Data analysis in qualitative research

By S. Thorne. 2000

1,605 citations recorded by the source corpus.

Unquestionably, data analysis is the most complex and mysterious of all of the phases of a qualitative project, and the one that receives the least thoughtful discussion in the literature. For neophyte nurse researchers, many of the data collection strategies involved in a qualitative project may feel familiar and comfortable. After all, nurses have always based their clinical practice on learning as much as possible about the people they work with, and detecting commonalities and variations among and between them in order to provide individualised care. However, creating a database is not sufficient to conduct a qualitative study. In order to generate findings that transform raw data into new knowledge, a qualitative researcher must engage in active and demanding analytic processes throughout all phases of the research. Understanding these processes is therefore an important aspect not only of doing qualitative research, but also of reading, understanding, and interpreting it. For readers of qualitative studies, the language of analysis can be confusing. It is sometimes difficult to know what the researchers actually did during this phase and to understand how their findings evolved out of the data that were collected or constructed. Furthermore, in describing their processes, some authors use language that accentuates this sense of mystery and magic. For example, they may claim that their conceptual categories “emerged” from the data1—almost as if they left the raw data out overnight and awoke to find that the data analysis fairies had organised the data into a coherent new structure that explained everything! In this EBN notebook, I will try to help readers make sense of some of the assertions that are made about qualitative data analysis so that they can develop a critical eye for when an analytical claim is convincing and when it is not. Qualitative data come …

Subfields: Education, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/72e3714314e16c563c301c7a827e94eaa1e497d8

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Citations in the corpus

Thematic analysis of qualitative data: AMEE Guide No. 131 (2020, methodology): “Depending on the study, the data set might include interviews, focus groups, recorded observations, field notes, journal entries, or other media such as photographs or videos (Thorne 2000; Nowell et al. 2017).”
Instructional leadership: the impact on the culture of teaching and learning in two effective secondary schools (2003, methodology): “Data obtained from the different interviews were analysed using Tesch’s approach (Tesch, 1990:89-91, Poggenpoel, 1998:334).”
Inductive and Deductive: Ambiguous Labels in Qualitative Content Analysis (2018, background): “Moreover, as the analysis progresses, new categories will emerge inductively, making tentative hypotheses (Thorne, 2000; Bernard, 2011).”
The Analytic Challenge in Interpretive Description (2004, background): “Within the body of qualitative methodological literature, there exist a wide range of “recipes” to guide the mechanics of the analytic process (Thorne, 2000).”
Doctoral supervision in a cross-cultural context: issues affecting supervisors and candidates (2014, methodology): “As themes emerged from the data, they were sorted into categories, using a constant comparative approach (Thorne, 2000).”
Characteristics of Qualitative Descriptive Studies: A Systematic Review (2016, methodology): “…technique in addition to qualitative content analysis or thematic analysis in QD studies, researchers compare each case with others, looking for similarities and differences and exploring why differences exist to generate a more general understanding of phenomena of interest (Thorne, 2000).”