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Chapter 14 - Analyzing data II: Qualitative data analysis
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Topics covered in the chapter
The basic principles of qualitative data analysis.
The concepts of ‘codes’ and ‘themes’.
The different types of coding that form the framework for qualitative analysis.
The ways by which you can graphically represent your qualitative analysis.
A discussion of some ways by which you can maximize the trustworthiness of your interpretations.
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Stages of qualitative analysis
Data familiarization – getting to know your data ‘inside out’, and beginning the analytical process.
Data reduction - discarding all irrelevant information.
Data organization - firstly through coding, and subsequently through developing broader themes to make sense of the data.
Data display - in the form of tables, charts, networks and other graphical formats.
Conclusion drawing/verification.
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Developing Codes and Themes
Coding is the organization of your raw data into ‘conceptual categories’.
Each code refers to a particular meaning of the data.
Data that has such meaning is organized into that relevant code.
Codes can be grouped together under broader themes.
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An example of organized data
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Using direct quotes
When they describe a phenomenon particularly well.
To show cases or instances that are unusual.
To show data that is unexpected.
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Maximising the quality of your analysis
Member checking.
Searching for negative cases and alternative explanations.
Triangulation.
Having an ‘audit trail’.
Reflexivity - The practice of examining one's own assumptions, beliefs, and judgments, and how they influence the research process.
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Writing up your qualitative analysis
Ensure that your written account is:
Contextually complete –making full reference to the context within which the data exists through rich description of the setting, identification of any idiosyncratic features and so on.
Tells a ‘good story’ to the reader.
Provides ‘verisimilitude’ – Does it provide the reader with a sense of almost ‘being there’, and give them a feeling of understanding of the culture?
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Summary
There are a number of ways of approaching qualitative data. Analyzing qualitative data should be an on-going process throughout, as well as after the collection of data.
There are several key stages to qualitative data analysis: data reduction, coding, data display and conclusion drawing/verification.
Coding involves assigning units of meaning to data chunks. These codes can then be displayed or organized to allow the drawing of conclusions.
There are a number of means by which you can maximize the trustworthiness of your data, these being member validation, searching for negative cases and alternative explanations, triangulation, having an audit trail, and reflexivity.
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