in qualitative research.

Anjonet says the festival promises to be an invaluable opportunity for individuals engaged in qualitative research to engage with research experts, gain new perspectives, and expand their networks.

"Master’s and doctoral students, researchers, practitioners, community partners and industry members with an interest in qualitative research are invited to join this enriching event. It offers a great opportunity to connect with like-minded individuals, share knowledge, and engage in meaningful discussions about qualitative research. Join us for a week filled with valuable insights and networking opportunities," she adds.

Renowned speakers and international and South African experts from various domains will grace the festival. They include, among others, Prof Vera Roos, Prof Ansie Elizabeth Kitching, Constant van Graan, Dr Elette Horn, Prof Linda Liebenberg, Prof Blessed Ngwenya, and Prof Chris Wekerle. Their diverse expertise promises to offer valuable insights into the field of qualitative research.

The registration fee is R6 000 per person for registrations until 30 August. Additionally, participants interested in attending only one or two days of the three-day event can register for R1 500 per day, while those registering solely for the last day (6 September) can do so for R750. It is worth noting that this event awards two ethics Continuing Professional Development (CPD) points and 19 general CPD points.

Full information on the speakers, topics and how to register can be found here .