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ORGANIZING AND PRESENTING QUALITATIVE DATA

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Presentation on theme: "ORGANIZING AND PRESENTING QUALITATIVE DATA"— Presentation transcript:

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

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

3 TABULATING DATA (Accompanying table)
Key: P1 = Primary 1, P 6 = 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 to 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 time span. 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 SEVEN 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

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

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

9 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

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

11 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

12 STAGES IN ANALYSIS Generating natural units of meaning
Classifying, categorizing and ordering these units of meaning Structuring narratives to describe the contents Interpreting the data

13 TWELVE TACTICS IN ANALYSIS
Counting frequencies of occurrence Noting patterns and themes Seeing plausibility Clustering Making metaphors Splitting variables

14 TWELVE TACTICS IN ANALYSIS
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

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

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


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