1. ORGANIZING AND PRESENTING QUALITATIVE DATA
© LOUIS COHEN, LAWRENCE MANION AND KEITH MORRISON

2. STRUCTURE OF THE CHAPTER
Tabulating data
Ten ways of organizing and presenting data analysis
Narrative and biographical approaches to data analysis
Systematic approaches to data analysis
Methodological tools for analysing qualitative data

3. TABULATING DATA (Accompanying table)
Key: P1 = Primary 1, P6 = Primary 6, F3 = Secondary Form 3, F5 = Secondary Form 5.
The left-hand column indicates the number of the respondent (1–12) and the level which the respondent taught (e.g. P1, F3 etc.).
Where data for respondents in each age phase are similar they are grouped into a single set of responses by row; where there are dissimilar responses they are kept separate.
The right-hand column indicates the responses. In many cases respondents all gave similar responses in terms of the topic (strengths and weaknesses of English-language teaching); these are grouped together.

4. Q7: Strengths and weaknesses of English-language teaching
1: P1
Students started learning English at a very young age and they should be good at it. However, this could also be a disadvantage as students were too young to learn English and to understand what they were taught.
2–6: P6
These respondents all commented that individual schools had great autonomy over syllabus design. Consequently, some syllabus contents were too rich to be covered within the limited timespan. Therefore, it was hard to make adjustments, though students could not cope with the learning requirements. This put pressure on both teachers and students. Worse still, some schools made students learn other foreign languages apart from English, and that made the learning of English more difficult.
7–9: F3
10–12: F5

5. TEN WAYS OF ORGANIZING AND PRESENTING DATA ANALYSIS
By groups of people
By individuals
By issue or theme
By research question
By instrument
By case studies
By narrative account
By events
By time sequence/timeframe
By theoretical perspectives

6. NARRATIVE APPROACHES TO DATA ANALYSIS
Humans make meaning and think in terms of ‘storied text’, which guides their actions.
Narrative analysis, together with biographical data, can give the added dimension of realism, authenticity, humanity, personality, emotions, views and values in a situation.

7. NARRATIVE APPROACHES TO DATA ANALYSIS
Narratives pass on information.
Narratives bring information to life.
Narratives meet people’s psychological needs in coping with life.
Narratives help a group to crystallize or define an issue, view, value or perspective.
Narratives can persuade or create a positive image.
Narratives help researchers and readers to understand the experiences of participants and cultures.
Narratives contribute to the structuring of identity.

8. CONSTRUCTING THE FINAL NARRATIVE
By temporal sequence (a chronology).
By a sequence of causal relations.
By key participants.
By key actions.
By emergent or key themes.
By key issues and clusters of issues.
By biographies of the participants.
By critical or key events.
By turning points in a life history or biography.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

9. CONSTRUCTING THE FINAL NARRATIVE
By different perspectives.
By key decision points.
By key behaviours.
By individual case studies or a collective analysis of the unfolding of events for many cases/participants over time.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

10. BIOGRAPHICAL APPROACHES TO DATA ANALYSIS
Biographies tend to follow a chronology.
Biographies report critical or key events and moments.
Biographies report key decisions and people.
Biographies can establish causality.
Biographies can restore broken identities or shattered futures.

11. NARRATIVE AND BIOGRAPHICAL APPROACHES TO DATA ANALYSIS
Narratives and biographies are selective, based on:
Key decision points in the story or narrative
Key, critical (or meaningful to the participants) events
Themes
Behaviours
Actions
People
Key experiences
Key places

12. SYSTEMATIC APPROACHES TO DATA ANALYSIS
Comparing different groups simultaneously and over time.
Matching the responses given in interviews to observed behaviour.
Analysing deviant and negative cases.
Calculating frequencies of occurrences and responses.
Assembling and providing sufficient data that keeps separate raw data from analysis.

13. SELECTIVITY IN QUALITATIVE ANALYSIS OCCURS BECAUSE OF . . .
Data overload
First impressions
Availability of people
Information availability
Positive instances
Internal consistency
Uneven reliability
Missing data
Revision of hypotheses
Confidence in judgement
Co-occurrence (may be mistaken for association)
Inconsistency

14. METHODOLOGICAL TOOLS FOR ANALYSING QUALITATIVE DATA
Analytic induction
Constant comparison
Typological analysis
Enumeration
Coding
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

15. STAGES IN ANALYSIS 1 2 3 4 Generating natural units of meaning
Classifying, categorizing and ordering these units of meaning 3
Structuring narratives to describe the contents 4
Interpreting the data

16. TWELVE TACTICS IN ANALYSIS
Counting frequencies of occurrence.
Noting patterns and themes.
Seeing plausibility.
Clustering.
Making metaphors.
Splitting variables.
Subsuming particulars into the general.
Factoring.
Identifying and noting relations between variables.
Finding intervening variables.
Building a logical chain of evidence.
Making conceptual/theoretical coherence.

17. CONTENT ANALYSIS Briefing Sampling Associating Hypothesis development
Hypothesis testing
Immersion in the data
Categorizing
Incubation
Synthesis
Culling
Interpretation
Writing
Rethinking

18. METHODOLOGICAL TOOLS FOR ANALYSING QUALITATIVE DATA
Analytic induction
Constant comparison
Typological analysis
Enumeration