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

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

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.

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

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

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.

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.

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

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

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.

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

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.

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

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

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

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.

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

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