provide a brief description of the generic qualitative approach and how it is different from other qualitative research designs or approaches.
Referencing Percy, Kostere, and Kostere (2015), the article attached, provide a brief description of the generic qualitative approach and how it is different from other qualitative research designs or approaches. Then consider the examples they offer on page 76 of the article and a potential study of counselors attitudes about working with psychiatrists. In 750 words, explain why a generic approach would be a consideration for this study.
Generic Qualitative Research
The Qualitative Report 2015 Volume 20, Number 2, Article 5, 76-85 http://www.nova.edu/ssss/QR/QR20/2/percy5.pdf
Generic Qualitative Research in Psychology
William H. Percy, Kim Kostere, and Sandra Kostere Capella University, Minneapolis, Minnesota, USA
Some topics for qualitative research in psychology are unsuitable for or cannot be adapted to the traditional qualitative designs such as case study, ethnography, grounded theory, or phenomenology. This paper explores reasons for this, and proposes that psychological researchers can use a generic qualitative design in such situations. After discussing the types of topics most suitable for a generic qualitative design, the paper differentiates generic qualitative designs from the more traditional qualitative designs, with particular attention to how generic qualitative inquiry differs from phenomenological psychological research. Finally, appropriate procedures for data collection and for thematic data analysis in a generic model are discussed and described in detail. Keywords: Qualitative Research, Thematic Analysis, Generic Qualitative Research, Basic Qualitative Research, Data Analysis for Qualitative Research.
Generic Qualitative Inquiry Differentiating Generic Qualitative Inquiry Many studies report people’s subjective opinions, attitudes, beliefs, or experiences of things in the outer world. Such psychological things cannot be measured in the statistical sense, and require qualitative methods (cf., Aronson, 1994). Sometimes, the other more focused approaches (e.g., ethnography, case study, grounded theory, or phenomenology) are not appropriate for one reason or another. In those cases, researchers should consider a more generic qualitative inquiry approach. Suppose a researcher were interested in investigating one of the following:
1. People’s attitudes, opinions, or beliefs about a particular issue or experience; 2. Workers’ feelings about their supervisors’ performance; 3. The reflections of women who left the convent on their “life journey”; 4. Senior managers’ reflections on experiences that have had significant
impacts on them during their careers; 5. Clients’ descriptions of their experiences of psychotherapy; 6. Children’s reports of their experiences being placed in special education
classes. Each of these topics calls for qualitative inquiry, but several other, more common approaches would not be suitable. Why not?
• Ethnography (see next section) focuses on the investigation of the network of social groupings, social customs, beliefs, behaviors, groupings, practices, etc., that define a “culture.” None of these topics focuses on that unit of analysis (social-cultural).
• Case studies are in-depth investigations of a “single case,” using multiple methods and multiple sources of data. A single case is defined by having
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clearly recognizable boundaries that differentiate the case from any other collection of instances. None of the groups of people above constitute a “case” in that sense.
• Grounded theory uses data from people to develop an explanation (theory) for the process in question developed over time. But none of the topics above, except perhaps the senior managers’ reflections, would lend themselves to development of theory. They are descriptive, not explanatory. If the researcher investigating the managers’ experiences in fact wanted to develop a theory of what experiences contribute to successful leadership style, that topic might qualify as grounded theory.
• Phenomenology investigates the “lived experience” of various psychological phenomena. Many of the phenomena this approach tackles include attitudes, beliefs, opinions, feelings, and the like. However, the phenomenologist’s interest is in the inner dimensions, textures, qualities, and structures (“essences”) of those cognitive processes, not in the external content or referents that may trigger the cognitive processes.
Differentiating Generic Qualitative Inquiry from Phenomenological Inquiry The most difficult distinction to make here is probably with phenomenology. Let’s take a moment for a more careful review of the differences. Phenomenology studies the inner essence of cognitive processing – what structures (temporality, spatiality, etc.) and textures (what are the felt qualities of the thoughts?) are found across the reports of many persons’ similar experiences? If a group of people describe how, in everyday life, they feel when they experience anger at work, the phenomenologist listens to what they all do similarly when processing their anger without thinking about it. Here is a common example, taken from an analysis in progress:
All the participants reported that anger feels big, expansive, magically powerful. They tend to feel themselves puff up, get hot, and start to believe they can change the problem by shouting or being harsh. They get very present- centered – nothing else matters when angry. Curiously, most also obsess about the past and past angers at the same time. “It’s like being in a time warp,” one said. Another said, “Tunnel vision. Only see one thing, but it feels timeless.”
The phenomenological interest is in the internal subjective structures of the experiencing itself. On the other hand, examples 1-6 described above focus on the actual content of their reports (what do they actually think about the issue? What are the experiences? Etc.). The attitude/opinion study would not be interested in the subjective psychological experiencing, but only in its content – what the experience was about. In any of the examples, if someone reported that anger was part of the experience, we’d be interested in the fact that someone was angry, not in what that experience of anger (“being angry”) was like. A second difference is that phenomenology investigates pre-reflective conscious experiencing, often referred to as “lived experience.” Contrast the term experiencing (phenomenology’s interest) with experiences (the focus of our topics above). Experiencing addresses the inward and ongoing act of taking in and making sense of a phenomenon – how does one do this? What is the structure of one’s cognitive processing? Experiences, on the other hand, focus our attention outwardly – What was experienced? What happened? To what does the belief point to in the outer world?
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Consider this example: Suppose we want to know something about a political campaign. The phenomenological question we’ll ask our voters might be something like this: What is it like for you experiencing this campaign? The opinion researcher, on the other hand, would more likely ask something like: Do you prefer X or Y in this campaign? Which of the following do you consider the most important issue? Please rank order your preferences in the race? What stands out as the most interesting thing about this campaign? The first question to ask yourself, after deciding on your general topic, is what you really want to know about your topic. If your focus is outward – on the content of opinions, on the actual-world experiences and happenings, on the thoughtful description and reflection of historical occurrences in people’s past—you might want to select generic qualitative inquiry as your methodological approach rather than phenomenology or one of the other approaches. To sum up, if the researcher is interested more in the actual outer-world content of their questions (the actual opinions themselves, the life experiences themselves, the participants’ reflections themselves) and less on the inner organization and structure of the participants’ experiencing processes, then phenomenology would not be appropriate, but a more generic qualitative analysis would be. Let’s turn our attention to what this generic qualitative approach is.
Description of Generic Qualitative Inquiry Generic qualitative inquiry investigates people’s reports of their subjective opinions, attitudes, beliefs, or reflections on their experiences, of things in the outer world. It can be selected as the methodological approach when:
1. The research problem and question require a qualitative or mixed-methods methodology. In fact, the generic qualitative approach is well suited to mixed methods studies, because its data usually can be re-structured as quantitative to relate to the statistical side of the study (Creswell, 1995; Tashakkori & Teddlie, 1998)
2. Ethnography, case study, grounded theory, or phenomenology is inappropriate because the focus of the study, the content of the information desired, or the kind of data to be obtained do not fit those approaches. These other approaches—particularly case study and grounded theory—can be used in mixed methods studies, but often their data cannot be as easily reformulated to integrate with the quantitative data.
3. The researcher has a body of pre-knowledge/pre-understandings (categories or sub-categories of information) about the topic that he or she wants to be able to more fully describe from the participants’ perspective. For instance, suppose that prior research has shown that employee morale correlates strongly and positively with their wage bracket, but nothing more is known than that. A researcher could ask what the employees actually think and feel about being in various wage brackets (sub-question one) and how those feelings and ideas influence their morale (sub-question two). Asking these two questions may expand the previous knowledge—that the two categories are related—with the qualitative employee-perspective information.
Generic qualitative inquiry is a useful approach when attempting survey research that includes qualitative elements in a mixed design. Indeed, this approach is appropriate when a fully qualitative survey approach is desired. Actually, researchers considering any study of
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people’s subjective “take” on actual external happenings and events should consider generic qualitative inquiry as their approach.
Generic Qualitative Data Collection Data collection in this approach typically uses data collection methods that elicit people’s reports on their ideas about things that are outside themselves. However, its focus on real events and issues means it seldom uses unstructured data collection methods (such as open- ended conversational interviewing from phenomenology, participant and/or non-participant field observation from ethnography, and the like). Instead, it requires semi- or fully-structured interviews, questionnaires, surveys, content- or activity-specific participant observation, and the like. The core focus is external and real-world, as opposed to internal and psychological. (Even the attitudes and opinions in opinion polling are valued for their reflection on the external issues.) By and large, generic qualitative data collection seeks information from representative samples of people about real-world events and processes, or about their experiences. We want less to “go deep” and more to get a broad range of opinions, ideas, or reflections. Occasionally, a small, non-representative, but highly informed sample can provide rich information about the topic. For instance, a few experienced nurses can often provide rich, accurate, and helpful information about common patient reactions to certain procedures, because part of a nurse’s role is to observe patients’ experience and reactions carefully. More often, however, the sampling in this approach aims for larger representation of the population in mind.
Although this is not hard-and-fast, generic qualitative data collection typically uses larger samples than other qualitative approaches use, because larger samples tend to be more widely representative. External generalization (reliability) is not necessary, because the data are sometimes not quantifiable. However, as with all qualitative inquiry, if the sample is transparently and fairly representative of the target population or is clearly information-rich about the topic, readers may be persuaded to apply the findings to similar people or situations outside the sample itself. Most generic qualitative studies rely on the following data collection methods:
• Semi- or fully-structured (closed-ended) interviews, either oral (the most common method) or written (uncommon). In these qualitative interviews, the questions are pre-structured based on the pre-knowledge of the researcher, although there may be opportunities for “tell me more” kinds of questions.
• Questionnaires. Usually these mix scaled or quantitative items (e.g., Likert- type scales asking preferences or degrees of agreement) with opportunities for qualitative comments; this approach requires mixed-methods designs. Again, the researcher will build these questionnaires and their items from pre-knowledge about the topic.
• Written or oral surveys. The standard opinion or voter poll is a good example, but survey research has its own rather deep literature and can be much more sophisticated that simple opinion or voter surveying. Once again, the items in the survey will be constructed on the basis of pre- knowledge about the topic.
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Data Analysis in Generic Qualitative Analysis: Thematic Analysis
…thematic analysis involves the searching across a data set – be that a number of interviews or focus groups, or a range of texts – to find repeated patterns of meanings. (Braun & Clark, 2006, p. 86)
Thematic analysis is process that is used to conduct an analysis of qualitative data. While it does not represent a complete research design, it does offer a process of data analysis that is flexible and compatible with many approaches to qualitative research and mixed methodology in particular generic qualitative analysis. Thematic analysis can be used to analyze data collected through a qualitative survey— a kind of qualitative survey using interviews (often) that are semi-structured—to investigate subjective experiences of objective things – like “your experience being a leader,” or “your experience of receiving treatment for X disorder,” or “your experience searching for a higher- paying faculty job.” A thematic analysis can also be used to conduct an analysis of the qualitative data in some types of case study and in mixed methodology studies. Really, thematic analysis is a generic approach to analyzing people’s reports that may form the basis for many different kinds of qualitative interpretation. Three types of thematic analysis will be described here: inductive analysis, theoretical analysis, and thematic analysis with constant comparison.
Since this is an inductive and intuitive process, there are no simple procedures or techniques for this kind of analysis. You many find it helpful to ask yourself questions like: “What do these quotes or observations have in common?” “What’s going on here?” “What does this tell me about how people view their world?” “How do these themes relate to each other?”. (Taylor & Bogdan, 1998, p. 156)
Inductive Analysis (IA)
Inductive analysis is data driven and does not attempt to fit the data into any preexisting categories. The researcher sets aside all pre-understandings. The data collected from each participant (interviews, observations, open-ended questionnaire, etc.) are analyzed individually. Once the data from all participants have been analyzed, the repeating patterns and themes from all participants are synthesized together into a composite synthesis, which attempts to interpret the meanings and/or implications regarding the question under investigation. Step-By-Step Analysis
1. Review and familiarize yourself with the data collected from each participant (interviews, journals, field notes, records and documents). Read the documents and highlight intuitively any sentences, phrases, or paragraphs that appear to be meaningful. During this process the researcher immerses him/herself in each participant’s data individually.
2. Review the highlighted data and use your research question to decide if the highlighted data are related to your question. Some information in the transcript may be interesting, but not relate to your question.
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3. Eliminate all highlighted data that are not related to your question. However, start a separate file to store unrelated data. You may want to come back and reevaluate these data in the future.
4. Take each piece of data and code it. The code can be very simple, like a serial number or an address – simply a way to keep track of individual items of data.
5. Cluster the items of data that are related or connected in some way and start to develop patterns. For each distinct pattern you discern, describe it in a phrase or statement that sums it up. If feasible or useful, assign a second level code to the patterns too. Note that the words describing the patterns are no longer the words of the participants, but your own. In field-specific research (e.g., psychology), attempt to make these words meaningful to specialists in the field (e.g., psychologists).
6. As you start to see patterns, identify items of data that correspond to that specific pattern. Place them in the previously assembled clusters (see 5) that manifest that pattern. Direct quotes taken from these data (transcribed interviews, field notes, documents, etc.) will elucidate the pattern. (The name or descriptor of your pattern thus is a more abstract phrase, whereas the data themselves are direct words from participants.)
7. Take all the patterns and look for the emergence of overarching themes. Themes are “patterns of patterns.” This process involves combining and clustering the related patterns into themes. As you see meaningful themes across patterns, assign a yet-more-abstract descriptor to the theme. Use standard psychological language and terms. This will be a third level of abstraction, supported by the patterns, in turn illustrated by the direct data.
8. After all the data have been analyzed, arrange the themes in a kind of matrix with their corresponding supportive patterns. (The patterns are used to elucidate the themes, just as the word data are used to support and illustrate the pattern descriptors). In the matrix, include the codes or descriptors for each of the data clusters. Thus, the supporting layers of words/text can easily be accessed when discussing an individual theme in your final report.
9. For each theme, write a detailed abstract analysis describing the scope and substance of each theme.
10. (Complete this process for each participants’ data) 11. Then combine the analysis of data for all participants including patterns and
themes that are consistent across the participants’ data. 12. Finally, the themes are synthesized together to form composite synthesis of
the data collected regarding the question under inquiry.
Theoretical Analysis (ThA) Theoretical analysis is employed in a situation in which the research has some predetermined categories (themes) to examine during the data analysis. In this situation, the research may use his/her pre-understandings when conducting the data analysis. However, in this case the researcher also remains open to the possibilities of new themes emerging from the thematic analysis. The theoretical thematic analysis is driven by theory and the themes that are predetermined are usually located in the research question. Thus, the research question will have identified concepts from theories on the topic under inquiry. The data collected are analyzed individually and patterns that emerged from the data will be organized under the
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appropriate preexisting themes, keeping in mind that new patterns and themes may also emerge from the data during the data analysis process. Researchers might approach this analysis in two phases: In the first phase, after preparing the data (steps 1-4 below), one works on assigning the data units to the pre- determined themes derived from previous research and theory and carries out the analyses as described in steps 5 through 13. Then, in phase two, return to the data and work with data units and patterns that did not seem to fit the pre-determined categories, again following steps 5-13. The themes derived from this analysis will likely not be found in previous research but may contribute to it. Step-By-Step Analysis
1. Read, review, and familiarize yourself with the data collected from each participant (interviews, journals, field notes, records and documents). Re-read the documents and highlight intuitively any sentences, phrases, or paragraphs that appear to be meaningful. Keeping in mind the predetermined categories (themes) that are related to the theory and research question posed as well as remaining open to any new patterns and themes that are related to the research question and have emerged from the data analysis. During this process, the researcher immerses him/herself in each participant’s data individually.
2. For each participant review the highlighted data and use your research question to decide if the highlighted data are related to your question. Some information in the transcript may be interesting, but not relate to your question.
3. Eliminate all highlighted data that are not related to your question, however, start a separate file to store unrelated data. You may want to come back and reevaluate these data in the future.
4. Take each item of data and code or give a descriptor for the data. The descriptor or name will often be a characteristic word from within the data.
5. Cluster the items of data that are related or connected in some way and start to develop patterns.
6. Patterns that are related to a preexisting theme are placed together with any other patterns that correspond with the theme along with direct quotes taken from the data (transcribed interviews, field notes, documents, etc.) to elucidate the pattern.
7. Any patterns that do not relate to preexisting themes should be kept in a separate file for future evaluation of the meanings as they relate to the overall topic. Repeat steps 1-7 for all participants.
8. Take all the patterns and look for the emergence of overarching themes. This process involves combining and clustering the related patterns into the preexisting themes.
9. After all the data have been analyzed, arrange the themes to correspond with the supporting patterns. The patterns are used to elucidate the themes.
10. Now revisit the patterns that did not fit the preexisting categories and remain open to any new patterns and themes that are related to the research topic and have emerged from the data analysis.
11. For each theme, the researcher needs to write a detailed analysis describing the scope and substance of each theme.
12. Each pattern should be described and elucidated by supporting quotes from the data.
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13. Finally, the themes are synthesized together to form composite synthesis of the question under inquiry.
Thematic Analysis with Constant Comparison (CC)
Thematic analysis with constant comparison can be either inductive analysis or theoretical analysis. The difference is that the data collected are analyzed as they are collected. The analysis begins during the collection of data. The first participant’s data are analyzed and as each subsequent participant’s data are analyzed, they are compared to the previously analyzed data. The analysis constantly moves back and forth between current data and the data that have already been coded and clustered into patterns. Patterns and themes will change and grow as the analysis continues throughout the process.
1. Review and familiarize yourself with the data collected from the first participant (interviews, journals, field notes, records and documents). Read the documents and highlight intuitively any sentences, phrases, or paragraphs that appear to be meaningful.
2. Review the highlighted data and use your research question to decide if the highlighted data are related to your question. Some information in the transcript may be interesting, but not relate to your question.
3. Eliminate all highlighted data that are not related to your question, however, start a separate file to store unrelated data. You may want to come back and reevaluate this data in the future.
4. Take each set of data and code or name the data. 5. Cluster the sets of data that are related or connected in some way and start
to develop patterns. 6. Complete this process for the first participants’ data. The researcher will
code and cluster the first participant’s data and as each subsequent participant’s data are analyzed, they are compared to the previously analyzed data. Throughout this process, each participant’s data are reviewed and analyzed, and the researcher is comparing and contrasting the data being analyzed with the data that have been previously analyzed in the study. Thus, a constant comparison emerges.
7. Throughout this process, data that correspond to a specific pattern are identified and placed with the corresponding pattern and direct quotes are taken from the data (transcribed interviews, field notes, documents, etc.) to elucidate the pattern.
8. Throughout the process, take all the patterns and look for the emergence of overreaching themes. This process involves combining and clustering the related patterns into themes.
9. Patterns and themes may tend to shift and change throughout the process of analysis, as previously completed analyses are compared with new data.
10. After all the data have been analyzed, arrange the themes to correspond with the supporting patterns. The patterns are used to elucidate the themes.
11. For each theme, the researcher writes a detailed analysis describing the scope and substance of each theme.
12. Each pattern should be described and elucidated by supporting quotes from the data.
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13. The data are synthesized together to form composite synthesis of the question under inquiry.
Thematic Analysis in a Mixed Methodology Study
Thematic analysis is often used in conducting the analysis of the qualitative portion of the data collected in a mixed methodology study. There are a number ways to develop a research design using mixed methods, below are a number of mixed methodology designs common to the field of psychology. An excellent source for more about mixed methods studies is Creswell (2003, 2008, 2013)
• Sequential studies (or what Creswell, 2003, calls two-phase studies): The researcher first conducts a qualitative phase of a study and then a quantitative phase, or vice versa. The two phases are separate.
• Parallel/simultaneous studies: The researcher conducts the qualitative and quantitative phase at the same time.
• Equivalent status designs: The researcher conducts the study using both the quantitative and qualitative approaches about equally to understand the phenomenon under study.
• Dominant-less dominant studies: The researcher conducts the study “within a single dominant paradigm with a small component of the overall drawn from an alternative design” (Creswell, 1995, p. 177).
• Designs with multilevel use of approaches: Researchers use different types of methods at different levels of data aggregation. For example: data could be analyzed qualitatively at the student level, qualitatively at the class level, quantitatively at the school level and qualitatively at the district level (Tashakkori & Teddlie, 1998, p. 18).
Aronson, J. (1994). A pragmatic view of thematic analysis. The Qualitative Report, 2(1).
Retrieved from http://www.nova.edu/ssss/QR/index.html Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research
in Psychology, 3, 77-101. Creswell, J. (1995). Research design: Qualitative and quantitative approaches. Thousand
Oaks, CA: Sage Publications, Inc. Creswell, J. (2003). Research design: Qualitative, quantitative, and mixed methods approaches
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(3rd ed.). Thousand Oaks, CA: Sage Publications, Inc. Creswell, J. (2013). Research design: Qualitative, quantitative, and mixed methods approaches
(4th ed.). Thousand Oaks, CA: Sage Publications, Inc. Taylor, S., & Bogdan, R. (1998). Introduction to qualitative research methods (3rd ed.). New
York, NY: John Wiley & Sons, Inc. Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and
quantitative approaches. Thousand Oaks, CA: Sage Publications, Inc.
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William H. Percy, PhD, was trained in qualitative methodologies at the Union Institute, where he received his PhD in Family Social Science in 1982. After practicing psychology and family therapy in Minneapolis for ten years, he joined the faculty of the Capella University, first in the Human Services department in 1996, and then the Harold Abel School of Psychology in 1998, where he served in various roles, including Director of Residence, Acting Director of Field Training, and core faculty. He served on the University Institutional Review Board for many years, and was instrumental, with Drs. Sandra and Kim Kostere, and Dr. Malcolm Gray in developing much of the infrastructure of the research programs for the Department of Psychology. Percy retired in 2012, and now writes novels in Idaho. Correspondence regarding this article can be addressed directly to: Dr. William H. Percy at E- mail: firstname.lastname@example.org or Phone: 208-264-1116
Kim Kostere has a Ph.D. in Clinical Psychology. He graduated from The Union Institute in 1989. He also has a Psy.S. in Humanistic and Clinical Psychology and Education, from the Center for Humanistic Studies. Dr. Kostere is a Licensed Psychologist and a licensed Marriage and Family Therapist in the State of Michigan. Dr. Kostere is a Clinical Member in AAMFT. He has over 20 years of experience in the field of psychotherapy and health care administration. Dr. Kostere’s research background is in qualitative research. He studied with both Clark Moustakas and Bruce Douglass. His master’s thesis and dissertation were both qualitative heuristic in design. He is currently faculty at Capella University and has been faculty since December, 1999. Dr. Kostere teaches advanced qualitative analysis, has designed courses, and mentors qualitative dissertations. He is currently serving as an Assistant Editor at The Qualitative Report. Correspondence regarding this article can also be addressed directly to: Dr. Kim Kostere at E-mail: email@example.com
Sandra Kostere has a Ph.D. in Clinical Psychology. She graduated from The Union Institute in 1989. She also has a Psy.S. in Humanistic and Clinical Psychology and Education, from the Center for Humanistic Studies. Dr. Kostere is a Licensed Psychologist in the State of Michigan and has over 20 years of experience in the field of psychotherapy and health care administration. Dr. Kostere’s research background is in qualitative research. She studied with both Clark Moustakas and Bruce Douglass. Her master’s thesis and dissertation were both qualitative heuristic in design. She is currently faculty at Capella University and has been faculty since December, 1999. Dr. Kostere teaches advanced qualitative analysis, has designed courses, and mentors qualitative dissertations. She is currently serving on the Editorial Board of The Qualitative Report. Correspondence regarding this article can also be addressed directly to: Dr. Sandra Kostere at E-mail: Sandra.firstname.lastname@example.org or Phone: 239-775-4775
Copyright 2015: William H. Percy, Kim Kostere, Sandra Kostere, and Nova Southeastern University.
Article Citation Percy, W. H., Kostere, K., & Kostere, S. (2015). Generic qualitative research in psychology.
The Qualitative Report, 20(2), 76-85. Retrieved from http://www.nova.edu/ssss/QR/QR20/2/percy5.pdf
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
- Generic Qualitative Inquiry
- Differentiating Generic Qualitative Inquiry
- Differentiating Generic Qualitative Inquiry from Phenomenological Inquiry
- Generic Qualitative Data Collection
- Data Analysis in Generic Qualitative Analysis: Thematic Analysis
- Aronson, J. (1994). A pragmatic view of thematic analysis. The Qualitative Report, 2(1). Retrieved from http://www.nova.edu/ssss/QR/index.html
- Generic Qualitative Inquiry