This document discusses qualitative data analysis methods for research. It begins by noting that the type of research problem and design determines the appropriate data analysis approach. Qualitative analysis focuses on words, meanings, and participant perspectives. Patterns are identified through frequencies, magnitudes, structures, processes, causes, and consequences. Two common analytic approaches are cross-case analysis, which allows broader understanding, and case-oriented analysis, which provides more individual understanding. Grounded theory is also discussed as an inductive process of discovery, categorization, and theory development through continuous data collection and analysis. Additional qualitative methods include semiotics and conversational analysis. Coding is an important part of qualitative data processing and involves open, axial, and selective coding to develop categories

1. Qualitative Data Analysis
Dr M Cronje
University of the Free State
Department of Criminology

2. Announcements
• Data collection tutorial activity due Friday
29 April @12:00
• Research design tutorial activity marks
and general feedback uploaded
• Writing and referencing workshop on the
29th of April during the tutorial time
• Write site consultation and extra marks
• Please submit all your assessments to
avoid getting an incomplete

3. Reflection on Week 8 Content

4. Week 9
Qualitative Data Analysis
Chapter 13

5. Introduction
• Your research problem determines the type of design (WHAT
do you want to find out and HOW do you want to do so?)
• Data analysis is also included under the research methodology
section of your proposal and is associated with the type of
research paradigm
• Important to remember (again)
– The nature of the DATA that you obtain determines if its a quantitative
or qualitative
– Numbers or specific responses (eg. yes/no OR likert scale responses) =
quantitative
– Non-numerical (eg. Voice recordings, written responses, literature,
social artefacts) = qualitative

6. Qualitative Approach Recap
• Aimed at describing and understanding (smaller
scale)
• Emphasis on words, meaning and interpretation of
the participants’ perspectives (Symbolic
interactionism)
• Context rich (engagement encouraged)

7. Qualitative Data Analysis
• Can be used for descriptive, exploratory and explanatory
purposes
• Explanation is achieved through pattern identification
– Frequencies
– Magnitudes
– Structures
– Processes
– Causes
– Consequences
• NB - the question that you want to answer will assist in
determining the analytic approach (ie. How the data will
be analysed, explained on the next few slides)

8. Pattern Identification
• Cross-case analysis
– Variable orientated analysis
• Obtaining partial understanding based on few, specific variables
and their association with a specific outcome
• Allows for broader understanding
• Reminder of nomothetic explanations (see week 4 lecture)
– Case orientated analysis
• In depth (multivariate) understanding of individual cases that
result in a specific outcome
• Allows for more individualistic understanding (multiple instances
of this approach can lead to better variable identification –
exploratory to explanatory)
• Reminder of idiographic explanations (see week 4 lecture)

9. Grounded Theory
• Inductive discovery of regularities
• Identification and categorisation of elements and
exploration of their connections
• Process of discovery, development and provisional
verification
• Data collection, analysis and theory development is a
continuous and reciprocal process
• Used to study phenomena that do not have existing
theoretical understanding (not testing but deveoping
theory)

11. Additional Qualitative Analytic Approaches
• Semiotics
– The study of signs and associated meanings
– Not limited to language only but can relate to various
other forms of communication and symbols
• Conversational Analysis
– In depth analysis of conversations including
• The socially constructed nature of conversations and
associated etiquette
• Understanding the context and associated meanings
• The structure and meaning of the conversations

12. Qualitative Data Processing
• Coding
– Importance of concepts (determines the complexity of
the associated coding)
– Open coding
• Initial creation of categories as well as the criteria for these and
further categories
• Determine the meaning and relevance of the content in relation to
your topic or research question
– Axial coding
• Making connections between the data
• What do the categories mean in relation to others
• Eg. Initial conditions, relation to the phenomenon, context in
which it occurs, consequences, etc.

13. – Selective coding
• Process of selecting a core category around which all
categories are integrated
• Lastly, tell the story.....
– Organise the categories into a relational framework
(how do they relate in terms of the core category?)
– Continue the data collection process to fill in the
details of the story and adjust where necessary using
valid evidence
– Validation of theory against data

14. Questions?