1. APPROACHES TO QUALITATIVE DATA ANALYSIS
© LOUIS COHEN, LAWRENCE MANION AND KEITH MORRISON
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

2. STRUCTURE OF THE CHAPTER
Elements of qualitative data analysis
Qualitative data analysis, thick description and reflexivity
Ethics in qualitative data analysis
Computer assisted qualitative data analysis (CAQDAS)
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

3. QUALITATIVE DATA
There is no one single or correct way to analyse and present qualitative data, so adopt 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

4. QUALITATIVE DATA ANALYSIS
Qualitative data analysis involved data reduction and data display.
Qualitative data analysis is an inductive process.
Preparing and organizing the data: transcription and summary.
Managing data files and types.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

5. ELEMENTS OF QUALITATIVE DATA ANALYSIS
Preparing and organizing the data.
Describing and presenting the data.
Analysing the data.
Interpreting the data.
Drawing conclusions.
Reporting the findings.
Ensuring accuracy, reliability, coherence, corroboration, validity and reliability.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

6. PRE-ORDINATE AND RESPONSIVE ANALYSIS
A priori: ideas, themes, codes, key points, analytical framework etc. decided in advance
Responsive
A posteriori: responding to the emerging data and what they reveal
A combination of pre‑ordinate and responsive categories, codes, themes, ideas, topics, concepts etc.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

7. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

8. 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
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

9. DATA ANALYSIS, THICK DESCRIPTION AND REFLEXIVITY
Fitness for purpose:
To raise issues
To prove or demonstrate
To explain
To seek causality
To explore
To test
To discover commonalities, differences, similarities
To examine the application and operation of the same issues in different contexts
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

10. 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 an 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

11. QUALITATIVE DATA ANALYSIS
Use a range of data and 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).
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

12. RESPONDENT VALIDATION
Respondent validation may be problematic as participants:
might 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;
might have faulty memories and recall events over-selectively, or incorrectly, or not at all;
might disagree with the researcher’s interpretations;
might wish to withdraw comments made in light of subsequent events in their lives;
might have said what they said in the heat of the moment or because of peer pressure or authority pressure;
might feel embarrassed by, or nervous about, what they said.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

13. ETHICS IN QUALITATIVE DATA ANALYSIS
Do no harm.
Identifiability, confidentiality and privacy of individuals.
Non-maleficence, loyalties (and to whom) and beneficence.
Consideration of the consequences of the research and its publication.
Research integrity.
Ownership of the data and how it may be used, informed consent and disclosure.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

14. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

15. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

16. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

17. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

18. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

19. SOFTWARE FUNCTIONS
To organize, manage, store and index data and files systematically.
To store data, notes and searches.
To search and interrogate data and text.
To make notes and edit, extend or revise them.
To transcribe and annotate field notes and audio and visual data.
To search and retrieve data from individual files or across data files, codes, notes, memos.
To display data in different ways and to create visual data modelling and graphics.
To display relationships of categories (e.g. hierarchical, temporal, relational, subsumptive, superordinate).
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

20. SOFTWARE FUNCTIONS To establish linkages between coding categories.
To cross-check to see if data can be coded into more than one category.
To code data.
To arrange codes into hierarchies (trees) and nodes (key codes).
To facilitate content analysis (e.g. frequencies of words, meanings, issues, themes, concepts, sequences, locations, people, etc.).
To check data (e.g. proofread).
To collate and segment data and make numerous copies of data.
To enable memoing.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

21. SOFTWARE FUNCTIONS
To conduct a search for words or phrases in the data and to retrieve text.
To annotate and append text to written, audio, graphic, image-based and visual data.
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 code memos and bring them into classification schema.
To assemble, re-assemble and recall data into categories.
To undertake frequency counts (e.g. of words, phrases, codes).
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

22. SOFTWARE FUNCTIONS
To establish the incidence of data that are contained in more than one category.
To retrieve coded and noded data segments.
To search for pieces of data which appear in a certain (e.g. chronological) sequence.
To filter, assemble and relate data according to preferred criteria (e.g. words, codes, themes, issues, nodes).
To link to external sources of data (e.g. Internet sites).
To draw conclusions and to verify conclusions and hypotheses.
To quote data in the final report.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

23. SOFTWARE FUNCTIONS To generate and test theory.
To export data into other formats/software.
To communicate with other researchers or participants.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

24. SOFTWARE FUNCTIONS
Perform the qualitative equivalent of statistical analyses, 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

25. 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 analyses of similarities, differences and relationships between texts and passages of text.
Annotate text and enable memos to be written about text.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

26. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors