Thematic Analysis: Inductive vs Theoretical

Themes or patterns within data can be identified in one of two primary ways in thematic analysis: in an inductive or „bottom up‟ way (e.g., see Frith
Thematic Analysis - Inductive vs Theoritical

Thematic analysis is a flexible yet rigorous method for identifying, analyzing, and reporting patterns (themes) within data. To produce a high-quality analysis, researchers must make several explicit decisions regarding their approach to the data and the underlying theories of meaning.

1. Inductive vs. Theoretical Thematic Analysis

Themes or patterns within data can be identified in one of two primary ways: in an inductive or "bottom up" way, or in a theoretical or deductive or "top down" way.

Inductive Approach ("Bottom Up")

An inductive approach means the themes identified are strongly linked to the data themselves (Patton, 1990). In this approach, if the data have been collected specifically for the research (e.g., via interview or focus group) the themes identified may bear little relationship to the specific question that was asked of the participants. They would also not be driven by the researcher’s theoretical interest in the area or topic.

Data-Driven: Inductive analysis is a process of coding the data without trying to fit it into a pre-existing coding frame, or the researcher’s analytic preconceptions. However, it is important to note that researchers cannot free themselves of their theoretical and epistemological commitments; data are not coded in an epistemological vacuum.
Theoretical Approach ("Top Down")

In contrast, a "theoretical" thematic analysis would tend to be driven by the researcher’s theoretical or analytic interest in the area and is thus more explicitly analyst-driven. This form of thematic analysis tends to provide less of a rich description of the data overall and a more detailed analysis of some aspects of the data.

The choice between inductive and theoretical maps onto how and why you are coding the data. You can either code for a quite specific research question (theoretical) or the specific research question can evolve through the coding process (inductive).

Case Example: Researching Hetero Sex

Inductive Approach: A researcher reads and re-reads data for any themes related to heterosexual experiences and codes diversely, ignoring previous research themes like Hollway’s (1989) "male sexual drive."

Theoretical Approach: The researcher focuses specifically on the way permissiveness plays out across the data, resulting in themes that may expand on Hollway’s original work.

2. Levels of Identification: Semantic vs. Latent

Another decision revolves around the "level" at which themes are to be identified: at a semantic or explicit level, or at a latent or interpretative level (Boyatzis, 1998).

Feature Semantic Level Latent Level
Focus Explicit/Surface meanings. Underlying ideas, assumptions, and ideologies.
Process Description → Summarization → Interpretation. Interpretative work from the start.
Theory Often linked to existing literature in final stages. Already theorized during theme development.
The Jelly Analogy: Imagine your data as an uneven blob of jelly. The semantic approach seeks to describe the surface of the jelly—its form and meaning. The latent approach seeks to identify the internal features and structures that gave the jelly that particular form.

Analysis at the latent level tends to come from a constructionist paradigm (Burr, 1995). In this form, thematic analysis overlaps with "discourse analysis" (sometimes called thematic discourse analysis), where broader assumptions are theorized as underpinning what is actually articulated.

3. Epistemology: Realist vs. Constructionist

The research epistemology guides what you can say about your data and informs how you theorize meaning. This is usually determined during conceptualization but may raise its head again during analysis.

Essentialist/Realist Approach

With an essentialist/realist approach, you can theorize motivations, experience, and meaning in a straightforward way. This assumes a simple, largely unidirectional relationship between meaning, experience, and language (language reflects and enables us to articulate experience) (Potter & Wetherell, 1987).

Constructionist Approach

From a constructionist perspective, meaning and experience are socially produced and reproduced, rather than inhering within individuals (Burr, 1995). Therefore, analysis within this framework does not seek to focus on individual psychologies. Instead, it seeks to theorize the socio-cultural contexts and structural conditions that enable the individual accounts provided.

Final Integration: Thematic analysis that focuses on "latent" themes tends to be more constructionist and starts to overlap with thematic discourse analysis. However, not all "latent" thematic analysis is constructionist. Choosing the right path ensures your analysis is rigorous, theoretically sound, and aligned with your research goals.

Navigating Methodological Choices

The thematic analysis involves a number of choices that are often not made explicit (or are certainly typically not discussed in the method section of papers), but which need explicitly to be considered and discussed. In practice, these questions should be considered before analysis (and sometimes even collection) of the data begins, and there needs to be an ongoing reflexive dialogue on the part of the researcher or researchers with regards to these issues, throughout the analytic process. The method section of Taylor and Ussher’s (2001) thematic discourse analysis of S&M provides a good example of research that presents this process explicitly; the method section of Braun & Wilkinson (2003) does not.

What counts as a theme?

A theme captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set. An important question to address in terms of coding is what counts as a pattern/theme, or what ‘size’ does a theme need to be? This is a question of prevalence both in terms of space within each data item, and prevalence across the entire data set. Ideally, there will be a number of instances of the theme across the data set, but more instances do not necessarily mean the theme itself is more crucial.

As this is qualitative analysis, there is no hard-and-fast answer to the question of what proportion of your data set needs to display evidence of the theme for it to be considered a theme. It is not the case that if it was present in 50% of one’s data items, it would be a theme, but if it was present only in 47%, then it would not be. Nor is it the case that a theme is only something that many data items give considerable attention to, rather than a sentence or two.

A theme might be given considerable space in some data items, and little or none in others, or it might appear in relatively little of the data set. So researcher judgement is necessary to determine what a theme is. Our initial guidance around this is that you need to retain some flexibility, and rigid rules really do not work. (The question of prevalence gets revisited in relation to themes and sub-themes, as the refinement of analysis [see later] will often result in overall themes, and sub-themes within those.)

Determining 'Keyness' Beyond Quantification

Furthermore, the ‘keyness’ of a theme is not necessarily dependent on quantifiable measures – but in terms of whether it captures something important in relation to the overall research question. For example, in Victoria’s research on representations of lesbians and gay parents on 26 talk shows (Clarke & Kitzinger, 2004), she identified six ‘key’ themes. These six themes were not necessarily the most prevalent themes across the data set – they appeared in between 2 and 22 of the 26 talk shows - but together they captured an important element of the way in which lesbians and gay men "normalise" their families in talk show debates.

Key Takeaways: Methods of Measuring Prevalence
  • Data Item Level: Counting if a theme appears anywhere in each individual data unit (e.g., a single talk show).
  • Speaker Level: Counting the number of different participants who articulated the theme across the data set.
  • Occurrence Level: Counting each individual instance across the entire set (raises questions on where instances begin/end).

Because prevalence was not crucial to the analysis presented, Victoria chose the most straightforward form, but it is important to note there is no right or wrong method for determining prevalence. What is important is that you are consistent.

There are various ‘conventions’ for representing prevalence in thematic (and other qualitative) analysis that does not provide a quantified measure (unlike much content analysis, Wilkinson, 2000). The following table outlines common rhetorical descriptors:

Convention / Descriptor Reference Example
“The majority of participants” Meehan et al., 2000: 372
“Many participants” Taylor & Ussher, 2001: 298
“A number of participants” Braun, Gavey, & McPhillips, 2003: 249
Such descriptors work rhetorically to suggest a theme really existed in the data, and to convince us they are reporting truthfully about the data. But do they tell us much? This is perhaps one area where more debate needs to occur about how and why we might represent the prevalence of themes in the data, and, indeed, whether, if, and why prevalence is particularly important.

Rich Description vs. Detailed Aspect Account

It is important to determine the type of analysis you want to do, and the claims you want to make, in relation to your data set. These two approaches offer different strategic benefits:

Approach A: Rich Thematic Description of Entire Data Set

You might wish to provide a rich thematic description of your entire data set, so that the reader gets a sense of the predominant or important themes. In this case, the themes you identify, code, and analyse would need to be an accurate reflection of the content of the entire data set.

In such an analysis, some depth and complexity is necessarily lost (particularly if you are writing a short dissertation or article with strict word limits), but a rich overall description is maintained. This is useful for under-researched areas or unknown participant views.

Approach B: Detailed and Nuanced Account of One Particular Aspect

Alternative use of thematic analysis is to provide a more detailed and nuanced account of one particular theme, or group of themes, within the data. This might relate to a specific question or area of interest within the data (a semantic approach), or to a particular ‘latent’ theme across the whole or majority of the data set.

An example of this would be Victoria’s talk show paper (Clarke & Kitzinger, 2004), which examined normalisation in lesbians’ and gay men’s accounts of parenting.

References:
  1. Boyatzis, R. E. (1998). Transforming qualitative information. Sage.
  2. Burr, V. (1995). An introduction to social constructionism. Routledge.
  3. Frith, H., & Gleeson, K. (2004). Clothing and embodiment: Men managing body image and appearance. Psychology of Men & Masculinity.
  4. Hollway, W. (1989). Subjectivity and method in psychology. Sage.
  5. Patton, M. Q. (1990). Qualitative evaluation and research methods. Sage.
  6. Potter, J., & Wetherell, M. (1987). Discourse and social psychology. Sage.
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Ramji Acharya
Kathmandu University, School of Education

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