Analysis of Data Qualitative Data Analysis

1. Analysis of DataQualitative Data Analysis Babbie & Mouton, 2005. The Practice of Social Research. Cape Town: Oxford

2. Qualitative Data Analysis • See diagram of Renate Tesch p.490 • NB is Tesch’s book – Qualitative research: Analysis types & software tools (1990) • More than twenty approaches to qualitative data • Following three is most common research interests: • Characteristics of language • Discovery of irregularities • Comprehension of meaning of text/action

3. Characteristics of Language Two analytical approaches: • Content Analysis • Focuses on content characteristics • Very much like quantitative analysis – useful in market research • Divided into two types • Conceptual/Thematic analysis – Comprises eight steps • Relational analysis (Relationship between elements in data) – Comprises various steps • Discourse Analysis • Focuses on process characteristics • Complex process & difficult to learn • Parker proposes seven criteria for distinguishing discourses: • Realizes in texts • Is about objects • Contains subjects • Is coherent set of meanings • Refers to other discourses • Reflects its own way of speaking • Is historically located

4. Content Analysis • Conceptual/Thematic analysis – Comprises eight steps: • Deciding the level of analysis (one word or a phrase) • How many concepts to code for • Whether to code for existence/frequency of a concept • How to distinguish among concepts • Developing rules for the coding of text • What to do with irrelevant information • Coding texts (making sense of the patterns & themes of the text • Analyzing results

5. Content Analysis • Relational analysis (Relationship between elements in data) – Comprises various steps such as: • Id the question • Choose the sample/s for analysis • Determine the type of analysis • Reduce text to categories & code for words or patterns • Explore the strength, sign & direction of relationships • Code the relationships • Possibly perform statistical analysis • Map out the representations

6. Research Interest: Discovery of Regularities • Grounded Theory: Discovery of regularities as the id of categories of elements & establishment of their connections • Inductively derived –discovered, developed & provisionally verified through systematic data collection & analysis of data pertaining to the research • Begins with an area of study & what is relevant to that area is allowed to emerge • An approach that allows us to study relatively unknown social phenomenon around which no theory exist • Two main processes to grounded theory analysis: • Coding procedures – open, axial & selective coding • Adjunctive procedures – e.g. memos

7. Computer-aided Tools for Qualitative Data Analysis (CAQDAS) • Following uses of a combination of various CAQDAS packages: • Making notes in the filed & Writing up/transcribing field notes • Editing: correcting, extending/revising field notes • Coding: attaching key words/tags to segments of text to permit later retrieval • Storage: keeping texts in an organized database • Search & retrieval: locating relevant segments of text & making it available for inspection • Data “linking”: connecting relevant data segments with each other, forming clusters/networks of info • Meaning: writing reflective commentaries on aspects of data for deeper analysis • Content analysis: counting frequencies, sequence/locations of words & phrases • Data display: placing selected/reduced data in condensed, organized format e.g. matrix for inspection • Conclusion drawing & verification: aiding the analyst to interpret displayed data & test/confirm findings • Theory building: developing systematic conceptually coherent explanations of findings, creating diagrams/theories • Preparing interim & final reports