1. APPROACHES TO QUALITATIVE DATA ANALYSIS © LOUIS COHEN, LAWRENCE MANION & KEITH MORRISON

2. STRUCTURE OF THE CHAPTER Data analysis, thick description and reflexivity Ethics in qualitative data analysis Computer assisted qualitative data analysis (CAQDAS)

3. QUALITATIVE DATA There is no one single or correct way to analyze and present qualitative data –Abide by fitness for purpose. Qualitative data analysis is often heavy on interpretation, with multiple interpretations possible. Data analysis and interpretation may often merge. Data analysis often commences early. Results of the analysis also constitute data for further analysis.

4. TO TRANSCRIBE OR NOT TO TRANSCRIBE INTERVIEWS Transcriptions can provide important detail and an accurate verbatim record of the interview. Transcriptions may omit non-verbal aspects, and contextual features of the interview. Transcriptions are very time consuming to prepare. Transcriptions must clarify conventions used.

5. DATA ANALYSIS, THICK DESCRIPTION AND REFLEXIVITY Fitness for purpose: –To describe; –To portray; –to summarize; –to interpret; –to discover patterns; –to generate themes; –to understand individuals and idiographic features; –to understand groups and nomothetic features.

6. DATA ANALYSIS, THICK DESCRIPTION AND REFLEXIVITY Fitness for purpose: –to raise issues; –to prove or demonstrate; –to explain and seek causality; –to explore; –to test; –to discover commonalities, differences and similarities; –to examine the application and operation of the same issues in different contexts.

7. QUALITATIVE DATA ANALYSIS The movement is from description to explanation and theory generation. Problems of data overload: data reduction and display become important. Double hermeneutic: the researcher interprets and already-interpreted world. The researcher is part of the world that is being interpreted, therefore reflexivity is required. Subjectivity is inescapable. The researcher’s own memory may be fallible, selective and over-interpreting a situation.

8. QUALITATIVE DATA ANALYSIS Use a range of data and to ensure that these data include the views of other participants in a situation. Address reflexivity. The analysis becomes data in itself, for further analysis (e.g. for reflexivity).

9. RESPONDENT VALIDATION Respondent validation may be problematic as participants: –May change their minds as to what they wished to say, or meant, or meant to say but did not say, or wished to have included or made public; –May have faulty memories and recall events over- selectively, or incorrectly, or not at all; –May disagree with the researcher’s interpretations; –May wish to withdraw comments made in light of subsequent events in their lives; –May have said what they said in the heat of the moment or because of peer pressure or authority pressure; –May feel embarrassed by, or nervous about, what they said.

10. ETHICS IN QUALITATIVE DATA ANALYSIS Identifiability, confidentiality and privacy of individuals Non-maleficence, loyalties (and to whom), and beneficence

11. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) To make notes To transcribe field notes and audio data To manage and store data in an ordered and organized way For search and retrieval of text, data and categories To edit, extend or revise field notes To code and arrange codes into hierarchies (trees) and nodes (key codes) To conduct content analysis To store and check data To collate, segment and copy data

12. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) To enable memoing, with details of the circumstances in which the memos were written To attach identification labels to units of text To annotate and append text To partition data into units To sort, re-sort, collate, classify and reclassify pieces of data to facilitate constant comparison and to refine schemas of classification To assemble, re-assemble, recall data into categories To display data in different ways To undertake frequency counts

13. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) To cross-check data to see if they can be coded into more than one category, enabling linkages between categories and data to be found To establish the incidence of data that are contained in more than one category To search for pieces of data which appear in a certain sequence To filter, assemble and relate data according to preferred criteria To establish linkages between coding categories

14. COMPUTER ASSISTED QUALITATIVE DATA ANALYSIS (CAQDAS) To display relationships of categories To draw and verify conclusions and hypotheses To quote data in the final report To generate and test theory To communicate with other researchers or participants

15. TYPES OF CAQDAS SOFTWARE Those that act as word processors Those that code and retrieve text Those that manage text Those that enable theory building Those that enable conceptual networks to be plotted and visualized Those that work with text only Those that work with images, video and sound

16. SOFTWARE FUNCTIONS Search for and return text, codes, nodes and categories; Search for specific terms and codes, singly or in combination; Filter text; Return counts; Present the grouped data according to the selection criterion desired, both within and across texts;

17. SOFTWARE FUNCTIONS Perform the qualitative equivalent of statistical analyzes, such as: ─ Boolean searches ─ Proximity searches ─ Restrictions, trees, crosstabs Construct dendrograms of related nodes and codes; Present data in sequences and locate the text in surrounding material in order to provide the necessary context;

18. SOFTWARE FUNCTIONS Locate and return similar passages of text; Look for negative cases; Look for terms in context (lexical searching); Select text on combined criteria; Enable analyzes of similarities, differences and relationships between texts and passages of text; Annotate text and enable memos to be written about text.

19. CONCERNS ABOUT CAQDAS Researchers may feel distanced from their data Software is too strongly linked to grounded theory rather than other forms of qualitative data analysis Software is best suited to data which require coding and categorization for developing grounded theory Too heavy a focus on coding and retrieving Removes data from context The software drives the analysis rather than vice versa Relegates the real task of hermeneutic understanding