
UNIVERSITY OF THE WEST OF ENGLAND
A thematic analysis of a set
of interview data and a
critical discussion of the
analysis and interpretation
Student Number : 02976321
Award : MSc Qualitative Methods in Psychology (Level M)
Module : Analysis and Interpretation of Interview Data (USP402HM)
Module Leader : Dr Kate Gleeson
Date : 25th August 2003
Word Count : 1999
A thematic analysis of a set of
interview data and a critical discussion
of the analysis and interpretation
Thematic analysis is historically a conventional practice in qualitative research which involves
searching through data to identify recurrent issues (Creswell, 1994, pp.153-155; Dey, 1993; Hayes,
2000, pp.173-179; Holliday, 2002, pp.98-122; Holloway, 1997, p.152; Miles & Huberman, 1994;
Ritchie et al., 2003, pp.219-262; Smith, et al., 1995, pp.9-26; Patton, 2002, pp.452-471). A theme (or
construct) is a cluster of linked categories conveying similar meanings. I performed an inductive
thematic analysis whereby themes emerged from data collected by another researcher’s semistructured interview. The exploratory power of this qualitative method was enhanced since I lacked
previous knowledge about the interview and the research agenda. However, revealing my personal
values and assumptions is crucial since qualitative research involves being a central channel for
analysis (see reflexive statement, appendix-i; Alvesson & Sköldberg, 2000; Creswell, 1994; Kopala &
Suzuki, 1999). In order to begin analysis a researcher must have at least some conceptual resource to
guide the processes of representation or interpretation, otherwise no sense at all could be made of any
data.
My analytic procedures (appendix-ii) which drew upon closely related techniques from
interpretative phenomenological analysis - IPA (Smith & Osborn, 2003; Willig, 2001, pp.50-69) and
grounded theory (Richardson, 1996, pp.75-101; Strauss & Corbin, 1998). However, I need to clearly
state my epistemological position as qualitative analyses vary considerably and often reflect
pragmatic conditions rather than philosophic principles. There is no simple distinction between
qualitative (naturalistic, contextual, idealist) and quantitative (experimental, positivist, realist)
methodologies (Hammersley, 1996; Creswell, 1994). Since researchers move back and forth between
abstract concepts and data all research involves processes of induction and deduction (Robson, 1993),
especially thematic analysis whereby induction creates themes and deduction authenticates them. My
epistemological approach aligned with inductive-realism. It was inductive because themes emerged
from the data and realist because my task as the analyst was to describe themes which exist
independently from my own interpretation. This differs to the positivist-realist approach involving
theory-led thematic analysis which tests hypotheses about textual data; comparable with analytic
induction which seeks to identify deterministic laws embedded within the essential nature of
phenomena. Research should clearly differentiate itself as primarily being explorative, generating
theoretical ideas or testing existing hypotheses. Because this exploratory analysis remained at the
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level of description rather than formulating any interpretations or explanations it is closer to
empiricism than constructionism.
I manually performed the analysis using basic data management facilities of a computer for
cutting, pasting and ordering information. This enabled easy transferral of original information into
this report in order to make my operations visible and assist reader evaluation. Although textual
investigations are aided by computer assisted qualitative data analysis software (CAQDAS) these are
largely architectural and the actual process of description, or interpretation, remains a human
endeavour (Dey, 1993; Willig, 2001, p.151; Kelle, 1995; Miles & Huberman, 1994; Weitzman, 2000).
Most CAQDAS offer the capacity for rapid searching and retrieval of data linked with the analyst’s
annotations or may even display conceptual hierarchies (e.g. Nudist, Codatext, Nvivo, Atlas, Qualpro,
Ethnograph; Scolari, 2003). They allow the researchers to store volumes of information in one place
without accumulating masses of written material.
The audiotaped interview data had already been transcribed into text by the original
researcher. However, it needed formatting for my own analysis and assigning line
numbers to identify individual databits (appendix-ii, stage 1). Eleven areas of interest arose
from my initial reading of the text which familiarised me with the interview’s material (appendix-ii,
stage 2.i). The data was then systematically reduced during open coding which
fragmented it into separate units for close examination and evaluation for similarities or
differences (appendix-i; appendix-ii, stage 2.ii; Strauss & Corbin, 1998, pp.101-121). At
this stage the participants information was summarised using line-by-line coding (or
indexing) which allowed individual bits of data to be closely identified and scrutinised.
This microanalytic process functioned to restrict my own interpretations by preventing
me from becoming immersed in the respondent’s worldview (Smith, et al., 1995, p.37).
Unlike performing an analysis on data collected for one’s own research this meant
covering the whole data to avoid rejecting any potentially irrelevant material. Whilst
intensely focused upon this process I made brief notes for the main issues without
letting these dominate the further decontextualisation of the data. This process of
reduction is a vital part of performing qualitative analysis so the source of themes
remain rooted in the data, preventing the participant’s reality disappearing by simply
overlaying themes from the researcher’s perspective (Ritchie et al., 2003, pp.237-248).
The IPA technique helped simultaneous deconstruction of both the data and the
dynamics between the interviewer and the participant. Data reduction established two
hundred and twenty four databits which I manually assigned to sixteen categories
(segmenting) by cutting and pasting on the computer (appendix-iii). This is a creative
yet rigorous process which fully demands the researcher’s analytic abilities so that each
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databit is given equal treatment. At this stage the sub-categories are loosely defined and
mundane category labels are recommended for general reference to empirical instances,
preferably using the participant’s own terms to ‘sensitize concepts’ (Blumer, 1969, cited
in Smith, 2003, p.85). Breaking the data down into small fragments means databits can
potentially relate to more than one category. However, this analysis involved a natural
assimilation of databits into sub-categories which was likely to be the result of such
microanalytic line-by-line fragmentation.
Themes began to emerge when databits relating to similar topics were placed
together in categories, whilst simultaneously moving between these categories and the
data (appendix-ii, stage 3; appendix-iii). This stage involved searching for positive or
negative comparisons between categories and databits to form clusters of initial protothemes. Re-ordering these clusters helped freely develop and modify the emerging
themes. I found that keeping proto-theme definitions as simple as possible at this stage
assisted the categorisation process but was very complicated because it involved being
fully immersed in the data. There had to be a balance between grouping information and
not creating too large a proto-theme to avoid collapsing categories to early. Assigning
identifiers to categories (i.e. C1, C2, etc.) and using line numbers helped track the
analysis because the names and definitions of emergent themes had a tendency to
transform throughout the analysis. Hence the need for a coherent coding system.
The next stage (appendix-ii, stage 4) involved the first process of trawling back through the data to
examine how information was assigned to each proto-theme and to evaluate their embryonic
meanings. A provisional name and flexible definition was created for each proto-theme. Whilst
themes evolve by the continual process of assembling and reassembling categories it is important to
have control over how separate databits relate to one or more of the categories. Up to this point
themes were primarily formed through inductive procedures but the next stage
(appendix-ii, stage 5) needed to verify their development via deductive procedures. This
involved axial coding which takes themes individually and searches through the original
text for related databits to check for deviant incidents or data inappropriately assigned
to a particular theme. This is closely associated with theory-led thematic analysis and
involves changing the epistemological position of the analyst (Strauss & Corbin, 1998,
pp.123-142). This recontextualisation is critical to the analytic process because human
perception is selective and may easily overlook relevant information or contradictory
incidents, particularly as themes evolve through very dynamic processes (Tesch, 1990).
Finally, ‘analyst constructed’ typologies were produced by assessing all the material
relating to each theme (Patton, 2002, p.458). Themes are brought alive with
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illustrations of the participant’s actual words in order to tangibly portray their
meanings to the reader (appendix-ii, stages 6 & 7; appendix-iv). The resulting themes
could be used for researcher triangulation to verify the authenticity of the original
researcher’s analyses (Bannister, et al., 1994, p.146).
Thematic analysis, IPA and grounded theory are methodologically similar analytic frameworks but
the manner in which themes, concepts and categories are epistemologically managed varies
considerably between these approaches (Spencer, et al., 2003; Ritchie, et al., 2003). They all attempt
to represent a view of reality via systematically working through text to identify topics that are
progressively integrated into higher order themes via processes of decontextualisation and
recontextualisation (Charmaz, 1995; Tesch, 1990; Willig, 1990). Their procedures are more
conceptually demanding than content analysis which employs a simpler empirical epistemology
(Wilson, 1995). However, content analysis and thematic analysis are often confused. The former
highlights criteria for the reliability and validity of the frequency of a theme or databit’s occurrence,
using predefined mutually exclusive and collectively exhaustive categories which are statistically
generalisable to outside factors such as gender (Bryman, 2001, pp.180-193). IPA is generally applied
to understanding individual cases whereas grounded theory was developed to understand social
processes. Both of these methods have the ability to draw upon numerous sources of data (Pidgeon,
1996). However, there are several versions of grounded theory, namely objective/realist and social
constructionist (Charmaz, 2000). Thematic analysis does not extend as far as grounded theory which
generates hypotheses from data sets and concerns issues of discovery. Thematic analysis and IPA are
epistemologically similar because, in general, researchers respectively produce descriptions and
interpretations of individual subjective experiences. The major difference occurs between their initial
stages of coding since IPA uses multiple re-readings to make wide-ranging and unfocused notes which
are quite different to the open coding strategy of thematic analysis and grounded theory.
Thematic analysis, IPA and grounded theory are ontologically similar because they treat language
transparently by simply taking for granted the meanings expressed in the data’s vocabulary. This
radically contrasts with discourse analysis (DA) and conversational analysis (CA) which promote the
constant deconstruction of accounts. DA and CA are part of the linguistic tradition whereas thematic
analysis grew out of the sociological tradition which treats text as a window into human experience
(Ryan & Bernard, 2000). The language-based analytic methods of DA and CA focus upon the
construction and structure of talk, text and interaction whereby meanings are conceptualised as being
obscured by rhetorical devices, linguistic styles or the interactive performance (Spencer, et al., 2003).
Consequently the system of transcription for DA and CA (depending on which method is used)
include phonetic, prosodic, paralinguistic and extralinguistic features of text and is exceedingly more
complex and time consuming than that required for thematic analysis. DA especially treats text as
having action orientations designed to achieve various socially interactive functions like questioning,
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agreement or justification (Cole, 1995). Empirical thematic analysis is not as explanatory and thus can
not comment upon the contextual function of themes. So generally, IPA and DA are easily designed
for interpretive and explanatory analyses whereas thematic analysis and CA remain purely descriptive
and are more suitable for exploratory investigations, from which point research questions and
hypotheses can be considered.
This reflects how qualitative methods can work from separate or integrated epistemological
perspectives and that this diversity means a particular study’s position needs identifying before we
can evaluate it (Willig, p.149). The mechanics of categorising data via qualitative thematic analysis
involves an vigorous process which spirals between the data, the analysis and the final account, as
opposed the distinctive linear processes in most quantitative research (Dey, 1993, pp.30-54). A well
structured system for effective data management is essential for the continual iterative movement
between the original data and any emergent themes throughout and after the analysis (Bryman, 2001;
Fielding, 1993; Wisker, 2001). Qualitative analysis requires a blend of systematic searching,
creativity, insightfulness and diligent detection for the development of themes, involving a dialogue
between analyst and data (Ritchie & Lewis, 2003). Several qualitative researchers may discover
different themes from a generic text, and often disagree with another’s classification of methods.
Consequently, it is crucial that analyses are transparently communicated for others to enter into
discussion, particularly if this involves research colleagues, supervisors or financers. The ultimate
purpose of conducting qualitative research is to recognise how the existence of themes and the
organisation of data are connected with a particular topic or the development of a line of reasoning.
Thematic analyses therefore shape, adjust or reform these objectives (Holliday, 2002, 98-122).
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