1. REAL WORLD RESEARCH THIRD EDITION
Chapter 17:
The Analysis and
Interpretation of
Qualitative Data
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2. Assumptions about qualitative data analysis
If you have a substantial amount of qualitative data you will use some kind of software package to deal with it (standard word-processing software may be adequate)
Unless you already have experience yourself, you will be helped or advised by someone who does have experience in this type of analysis
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3. Different approaches to qualitative analysis
1 Quasi-statistical approaches
2 Thematic coding approach
3 Grounded theory approach
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4. Deficiencies of the human as analyst
1 Data overload
2 First impressions
3 Information availability
4 Positive instance
5 Internal consistency
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5. Deficiencies of the human as analyst - continued
6 Uneven reliability
7 Missing information
8 Revision of hypotheses
9 Fictional base
10 Confidence in judgement
11 Co-occurrence
12 Inconsistency
(adapted and abridged from Sadler, 1981, pp. 27–30)
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6. Common features of qualitative data analysis
giving labels (‘codes’) to chunks (words, phrases, paragraphs, or whatever
adding comments, reflections, etc. (commonly referred to as ‘memos’)
going through the materials trying to identify similar phrases, patterns, themes, relationships, sequences, differences between subgroups, etc.
continued …
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7. Common features of qualitative data analysis - continued
using these patterns, themes, etc. to help focus further data collection
gradually elaborating a small set of generalizations that cover the consistencies you discern in the data, and
linking these generalizations to a formalized body of knowledge in the form of constructs or theories
(based on Miles and Huberman, 1994, p. 9)
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8. Advantages of specialist QDA packages
They provide an organized single location storage system for all stored material
They give quick and easy access to coded material without using ‘cut and paste’
They can handle large amounts of data very quickly
They force detailed consideration of all the text on a line-by-line basis
They help the development of consistent coding schemes
They can analyse differences, similarities and relationships between coded elements
Many have a range of ways of displaying results
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9. Disadvantages of specialist QDA packages
Proficiency in their use takes time and effort
There may be difficulties in changing, or reluctance to change, categories of information once they have been established
Particular programs tend to impose specific approaches to data analysis
Tendency to think that simply because you have used specialist software you have carried out a worthwhile analysis
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10. Ways of keeping track of information
These include the use of:
Session summary sheets
Document sheets
Memoing
The interim summary
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11. Phases of thematic coding analysis
1 Familiarizing yourself with your data
2 Generating initial codes
3 Identifying themes
4 Constructing thematic networks
5 Integration and interpretation.
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12. Advantages of thematic coding analysis
1 Very flexible, can be used with virtually all types of qualitative data
2 A relatively easy and quick method to learn and use
3 Accessible to those with little experience of qualitative research
4 The results of the analysis can be communicated without major difficulties to practitioners, policy makers and an educated general public
continued …
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13. Advantages of thematic coding analysis - continued
5 A useful method to employ when working within a participatory research paradigm
6 Provides a means of summarizing key features of large amounts of qualitative data, using a principled approach
7 Not tied to a particular level of interpretation and can be used in a wide variety of fields and disciplines
(based, in part, on Braun and Clarke, 2006, pp. 96–7)
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14. Disadvantages of thematic coding analysis
1 The flexibility of the method means that the potential range of things that can be said about your data is broad, which can be inhibiting
2 It is frequently limited to description or exploration with little interpretation
3 It is not uncommon to find little information about details of the procedure
Compared to ‘branded’ forms of analysis such as grounded theory it is a generic approach which currently has less kudos as an analytic method
(based, in part, on Braun and Clarke, 2006, pp. 96–7)
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15. What can you code? 1 Specific acts, behaviours 2 Events 3 Activities
4 Strategies, practices or tactics
5 States
6 Meanings
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16. What can you code? - continued
7 Participation
8 Relationships or interaction
9 Conditions or constraints
10 Consequences
11 Settings
(based on Gibbs, 2007, Table 4.1. pp. 47–8)
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17. Response after coding
Source: Gibbs, 2007
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18. Techniques for identifying themes
1 Repetitions
2 Indigenous categories
3 Metaphors and analogies
4 Transitions
5 Similarities and differences
6 Linguistic connectors
7 Missing data
8 Theory-related material
(summarized from Ryan and Bernard, 2003, pp. 89–94)
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19. Example of a thematic network
Source: Goldbart and Marshall, 2004
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20. Tactics to ‘generate meaning’
1 Noting patterns, themes and trends
2 Seeing plausibility
3 Clustering
4 Making metaphors
5 Counting
6 Making contrasts and comparisons
7 Partitioning variables
continued …
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21. Tactics to ‘generate meaning’ - continued
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
based on Miles and Huberman (1994, pp. 245–6)
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22. Using tables for comparative analysis
Time-ordered tables where the columns are arranged in time sequence - includes event listing
Role-ordered tables where the rows represent data from sets of individuals occupying different roles
Conceptually clustered tables where the columns are arranged to being together items ‘belonging together’ (e.g. relating to same theme)
Effects tables displaying data on outcomes
Issues tables where the columns concern issues and what happens in connection with them
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23. Using networks to understand patterns and relationships
Types include:
Context charts
Event flow networks
Activity records
Flow charts
Conceptually ordered tree diagrams
Cognitive maps
Causal networks
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24. Assessing the quality of qualitative data analysis
Assessing data quality
1 Checking for representativeness
2 Checking for researcher effect
3 Triangulation
4 Weighting the evidence
continued …
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25. Assessing the quality of qualitative data analysis - continued
Testing patterns
5 Checking the meaning of outliers
6 Using extreme cases
7 Following up surprises
8 Looking for negative evidence
continued …
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26. Assessing the quality of qualitative data analysis - continued
Testing explanations
9 Making if-then test
10 Ruling out spurious relationships
11 Replicating a finding
12 Checking out rival explanations
13 Getting feedback from informants
(summarized from Miles and Huberman, 1994, p. 262–77)
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27. The three stages of grounded theory analysis
1 Find conceptual categories in the data
2 Find relationships between these categories
Conceptualize and account for these relationships though finding core categories
continued …
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28. The three stages of grounded theory analysis - continued
1 Find conceptual categories in the data - through open coding
2 Find relationships between these categories - through axial coding
Conceptualize and account for these relationships though finding core categories - through selective coding
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29. Data analysis in multi-strategy designs
1 Data reduction. Involves summarizing both quantitative and qualitative data
2 Data display. Using tables, graphs, etc. with quantitative data and matrices, charts, networks, etc. with qualitative data
3 Data transformation. ‘Qualitizing’ quantitative data and/or ‘quantizing’ qualitative data
4 Data correlation. Correlating quantitative data with qualitized data
continued …
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30. Data analysis in multi-strategy designs - continued
5 Data consolidation. Combining both data types to create new variables or data sets.
6 Data comparison. Comparing data from different data sources.
7 Data integration. Integrating all data into a coherent whole, or separate quantitative and qualitative coherent wholes
Based on Onwuegbuzie and Teddlie (2003, p. 375)
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31. Strategies for integrating quantitative and qualitative data through analysis
Using results from analysis of one form of data in approaching the analysis of another form of data
Synthesis of data generated from a variety of sources, for further joint interpretation
Comparison of coded or thematic qualitative data across groups defined by categorical or scaled variables
Pattern analysis using matrices
continued …
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32. Strategies for integrating quantitative and qualitative data through analysis - continued
Conversion of qualitative to quantitative coding to allow for statistical analysis.
Conversion of quantitative data into narrative form
Inherently mixed data analysis, where a single source gives rise to both qualitative and quantitative information
Iterative analyses involving multiple, sequenced phases where the conduct of each phase arises out of or draws on the analysis of the preceding phase.
(based on Bazeley, 2009, p. 205)
©2011 John Wiley & Sons Ltd.