REAL WORLD RESEARCH THIRD EDITION Chapter 17: The Analysis and Interpretation of Qualitative Data 1©2011 John Wiley & Sons Ltd.

REAL WORLD RESEARCHTHIRD EDITION

Chapter 17:

The Analysis and

Interpretation of

Qualitative Data

1©2011 John Wiley & Sons Ltd.

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


Different approaches to qualitative analysis

1 Quasi-statistical approaches

2 Thematic coding approach

3 Grounded theory approach


Deficiencies of the human as analyst

1 Data overload2 First impressions3 Information availability4 Positive instance5 Internal consistency

continued …


Deficiencies of the human as analyst - continued

6 Uneven reliability7 Missing information8 Revision of hypotheses9 Fictional base10 Confidence in judgement11 Co-occurrence12 Inconsistency

(adapted and abridged from Sadler, 1981, pp. 27–30)


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 …


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)


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


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


Ways of keeping track of information

These include the use of:

• Session summary sheets• Document sheets• Memoing• The interim summary


Phases of thematic coding analysis

1 Familiarizing yourself with your data2 Generating initial codes3 Identifying themes4 Constructing thematic networks5 Integration and interpretation.


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 …12©2011 John Wiley & Sons Ltd.

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)


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

4 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)


What can you code?

1 Specific acts, behaviours 2 Events3 Activities 4 Strategies, practices or tactics5 States6 Meanings

continued …


What can you code? - continued

7 Participation8 Relationships or interaction9 Conditions or constraints10 Consequences 11 Settings

(based on Gibbs, 2007, Table 4.1. pp. 47–8)


Response after coding


Source: Gibbs, 2007

Techniques for identifying themes

1 Repetitions2 Indigenous categories3 Metaphors and analogies4 Transitions5 Similarities and differences6 Linguistic connectors7 Missing data8 Theory-related material

(summarized from Ryan and Bernard, 2003, pp. 89–94)


Example of a thematic network


Source: Goldbart and Marshall, 2004

Tactics to ‘generate meaning’

1 Noting patterns, themes and trends2 Seeing plausibility3 Clustering4 Making metaphors5 Counting6 Making contrasts and comparisons7 Partitioning variables

continued …


Tactics to ‘generate meaning’ - continued

8 Subsuming particulars into the general9 Factoring10 Noting relations between variables11 Finding intervening variables12 Building a logical chain of evidence13 Making conceptual/theoretical coherence

based on Miles and Huberman (1994, pp. 245–6)


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


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


Assessing the quality of qualitative data analysis

Assessing data quality

1 Checking for representativeness2 Checking for researcher effect3 Triangulation4 Weighting the evidence

continued …


Assessing the quality of qualitative data analysis - continued

Testing patterns

5 Checking the meaning of outliers6 Using extreme cases7 Following up surprises8 Looking for negative evidence

continued …


Assessing the quality of qualitative data analysis - continued

Testing explanations

9 Making if-then test10 Ruling out spurious relationships11 Replicating a finding12 Checking out rival explanations13 Getting feedback from informants

(summarized from Miles and Huberman, 1994, p. 262–77)


The three stages of grounded theory analysis

1 Find conceptual categories in the data2 Find relationships between these categories3 Conceptualize and account for these

relationships though finding core categories

continued …


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

3 Conceptualize and account for these relationships though finding core categories - through selective coding


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 …29©2011 John Wiley & Sons Ltd.

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)


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 …


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)


REAL WORLD RESEARCH THIRD EDITION Chapter 17: The Analysis and Interpretation of Qualitative Data 1©2011 John Wiley & Sons Ltd.

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