Working Papers www.mmg.mpg.de/workingpapers MMG Working Paper 18-02 ● ISSN 2192-2357 SUSANNE FRIESE, JACKS SORATTO, DENISE PIRES Carrying out a computer-aided thematic content analysis with ATLAS.ti Max Planck Institute for the Study of Religious and Ethnic Diversity Max-Planck-Institut zur Erforschung multireligiöser und multiethnischer Gesellschaften
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Working Paperswww.mmg.mpg.de/workingpapers
MMG Working Paper 18-02 ● ISSN 2192-2357
SuSanne FrieSe, JackS Soratto, DeniSe PireS
Carrying out a computer-aided thematic content analysis with ATLAS.ti
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Susanne Friese, Jacks Soratto, Denise Pires Carrying out a computer-aided thematic content analysis with ATLAS.ti
MMG Working Paper 18-02
Max-Planck-Institut zur Erforschung multireligiöser und multiethnischer Gesellschaften, Max Planck Institute for the Study of Religious and Ethnic DiversityGöttingen
Working Papers are the work of staff members as well as visitors to the Institute’s events. The analyses and opinions presented in the papers do not reflect those of the Institute but are those of the author alone.
Download: www.mmg.mpg.de/workingpapers
MPI zur Erforschung multireligiöser und multiethnischer GesellschaftenMPI for the Study of Religious and Ethnic Diversity, GöttingenHermann-Föge-Weg 11, 37073 Göttingen, GermanyTel.: +49 (551) 4956 - 0Fax: +49 (551) 4956 - 170
Thematic analysis, similar to content analysis, is an umbrella term for analyzing
qualitative data. Braun and Clarke (2006: 79) describe thematic content analysis as
a method for “identifying, analyzing, and reporting patterns (themes) within data.
It minimally organizes and describes your data set in (rich) detail. However, fre-
quently it goes further than this, and interprets various aspects of the research topic.”
Brown and Clarke suggest that thematic analysis should be a foundational method
for qualitative analysis as “thematizing meanings” is one of a few shared generic
skills across qualitative analysis and it can be applied across a range of theoretical
and epistemological approaches.
Looking for patterns in the data is also common in other methodological
approaches, but often these methods are theoretically bounded like interpretive phe-
nomenological analysis to phenomenology (Smith and Osborn 2003) or the various
forms of grounded theory to theoretical positions like objectivism, constructivism,
symbolic interactionism, feminism, or Foucault’s discursive approach (Glaser 1998;
Corbin and Strausss 2015; Charmaz 2014; Clarke 2015).
Thematic analysis can be used within different theoretical frameworks in a deduc-
tive “top down” way (e.g., Boyatzis 1998; Hayes 1997), or in an inductive “bottom up”
way (e.g., Frith and Gleeson 2004). As Priya and Dalal (2015: 212) put it: “a method
which works both to reflect reality, and to unpick or unravel the surface of ‘reality’.”
As any theoretical framework implies several assumptions about the nature of reality
and the data that stand for this reality, a good thematic analysis, according to Brown
and Clarke (2006) will always make this transparent.
What is a Theme?
According to Brown and Clarke (2006: 82), “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.” It is an outcome of coding,
but is not represented by a single code directly. Codes help in organizing, structur-
ing, and retrieving data and they support the identification of themes, but rarely is
a code also a theme. Prevalence in the data, i.e. frequency of occurrence within or
across document is not necessarily a criterion for developing a theme. Essential is the
researcher’s assessment of whether or not the theme captures something important
in relation to the overall research question. This is guided by the overall aim of the
analysis, which in turn influences the level at which themes are identified. Should
the analysis result in a rich thematic description of the data set, or should it give a
detailed account of one aspect? In the first case, themes are extracted at the semantic
or explicit level, in the latter case, at a latent or interpretative level (Boyatzis 1998).
Semantic or latent level of analysis
A thematic analysis can be conducted at the semantic or at the latent level. Semantic
analysis means that data is coded at face value, i.e. at the explicit or surface meaning.
The analyst is not looking for anything beyond what has been said or written. This
applies to the identified themes as well. According to Brown and Clarke (2006: 13),
ideally, the analytic process involves a progression from description, where the data have simply been organized to show patterns in semantic content, and summarized, to inter-pretation, where there is an attempt to theorize the significance of the patterns and their broader meanings and implications (Patton 1990), often in relation to previous literature (see Frith and Gleeson 2004, for an excellent example of this).
In comparison, a latent level analysis goes beyond the semantic content of the data.
It starts with examining the underlying meanings, assumptions, and conceptualiza-
tions that inform the semantic content of the data. In addition to applying codes to
the data, the researcher is writing detailed analytic memos. Thus, the development
of the themes involves interpretative work and the results are not mere description
but are already theorized. Analysis within this latter tradition tends to come from
a constructionist paradigm (e.g., Burr 1995), fits well with some discourse analytic
approaches like Taylor and Ussher (2001), and is overall more suitable when you
are theoretically rooted within the interpretive paradigm (Denzin and Lincoln 1994;
Keller 2012).
Inductive or Deductive Analysis
Depending on which type of thematic content analysis you use, data can be analyzed
inductively or theory-based in a top-down deductive fashion. Inductive analysis is
data driven, and the analysist is not trying to fit the data into a pre-existing coding
frame. The codes do not map the questions asked to the participants, they reflect the
responses given by the research participants; the identified themes are derived from
the data. This, however, does not mean that data are coded in an epistemological
vacuum. As discussed earlier, the researchers need to clarify their underlying theoret-
ical and epistemological point of view. Brown and Clarke’s thematic content analysis
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