thematic analysis in social work a case study

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Home > Books > Global Social Work - Cutting Edge Issues and Critical Reflections

Thematic Analysis in Social Work: A Case Study

Submitted: 14 May 2019 Reviewed: 02 September 2019 Published: 09 December 2019

DOI: 10.5772/intechopen.89464

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The article aims to provide a step-by-step description of how thematic analysis was applied in a study examining why men choose to undertake social work as an area of study. Participants in the study came from the University of Concepción in Chile and the University of Quebec in Abitibi-Témiscamingue in Canada. The six phases of the thematic analysis are described in detail to provide students and novice social work researchers with a guide to this method of analysis. Thematic analysis offers a flexible, yet rigorous approach to subjective experience that is highly applicable to research in social work as a means of promoting social justice and combating inequalities.

• thematic analysis
• social work
• qualitative research

Author Information

Oscar labra *.

• Department of Human and Social Development, Université du Québec en Abitibi-Témiscamingue, Canada

Carol Castro

• Université du Québec en Abitibi-Témiscamingue, Canada

Robin Wright

• School of Social Work, University of Windsor, Canada

Isis Chamblas

• School of Social Work, Bío-Bío, University of Concepción, Chile

*Address all correspondence to: [email protected]

1. Introduction

There exist few detailed guidelines for thematic analysis, which represents a gap in the scientific literature. This article aims to partially remedy this scarcity by examining thematic analysis methods, drawing on the authors’ experiences as social work researchers, particularly as pertains to a case study. The present study is a six-step guide addressed specifically to students and novice researchers.

Thematic analysis has gained increasing currency in various branches of social work research, such as qualitative analysis [ 1 , 2 , 3 ], aboriginal research [ 4 ], resilience studies [ 5 ], the practice of social work in healthcare [ 6 , 7 , 8 ], and minors [ 9 , 10 ]. Nevertheless, little has been written on the specific adaptations and modulations that thematic analysis requires for use in social work research if it is to reflect the field’s specific preoccupations. It is important to note from the outset that thematic analysis in qualitative research is an empirical inductive approach to collect data.

The particular importance of qualitative research methods, such as thematic analysis, for social work is that these approaches can also serve to promote social justice and combat inequalities. Qualitative methods allow researchers to transmit people’s ideas, perceptions, and opinions by analyzing and disseminating participant discourses. This “speech act” is based on the values that guide social work, namely, respect for personal and collective rights, as well as a recognition of the need to perceive and understand human beings as constituents of an interdependent system that carries the potential for change. In employing qualitative research methods, social work researchers have a responsibility to promote social change and contribute to resolve social problems by analyzing and disseminating collected testimonies, which also serve as a basis from which to formulate future research and intervention paths. No other research methods have the same capacity to give voice to the disenfranchised in order to foster social change.

In order to contextualize the discussion of thematic analysis, the following section will first explore the broader framework of qualitative research. Why is qualitative research well suited to social work? After examining potential answers to this question, the discussion will then proceed to its core subject: thematic analysis and its usefulness in social work research, demonstrated by specific examples from fieldwork. This constitutes the primary aim of the present article.

2. Qualitative research and its relevance for social work

Qualitative methods are an established component of research models in various branches of inquiry, including social work, and have been used by social work researchers studying a range of dimensions, such as the family [ 11 , 12 , 13 , 14 ], women [ 15 , 16 , 17 ], children [ 18 , 19 , 20 , 21 ], and mental health services [ 18 , 22 , 23 , 24 ]. Thus, qualitative research methods have served to develop various domains of social work intervention ( Table 1 ).

Characteristics of qualitative research.

Source: Deslauriers [ 25 ]; Guba [ 26 ]; Hatch [ 27 ]; Hernández Sampieri et al. [ 28 ]; Marshall and Rossman [ 29 ]; Morse and Richards [ 30 ].

Over the past three decades, many authors have proposed varying definitions of qualitative research. Table 2 shows the major components of those definitions, providing clues as to the fundamental elements of the “DNA” of qualitative research and their relevance for social work.

Qualitative research criteria.

3. Applications of qualitative research

The elaboration of a research protocol or project requires asking whether qualitative research is relevant to the study’s methods and goals. The choice to adopt a qualitative approach is generally based on at least one of the criteria presented in Table 3 .

Research questions typology.

These seven elements represent contexts in which qualitative research is apposite. In order to demonstrate the application of these elements in fieldwork, Table 3 presents examples of questions used by the authors in previous qualitative studies.

Qualitative research includes a range of analytical methods applicable in various contexts. Those that appear to be adopted most often include phenomenographic analysis, phenomenological analysis, grounded theory (GT), case studies [ 32 ], narrative analysis [ 31 ], content analysis [ 33 , 34 , 35 ], participatory action research [ 36 , 37 , 38 ], aboriginal research [ 39 , 40 , 41 ], discourse analysis [ 42 , 43 , 44 , 45 ], and systematic analysis [ 46 , 47 ].

4. Defining thematic analysis

The definition of thematic analysis adopted in the present paper is that of a method that allows researchers to identify and organize relevant themes and subthemes, which can then be used as units of analysis [ 48 , 49 ] in subsequent detailed re-readings of a data set [ 50 ], through which researchers increasingly familiarize themselves with the data and explore the meanings associated with the concepts emerging from participant testimonies [ 51 , 52 ]. The central operation of thematic analysis, therefore, is thematization [ 53 ]. It is important to specify that “data set” refers to all materials compiled within the scope of a given study: transcripts of interviews conducted with participants, written testimonies, verbal communications, study objectives, and research questions, as well as all other relevant materials, which can include newspaper articles, annual research reports, and social work intervention reports, among others.

Repeated readings of a data set are necessary for the identification of the most salient significations in the collected materials. It is through these processes that researchers can reveal the affective, cognitive, and symbolic dimensions of the assembled data.

Social work research should seek to address issues of social justice and inequality or, at the very least, should not contribute to deficit constructions of marginalized populations by failing to acknowledge issues of discrimination and oppression.

5. The phases of thematic analysis

Thematic analysis involves six phases (see Figure 1 ). For the purposes of the present discussion, these phases will be described using examples from the authors’ experiences during a previous study, in which one of the main research themes was the reasons why certain men choose professions socially viewed as feminine [ 54 ]. The study involved 26 male participants enrolled in social work university programs: 13 in Chile and 13 in the Canadian province of Québec. The research question was exploratory, since no previous studies had addressed the issue directly; the thematic analysis, therefore, required a high degree of interpretation to fully grasp the significations emerging from participant testimonies. Specifically, the research question sought to discover the motivations, obstacles, and positive reference points, which characterized men’s interest in social work, a profession socially viewed as feminine. The following extensive discussion will refer to examples from the aforementioned study in order to examine in detail the methodological progression of the six phases of thematic analysis.

Thematic analysis: Six interactive phases.

Presentation of results.

It is essential to note that the six phases presented in Figure 2 overlap and interact: the phases are not exclusively successive, since there is a measure of recursion involved, in what is nevertheless a generally linear process. These characteristics indicate that thematic analysis is a flexible yet rigorous method of data analysis (see Figure 1 ). Three distinct approaches may be applied to thematic analysis: deductive (when themes are defined at the outset, prior to analyses), inductive (when themes emerge in the course of analysis), or, frequently, a deductive-inductive combination.

5.1 Phase 1: Familiarization with collected data

The first phase begins with the task of transcribing audio recordings of individual or group interviews carried out in the course of the study. The next step involves proceeding through initial readings of the transcripts in order to find the most salient significations in the participants’ testimonies. The material must be read thoroughly, attentively and analytically, particularly in order to identify those elements that may at first seem banal, yet frequently crucial to understanding the significations of a participants’ discourse.

Several techniques can help researchers to structure their first readings of the material. For example, an initial coding chart allows for the clear identification of excerpts that appear immediately relevant. As well, researchers familiar with thematic analysis frequently make annotations in the margins of transcripts or highlight in color certain excerpts that appear to be particularly significant.

The following excerpt and the comments cited below illustrate one researcher’s initial observations following a first reading of material collected in the course of a study:

Q: Which factors influenced your career choice?

A: I have a childhood friend who is a SW [social worker] and I went to talk with him. He told me about the main orientations of the profession, and I took the decision to enter this line of work. So it was the advice of a friend that helped me to make my decision to undertake social work, which I had not really thought about when I was in high school. It came from these conversations with this friend about the program, and I feel that I do not regret this decision. (Chilean participant No. 8, page 54)

The meanings identified in the testimony of Participant No. 8 were that: a) the participant had a close relationship with a social worker who influenced his career choice; b) the participant wanted to learn about the profession before deciding to undertake it; c) the profession’s orientations attracted the participant; d) the participant had not chosen a career path upon completing his secondary education; and e) the participant was satisfied with his choice of studies.

The example demonstrates that even a short interview excerpt can be a rich source of information, in this case indicating the various factors that characterized and influenced the participant’s choice to study toward a career in social work.

It is worth noting that qualitative data software, such as Nvivo®, presents additional coding capabilities and is in widespread use. For the purposes of the present study, however, the researchers opted to employ a manual coding technique.

Listening to and transcribing participant interviews.

Before undertaking readings of the material, it is helpful to construct an initial coding chart on which researchers can record their first impressions of the readings; this coding chart may identify the participant’s pseudonym, the specific excerpt in question, the transcript page number, and the signification or observation noted.

It is useful, as well, to keep the study objectives physically visible or close at hand for quick reference; this is especially recommended if the researcher carrying out the thematic analysis did not personally carry out the interviews or did not participate in the elaboration of the research project.

Initial readings of the material should be carried out repetitively, without at first overly focusing on particular details, in order to develop a familiarity with the raw data collected from participants. Examples of questions to keep in my mind during these first readings include:

What is this person trying to say?

Why are they talking about that in this particular way?

How should I interpret what I am reading?

In order to maintain familiarity with the raw data, repeated readings must be carried out in close succession, which contributes to a fuller understanding of participant testimonies and their significations. Researchers must keep in mind that they are scrutinizing the data for any and all information that relates to the research question and study objectives.

During these surface readings of the data set, researchers should use the initial coding chart to note any emerging elements that seem unfamiliar, interesting, or specifically related to the study question and objectives.

It is possible that following a few initial readings, researchers will be able to identify certain elements of data as themes (normally, this operation is not carried out until Phase 3). It is advisable in these instances to proceed cautiously, noting all pertinent elements on the coding chart and continuing to progress through the readings while noting elements that appear related to the theme, but refraining from premature definition.

Within a constructivist perspective, in the first phase of thematic analysis, the researcher adopts a subjectivist epistemological approach the reality under study. In the course of this process, researcher and respondent become a mutually constructed unit. The results, therefore, are the products of interactions between their realities ([ 26 ] in [ 55 ]:p. 17). In this process of production, social work researchers must maintain consciously reflexive, in order to minimize the potential effects of their prejudices or opinions, which could otherwise deform or falsify interpretation.

It is always preferable that the researcher carrying out the readings be the same person that carried out interviews with participants; this will place the researcher in a better epistemological position to ensure continuity throughout the thematic analysis process. If someone else is tasked with carrying out the readings, it is imperative that they become highly familiar with all aspects of the research project before beginning their analyses.

5.2 Phase 2: Generating initial codes

In this second phase, the researcher will use information identified as relevant in Phase 1 to generate initial codes. At the outset, researchers begin grouping elements of data according to similarities or perceived patterns: these are initial codes (see Tables 4 and 5 ). This ordering of the data is necessary to develop a comprehensive perspective on the participants’ latent or semantic discourse. An experienced researcher will likely proceed more quickly through this process; indeed, some researchers frequently combine the first two phases of thematic analysis.

Coding chart: Chile students.

Coding chart: Quebec students.

To begin, a code is a type of raw data extracted from interviews and field notes. These include words or phrases that are representative of groups or patterns of data (see Table 4 ). Miles and Huberman [ 56 ] identify three types of codes. The first is descriptive codes, which require very little interpretation. The second is interpretive codes, which represent data that require a certain depth of interpretation in order to be fully understood. The third type is inferential codes, relating to data that are explicative and indicate causal relationships.

Within the classification elaborated by Miles and Huberman [ 56 ], therefore, the examples presented in this article largely correspond to the descriptive type. When identifying descriptive codes, researchers have two options: using words or phrases drawn directly from participant testimonies (Level 1) or, where more appropriate, making reference to concepts drawn from relevant theory. The body of accumulated conceptual knowledge allows social work researchers to contextualize problems under study and more fully understand participants’ subjective reality. Social work researchers must remain conscious, however, of how their hypotheses influence their formulations of research questions, objectives, and resulting methodological choices that necessarily precede their analyses.

In order to systematically classify the information, codes and interview excerpts should be grouped in relation to clearly identify study objectives, as shown in Tables 4 and 5 . Particularly for researchers unfamiliar with thematic analysis, this method is effective in developing a better grasp of the classification processes involved in classifying generated data within the scope of defined study objectives.

Codes are always a combination of the descriptive and interpretive. This is evident in the preliminary codes cited in Tables 4 and 5 .

It is important to note that this method does not require codes to be generated for every line of transcript in the data set. Depending on interview type, a data set typically contains between 7000 and 9000 words, or close to 700 lines. A code can represent two, three, or more lines of transcript. It is always advisable to begin by working with the specific words used by participants (Level 1) and only after repeated readings to begin establishing links with concepts drawn from theory (Level 2), as in Table 6 , for example.

Thematic matrix.

Phase 2 concludes once all the elements of the data set have been coded. It is important to note that there is no minimum or maximum number of codes to be generated from a data set: the number is determined by each researcher’s judgment in assessing what is or is not pertinent, a skill that develops over time, in the course of work with transcripts.

5.3 Phase 3: Searching for themes

In qualitative research, a theme (sometimes also termed “category”) [ 31 ] is an element of data or sequence of words that can serve as a synoptic and accurate representation of the signification that interviewed participants attribute to an object, phenomenon, or situation. A theme, therefore, is composed of coded data grouped together according to similarities or patterns.

The search for themes is open ended, and the number and variety of results will depend on how systematically and thoroughly the first two phases were carried out. The process involves identification, differentiation, recombination, and grouping: certain themes will emerge distinctly from the data, others will be the product of either identifying more than one theme in what at first appeared to be one integral category, while others will emerge from the fusion of two or more themes that initially appeared distinct; themes that are divergent, yet related, may also be grouped into broader categories. With certain data sets, yet another level of classification will map the hierarchical relationships between themes. For Crabtree and Miller [ 57 ], the process of linking themes leads to the discovery of yet other themes and patterns in the data, that is, it generates overarching themes and allows for the identification of broad connections. This process of grouping distinct elements identified within a data set into themes constitutes the core task of thematic analysis.

In the example of the study discussed in the present article, data collected from interviews with Chilean and Québec students 1 were coded according to the study’s primary objective. As Table 7 demonstrates, a primary theme was identified in reference to theory (influence of life trajectory), while three subthemes emerged from the coded data.

Final thematic matrix.

Table 6 demonstrates how a primary theme connects three subthemes generated from seven distinct codes. In this example, the motivations to pursue social work of Chilean and Québec students participating in the study were all grouped in the primary theme “Influence of life trajectory.”

As mentioned above, there are no guidelines dictating minimum or maximum numbers of themes or subthemes to identify in a given study, independent of particular factors, such as number of participants. It is of utmost importance that themes and subthemes be delineated precisely in order to represent accurately and comprehensively the complexity of data collected from study participants. Themes therefore will vary qualitatively, substantively, and quantitatively from one study to another. In the example cited, a single-primary theme proved sufficiently broad to represent the significations derived from the data, enabling the authors to answer the research question and achieve the study objective.

A method useful in Phase 3 is to elaborate a coding sheet on which to classify elements of data that could not be precisely categorized in Phase 2 or that do not appear directly linked with the research question or study objectives. These data can prove highly relevant later, as additional themes are identified.

reading through the coding generated during Phase 2 (see Table 5 ), from right to left, in order to verify the accuracy of the identified elements of data;

assessing the correlation of codes with interview excerpts, as well as their relevance in relation to study objectives;

grouping the coded information in reference to concepts or sequences of words according to similarities or patterns: this is the identification of themes;

reviewing the identified themes in order to further categorize subthemes, overarching themes, or groups of themes, as the case may be; and

reading the material in order to identify hierarchical relationships between the themes.

Throughout this process, it is essential to keep in mind the stated study objectives, as well as to question continually whether the codes, themes, and subthemes are relevant to the research question and study objectives or whether they fall beyond the delineated scope of the study. It is important to point out that the themes and subthemes in which codes are grouped can represent concepts drawn from theory or original categories elaborated by the researcher. The epistemological challenge for researchers is to remain analytical in relation to the data that emerge from this phase of coding and to analyze them with reference to theory.

Phase 3 culminates in the elaboration of a thematic matrix that demonstrates connections between themes, subthemes, and codes (see Table 7 ). The matrix offers a clear overview of the ordered complexity of the relationships identified within the data set. It is useful, as well, to include within the matrix a column listing the study objectives or research question, providing an easily accessible reference with which to verify the relevance of data to the stated research goals.

5.4 Phase 4: Reviewing the themes

A comprehensive description of a given phenomenon requires a systematic review of the themes identified in Phase 3. Although, for the purposes of discussion, Phase 4 is identified as distinct from and subsequent to Phase 3, in practice researchers familiar with thematic analysis will frequently carry out the two phases simultaneously.

Is this a theme, subtheme, or code?

Does the theme accurately represent the data with which it is linked (codes and interview excerpts)?

Is the theme too abstract or difficult to understand or, conversely, is it so specific that it cannot be linked more broadly with data?

Is there a clearly identifiable logic to the hierarchical relationships between themes, subthemes, and codes (i.e., clear distinction between broader categories and more specific elements, as in Table 7 ?

Which data do the theme include and which do these exclude?

Is the theme a good representation of the subthemes? Are the subthemes a good representation of the codes?

Does the thematic matrix contain the information necessary to answer the research question and the study objectives?

These questions allow the researcher to assess the validity of the matrix and the coherence of its components. As in the preceding steps, validating the relevance of each element and the links between them is essential to ensuring the authenticity of results. It is important, however, to nuance the notion of validity. In qualitative research, a result is only considered valid if it is reproducible, that is, if it is not an individual occurrence of a given observation. Validity, moreover, may be internal or external. Internal validity refers to the degree to which valid conclusions can be drawn from a study, based on an assessment of all research parameters. External validity is the degree to which internally valid results may be extrapolated beyond specific study samples and settings, that is, to people and contexts other than those considered in the study.

A range of factors may have an incidence on a study’s internal validity, including participants’ personal histories, maturation and pretest habituation, participant selection, experimental mortality, and instrument bias. External validity is subject to other factors, such as interaction between historical factors and interventions, the effect of reactivity (that is, participants’ awareness of taking part in a study resulting changes in behavior), and researcher bias.

Researchers must also take into account other dimensions of validity relevant to social work research, for example, reflexive practice in collaboration with other researchers [ 58 ], data triangulation [ 59 , 60 ], and iterative research that allows participants to react to interpretations of previous results.

A detailed, comprehensive review of the thematic matrix frequently results in adjustments, including changes to the designations and relative positions of codes and themes, as well as the outright deletion of certain themes and subthemes that are not relevant to the research question (see Table 7 ). As a result of this review process, it is often necessary to rename themes that prove unclear, inaccurate, or disconnected from the identified codes. In such cases, themes are said to have evolved. As with each step of each phase, it is through the practice of these operations that researchers unfamiliar with thematic analysis will develop a better grasp of its techniques.

A comparison between Tables 6 and 7 illustrates this process. In this case, the subthemes initially identified as referring to experiences were adjusted in Table 7 to represent motivations. A second important change consisted in adjusting the code designated in Table 6 as “educational performance,” in order to further specify “ good educational performance” in Table 7 . A final change made to the thematic matrix concerned the position of the “ parental influence ” code, which had been placed in the “professional trajectory” subtheme in Table 7 but, subsequent to review, was placed within the “personal motivations” subtheme in Table 7 . In this example, the other data in the matrix remained unchanged following the Phase 4 review (see Table 7 ).

A valuable method of ensuring that the themes, subthemes, and codes are clearly delineated and appropriately positioned is to submit the thematic matrix to additional review by one or two researchers uninvolved in the study who are familiar with thematic analysis methods. If the reliability analysis process is successful, that is, if the independent reviewers concur that the themes reliably represent the codes derived from the data set to which they are linked within the matrix, the thematic analysis can proceed to Phase 5.

5.5 Phase 5: Defining and naming themes

Phase 5 consists of two major stages. First, the themes and subthemes undergo a definitive revision. Thus, the thematic matrix must once again be analyzed thoroughly in order to assess the validity of hierarchical relationships and verify whether the designations given at both levels are an accurate reflection of the significations represented by the codes. It is essential that names given to the themes be revised repeatedly, until no ambiguities remain as to their accuracy. The second stage of Phase 5 is interpretive and consists in the conceptual definition of the themes and subthemes that will be subject to analysis in Phase 6.

Educational motivations: an individual’s [student’s] capacity to construct short- and long-term objectives [in their educational trajectory], notwithstanding difficulties. It is through motivation that needs are transformed into objectives and projects [ 61 , 62 ].

Personal motivations: the choice, energy, and direction of behavior [ 63 ].

Professional motivations: the set of dynamic factors that determine an individual’s [student’s] interest in succeeding [in the chosen profession] [ 64 ].

In defining themes, it is advisable to refer exclusively to specialized reference works conventionally accepted in relevant fields of study, such as dictionaries or encyclopedias of social work, education, or sociology, depending on the focus of a given study.

It is important to mention that the boundary between Phases 4 and 5 may be difficult to pinpoint, since both involve a revision of the themes. The distinction lies in that the final revision and conclusive assessment of themes in Phase 5 is the culmination of the repeated reviews of designations, categories, and relationships performed in Phase 4. In Phase 5, therefore, the researcher’s principal task is to define and name the themes, in reference to all the operations performed in the previous phases, ensuring that they faithfully represent the significations emerging from the data set.

5.6 Phase 6: Presenting and discussing results

Whether to be included in a book, article, or other form of publication, the crux of the material supporting the results presented and discussed is to be found in notes taken by researchers during interviews with participants and the thematic matrix developed in Phase 3 and revised in Phase 4. For the purposes of the present discussion, it is worthwhile to address the two main components of Phase 6, presentation and discussion, as distinct from one another.

In the presentation of results, researchers must produce a clear and coherent description that makes reference the data outlined in the thematic matrix. The presentation should be accompanied by explanations and clarifications sufficient for readers unfamiliar with the specific area of study to understand the results without room for erroneous interpretation. It is highly advisable to quote interview excerpts that are particularly illustrative of the assertions and conclusions described.

A clear presentation of data outlined in the thematic matrix should reflect the order of the hierarchical relationships between the themes and subthemes. In the study of Chilean and Québec students enrolled in social work programs described in the present article, the primary theme of “ influence of life trajectories ” integrated all subthemes and associated codes. The presentation of results, therefore, began with a description of the primary theme and then proceeded through a descriptive and coherent account, supported by illustrative interview excerpts, that outlined all relevant elements of data, beginning with the most broadly inclusive (primary theme) and proceeding toward the most specific (codes).

This is clearly evident in the following excerpt of the presentation of results in the study involving male social work students in Chile and Québec, which provides valuable examples of thematic analysis methods.

This section will first present the motivations that prompt Chilean and Québec male students’ choices to undertake social work. […] On the personal level, the two primary motivations that emerge from the testimonies of Chilean students are the desire to help others and the appeal of social work as a vocation, followed in the order of importance by the influence of family or social circle members who had studied social work.

The following excerpt from the testimony of one student (1) illustrates the motivation to help others and undertake social work as a vocation: “I went into social work […] to be able to help people. I believe that this is the factor that made me enrol” (René). The testimonies of Québec students, however, suggest that their strong motivations are attributable to good relationships with family, specifically parents [who had worked in the health system], and negative personal experiences in the past, among others. The testimony of one participant typifies this primary motivation of most Québec students participating in the present study: “The fact of having two parents who work in the health system. Since I was little, I have been going to hospitals and I have seen how it all works” (Simon) [ 54 ].

It is important that the presentation of results remains descriptive, as in the example cited above. The logical question to ask at this point is: when does the presentation of results end? The answer, too, is logical: when the relevant elements of the final subtheme have been presented. In the study cited above, therefore, the presentation concludes with a description of the professional motivations subtheme (see Table 7 ). Once the results have been comprehensively presented, they must subsequently be discussed.

In the discussion, researchers must address the presented results within an analytical perspective. As in the example cited below, the discussion makes reference to the broader literature relevant to the phenomenon under study:

The present study offers comparative and complementary views on the various dimensions associated with the motivations of men who engage in social work. Participants’ answers to the question “ What made you choose social work studies ?”, suggest that their motivations are varie, “multifactorial” [ 65 ] and linked with life trajectories. At the level of the sub-category of “educational motivations,” it appears that Chilean men seek cognitive and technical skills with which to achieve their goal of social change. The results suggest that these men aspire to the values of social work (EASSW, 2015 [3]) and a more humanist and just society in which social work occupies a position of importance among social science professions. These motivations originate in two factors. One is the participants’ social engagement prior to enrolling in university studies. The other is their personal orientation towards humanist values. In the case of participants from Québec, their main motivations lie in good results obtained during pre-university social science studies, which inspired them to undertake social work at university. Our results corroborate a number of previous studies [ 54 , 66 , 67 ].

The above excerpt illustrates how the discussion builds on the description of results in order to produce an analytical discourse that compares and contrasts the results and conclusions of the study with those of other studies and authors.

The discussion should follow the same order of themes as in the presentation of results.

It should underscore and further develop those themes that most closely correlate with the stated study objectives; it is not always possible to address all the obtained results within the discussion; therefore, a capacity to synthesize is particularly useful at this last stage of the thematic analysis.

The discussion must be framed analytically; the goal is to go beyond the descriptive, in order to demonstrate why the results are meaningful within the context of previous research.

7. Conclusion

As a qualitative research method that offers a simultaneously flexible and rigorous approach to data, thematic analysis allows social work students and novice social work researchers to approach the discourses, opinions, and visions of respondents both analytically. These qualities make it particularly applicable to social work research. Thematic analysis also represents the intellectual and ethic challenge, for experienced and novice researchers alike, of attempting to reveal and interpret themes and subthemes in the participants’ discourse. The ethical challenge for researchers is to avoid substituting personal objectives for research objectives, since this may impact the interpretation of data collected from participants and, consequently, skew study results.

The other challenge facing social work researchers employing thematic analysis is to keep their subjectivity in check. When describing and categorizing testimonies of human experiences, perspectives, and emotions, whether expressed in words or communicated inadvertently by respondents through behavior during interviews, researchers must remain especially vigilant so that their author’s own personal histories and professional experiences do not contaminate their interpretation of the data, altering the significance of participant testimonies. Indeed, this reflects one of the fundamental principles of social work practice and research methodologies that knowledge and techniques must always be applied methodically and objectively.

From the example that is present in this work and following the six stages of the thematic analysis, the researcher can draw inspiration to use this method of analysis and to apply other research designs. Finally, the qualitative research in Trabajo Social students from the two participating universities allowed us to use thematic analysis to better understand the motivations, difficulties, and anchors that make students from two different realities interest in social work.

Limitations

The thematic analysis approach discussed in the present paper must be interpreted with prudence. The article cited above provides a case example of how thematic analysis was applied in one study examining why men choose to undertake social work as an area of study. An additional limitation is the difficulty for researchers to ignore previous, tacit knowledge, which may have influenced the analysis of results [ 68 ]. Furthermore, the construction of certain themes and subthemes cited in Labra [ 54 ] may have been influenced by social desirability, that is, formulated so as to correspond with researchers’ expectations, given that Nvivo® software was not used to manage qualitative data. Nevertheless, the research design of the case example presented above, in which the interview guide was elaborated in reference to both the specific problem under study and a directly relevant conceptual framework, constitutes a significant element underpinning the validity of the thematic analysis approach.

We would like to thank Normand Brodeur, professor at the School of Social Work of Laval University, Canada, and Hugo Asselin, professor at the School of Aboriginal Studies of the University of Abitibi-Témiscamingue, Canada, for the reading and his valuable recommendations to this article. Similarly, we would like to thank in a very special way Carlos Contreras and Jorge Lara, students of the School of Social Work of the University of Concepción, Chile, who have been testing through research verbatim, the phases of the thematic analysis described in this article.

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• This study was built on the analysis of interviews with twenty-six (n = 26) students: 13 are respondents enrolled in a social work program at the University of Québec in Abitibi-Témiscamingue (UQAT) in Canada and the thirteen others enrolled in a social work program at a university of Concepción. The first participants were recruited by way of e-mail messages sent to male students enrolled at the University of Concepción and of University of Quebec in Abitibi-Témiscamingue on in undergraduate social work studies for the winter semesters of 2014 and 2015. The rest were recruited using the "snowball".

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Thematic analysis is a method of qualitative data analysis that was first described in the 1970s (Joffe, Harper and Thompson (eds), Qualitative Research Methods in Mental Health and Psychotherapy: A Guide for Students and Practitioners, Wiley-Blackwell, 2012) but became more prominent at the end of the 1990s with researchers such as Boyatzis ( 1998 ) and Hayes ( 1997 ) (as cited in Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol.2) (pp. 57–71). American Psychological Association.). As qualitfvecome more accepted across social science disciplines and now across health professions education, the need for systematic methods to analyze qualitative sets is more accentuated (Castleberry and Nolen, Currents in Pharmacy Teaching and Learning 10:807–815, 2018). ( Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol.2) (pp. 57–71). American Psychological Association.) highlighted that thematic analysis is “an accessible, flexible, and increasingly popular method of qualitative data analysis” (p. 57). Although thematic analysis shares similarities with other methodologies that have systematic processes for analyzing data such as Interpretative Phenomenological Analysis or grounded theory, it does not “require the detailed theoretical and technological knowledge” of these approaches (Braun and Clarke, Qualitative Research in Psychology 3:77–101, 2006). However, (Braun and Clarke, Qualitative Research in Psychology 3:77–101, 2006) emphasized that the theoretical position of the study needs to be made explicit, as there are inherent assumptions regarding the nature of the data that has been analyzed.

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Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA handbook of research methods in psychology (vol.2) (pp. 57–71). American Psychological Association.

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Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 , 77–101. https://doi.org/10.1191/1478088706qp063oa

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Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development . Sage.

Castleberry, A., & Nolen, A. (2018). Thematic analysis of qualitative research data: Is it as easy as it sounds? Currents in Pharmacy Teaching and Learning, 10 , 807–815. https://doi.org/10.1016/j.cptl.2018.03.019

Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5 , 1–11.

Hayes, N. (1997). Theory-led thematic analysis: Social identification in small companies. In N. Hayes (Ed.), Doing qualitative analysis in psychology (pp. 93–114). Psychology Press.

Joffe, H. (2012). Thematic analysis. In D. Harper & A. Thompson (Eds.), Qualitative research methods in mental health and psychotherapy: A guide for students and practitioners (pp. 203–223). Wiley-Blackwell.

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16 , 1–13. https://doi.org/10.1177/1609406917733847

Schwandt, T. A. (2015). The Sage dictionary of qualitative inquiry (4th ed.). Sage Publications.

Tuckett, A. G. (2005). Applying thematic analysis theory to practice: A researcher’s experience. Contemporary Nurse, 19 (1–2), 75–87. https://doi.org/10.5172/conu.19.1-2.75

Xu, W., & Zammit, K. (2020). Applying thematic analysis to education: A hybrid approach to interpreting data in practitioner research. International Journal of Qualitative Methods , 19 , 1–9. https://doi.org/10.1177/1609406920918810

Additional Resources

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11 (4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars. All Ireland Journal of Higher Education , 9 (3), 3351–3364. http://ojs.aishe.org/index.php/aishe-j/article/view/335

Thematic analysis:-7:23

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Thematic analysis (the ‘Braun & Clarke’ way): an introduction-1:02:19

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Understanding Thematic Analysis: 6 steps to perform Thematic Analysis- 6:26

https://www.youtube.com/watch?v=WodStS6nQSk

Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15 (3), 398–405. https://doi.org/10.1111/nhs.12048

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18.4 Thematic analysis

Learning objectives.

Learners will be able to…

• Explain defining features of thematic analysis as a strategy for qualitative data analysis and identify when it is most effectively used
• Formulate an initial thematic analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with thematic analysis?

As its name suggests, with thematic analysis we are attempting to identify themes or common ideas across our data. Themes can help us to:

• Determine shared meaning or significance of an event
• Povide a more complete understanding of concept or idea by exposing different dimensions of the topic
• Explore a range of values, beliefs or perceptions on a given topic

Themes help us to identify common ways that people are making sense of their world. Let’s say that you are studying empowerment of older adults in assisted living facilities by interviewing residents in a number of these facilities. As you review your transcripts, you note that a number of participants are talking about the importance of maintaining connection to previous aspects of their life (e.g. their mosque, their Veterans of Foreign Wars (VFW) Post, their Queer book club) and having input into how the facility is run (e.g. representative on the board, community town hall meetings). You might note that these are two emerging themes in your data. After you have deconstructed your data, you will likely end up with a handful (likely three or four) central ideas or take-aways that become the themes or major findings of your research.

Variations in approaches to thematic analysis

There are a variety of ways to approach qualitative data analysis, but even within the broad approach of thematic analysis, there is variation. Some thematic analysis takes on an inductive analysis approach. In this case, we would first deconstruct our data into small segments representing distinct ideas (this is explained further in the section below on coding data). We then go on to see which of these pieces seem to group together around common ideas.

In direct contrast, you might take a deductive analysis approach (like we discussed in Chapter 8 ), in which you start with some idea about what grouping might look like and we see how well our data fits into those pre-identified groupings. These initial deductive groupings (we call these a priori categories) often come from an existing theory related to the topic we are studying. You may also elect to use a combination of deductive and inductive strategies, especially if you find that much of your data is not fitting into deductive categories and you decide to let new categories inductively emerge.

A couple things to note here. If you are using a deductive approach, be clear in specifying where your a priori categories came from. For instance, perhaps you are interested in studying the conceptualization of social work in other cultures. You begin your analysis with prior research conducted by Tracie Mafile’o (2004) that identified the concepts of fekau’aki (connecting) and fakatokilalo (humility) as being central to Tongan social work practice. [1] You decide to use these two concepts as part of your initial deductive framework, because you are interested in studying a population that shares much in common with the Tongan people. When using an inductive approach, you need to plan to use memoing and reflexive journaling to document where the new categories or themes are coming from.

Coding data

Coding is the process of breaking down your data into smaller meaningful units. Just like any story is made up by the bringing together of many smaller ideas, you need to uncover and label these smaller ideas within each piece of your data. After you have reviewed each piece of data you will go back and assign labels to words, phrases, or pieces of data that represent separate ideas that can stand on their own. Identifying and labeling codes can be tricky. When attempting to locate units of data to code, look for pieces of data that seem to represent an idea in-and-of-itself; a unique thought that stands alone. For additional information about coding, check out this brief video from Duke’s Social Science Research Institute on this topic. It offers a nice concise overview of coding and also ties into our previous discussion of memoing to help encourage rigor in your analysis process.

As suggested in the video [2] , when you identify segments of data and are considering what to label them ask yourself:

• How does this relate to/help to answer my research question?
• How does this connect with what we know from the existing literature?
• How does this fit (or contrast) with the rest of my data?

You might do the work of coding in the margins if you are working with hard copies, or you might do this through the use of comments or through copying and pasting if you are working with digital materials (like pasting them into an excel sheet, as in the example below). If you are using a CAQDAS, there will be a function(s) built into the software to accomplish this.

Regardless of which strategy you use, the central task of thematic analysis is to have a way to label discrete segments of your data with a short phrase that reflects what it stands for. As you come across segments that seem to mean the same thing, you will want to use the same code. Make sure to select the words to represent your codes wisely, so that they are clear and memorable. When you are finished, you will likely have hundreds (if not thousands!) of different codes – again, a story is made up of many different ideas and you are bringing together many different stories! A cautionary note, if you are physically manipulating your data in some way, for example copying and pasting, which I frequently do, you need to have a way to trace each code or little segment back to its original home (the artifact that it came from).

When I’m working with interview data, I will assign each interview transcript a code and use continuous line numbering. That way I can label each segment of data or code with a corresponding transcript code and line number so I can find where it came from in case I need to refer back to the original.

The following is an excerpt from a portion of an autobiographical memoir (Wolf, 2010) [3] . Continuous numbers have been added to the transcript to identify line numbers (Figure 18.4). A few preliminary codes have been identified from this data and entered into a data matrix (below) with information to trace back to the raw data (transcript) (Figure 19.1).

• Mafile'o, T. (2004). Exploring Tongan Social Work: Fekau'aki (Connecting) and Fakatokilalo (Humility). Qualitative Social Work, 3 (3), 239-257. ↵
• Duke Mod U Social Science Research Institute. (2016, November 11). How to know you are coding correct: Qualitative research methods. [Video]. YouTube. https://www.youtube.com/watch?v=iL7Ww5kpnIM&feature=youtu.be ↵
• Wolf, H. R. (2010). Growing up in New York City: A generational memoir (1941-1960). American Studies Journal, 54. http://www.asjournal.org/54-2010/growing-up-in-new-york-city/ ↵

Thematic analysis is an approach to qualitative analysis, in which the researcher attempts to identify themes or patterns across their data to better understand the topic being studied.

An approach to data analysis in which we gather our data first and then generate a theory about its meaning through our analysis.

The act of breaking piece of qualitative data apart during the analysis process to discern meaning and ultimately, the results of the study.

Part of the qualitative data analysis process where we begin to interpret and assign meaning to the data.

An approach to data analysis in which the researchers begins their analysis using a theory to see if their data fits within this theoretical framework (tests the theory).

Categories that we use that are determined ahead of time, based on existing literature/knowledge.

A data matrix is a tool used by researchers to track and organize data and findings during qualitative analysis.

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Methodology

• How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

• How do patients perceive doctors in a hospital setting?
• What are young women’s experiences on dating sites?
• What are non-experts’ ideas and opinions about climate change?
• How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

• An inductive approach involves allowing the data to determine your themes.
• A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

• A semantic approach involves analyzing the explicit content of the data.
• A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Discourse analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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Caulfield, J. (2023, June 22). How to Do Thematic Analysis | Step-by-Step Guide & Examples. Scribbr. Retrieved April 13, 2024, from https://www.scribbr.com/methodology/thematic-analysis/

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