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Iterative categorization (IC): a systematic technique for analysing qualitative data
Author(s) -
Neale Joanne
Publication year - 2016
Publication title -
addiction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.424
H-Index - 193
eISSN - 1360-0443
pISSN - 0965-2140
DOI - 10.1111/add.13314
Subject(s) - categorization , computer science , coding (social sciences) , thematic analysis , qualitative research , natural language processing , qualitative analysis , data science , iterative and incremental development , narrative , information retrieval , data mining , artificial intelligence , linguistics , mathematics , software engineering , statistics , philosophy , social science , sociology
The processes of analysing qualitative data, particularly the stage between coding and publication, are often vague and/or poorly explained within addiction science and research more broadly. A simple but rigorous and transparent technique for analysing qualitative textual data, developed within the field of addiction, is described. The technique, iterative categorization (IC), is suitable for use with inductive and deductive codes and can support a range of common analytical approaches, e.g. thematic analysis, Framework, constant comparison, analytical induction, content analysis, conversational analysis, discourse analysis, interpretative phenomenological analysis and narrative analysis. Once the data have been coded, the only software required is a standard word processing package. Worked examples are provided.