The author describes a general approach to the preparation
and planning of the data analysis section of a qualitative research proposal.
Diversity in qualitative analysis: multiple
approaches and methods and no one right way; but also some features common to
all methods; importance of the audit trail (how does researcher get from data
to conclusions?)
Analytic induction: uses induction to raise the level
of abstraction and to trace out relationships between concepts
Miles and Huberman: data
reduction, data display, drawing and verifying conclusions
- data
reduction – reduce data without significant loss of information
- data
display – use any type of diagram to display data as analysis proceeds
- drawing
conclusions – 13 tactics suggested (Appendix 1, p347)
- verifying
conclusions – 13 tactics suggested (Appendix 1, p347)
13
tactics suggested (Appendix 1, p347) that making conceptual/theoretical
coherence.
1.
noting patterns, themes
2.
seeing plausibility
3.
clustering
4.
making metaphors
5.
counting
6.
making contrasts/comparisons
7.
partitioning variables
8.
subsuming particulars into the general
9.
factoring
10. noting
relations between variables
11. finding
intervening variables
12. building
a logical chain of evidence
13. making
conceptual/theoretical coherence
13
tactics suggested (Appendix 1, p348) of getting feedback from informants.
1.
checking for representativeness
2.
checking for researcher effects
3.
triangulating
4.
weighting the evidence
5.
checking the meaning of outliers
6.
using extreme cases
7.
following up surprises
8.
looking for negative evidence
9.
making if-then tests
10. ruling
out spurious relations
11. replicating
a finding
12. checking
out rival explanations
13. getting
feedback from informants
Coding: assigning labels to pieces of data; different
types and levels of coding (first level descriptive, low inference; higher
levels analytic, finding patterns and/or conceptualization and/or interpreting)
Memoing: recording all ideas (substantive,
theoretical methodological, etc.) that occur during coding
Abstracting and comparing: two fundamental activities
in qualitative data analysis (abstracting – conceptualizing data at higher
levels; comparing – similarities and difference between pieces of data or
concepts)
Grounded theory analysis: open, axial, selective
coding; concept-indicator model (open coding – discovering abstract concept in
the data; axial coding – discovering connections between abstract concepts;
selective coding – raising the level of abstraction again to the core category)
Narratives and meaning: preserving storied character
of data; multiple methods of analysis; form and content
Ethnomethodology: how shared meanings and social
norms are developed, maintained; focus on everyday behaviour; central role of
language; conversation analysis
Discourse analysis: field of research with different
approaches; the nature of discourse; its structure and relationship to
hierarchies, power, ideology
Semiotics: the science of signs; language as a sign
system; how language produces meaning
Documentary analysis: social production of document;
social organization of document; analysis of meaning; applying different theoretical
perspectives
Computers: CAQDAS; choosing software for the analysis
of qualitative data
Ref: Keith Punch, Introduction to Methods in
Education, Chapter 9 The Analysis of Qualitative Data, Sage, 2009: p204
