1. More on Qualitative Data Collection and Data Analysis

2. Forms of Data
John Creswell (1998) notes there are four basic types of data that may be collected, depending on the methodology used:
Observations
Interviews
Documents
Audio-visual materials

3. Main Types of Qualitative Notes
Field Notes (to record all your observations)
running account of what happens or transcriptions/observations of videos
important to be thorough in taking field notes, particularly at the earliest phases of research
Personal Notes (Personal Diary)
personal reactions, how you feel, self-reflection, memories, and impressions
like a diary, so you can later see your own influence on the data and the effects of personal events on the data collection
Methodology Notes
Description of methods used, reasons for using those methods, ideas for possible changes
used for keeping track of changes and rationale for changes
can include methods of analysis.
Theoretical Notes (Analytic Memo)
emergent trends, hypotheses
can include guesses and hunches to follow up later in your research.
also tp describe changes made to emergent categories and hypotheses, and the reasons why those changes were made

4. What is content analysis?
Berg (2009) calls it a “careful, detailed, systematic examination” of the data gathered through your observations or interviews, or sources like documents, archives, diaries, etc.

5. Approaches to the Analysis
Interpretative Approaches
Treat social action and human activity as text
Social Anthropological Approaches
Analysis of field notes and other data
Collaborative Social Research Approaches
Work with stakeholders

6. Content Analysis Systematic and objective Manifest Content
physically present and countable elements (what is actually seen)
Latent Content
interpretive reading of underlying meaning and semantics (semiotic)

7. Communication Components
Sender –– message –– audience
Who is the sender?
What is the message? – theme, emphasis, intent
What group is the message directed at?
In Vivo Codes
literal terms used by individuals under investigation
represents behavioral process
Sociological Constructs
Concepts formulated by the analyst

8. What to examine What is the level and unit of analysis?
Manifest Content
Words
Characters
Images
Items
Latent Content
Themes
Concepts
Semantics

9. Classes and Categories
Categories can be deductive (drawn from theory) or inductive (drawn from data) or combination of the two
Distinguishing between and among persons, things, and events
Common classes—used by virtually everyone
Special Classes—used by members of certain areas (argot or jargon)
Theoretical Classes—provides an overarching pattern (concepts)

10. Interrogative Hypothesis Testing
Make a rough hypothesis
Search for negative cases
Examine all relevant cases

11. Method of Constant Comparison
Look for indicators of categories in events and behavior - name them and code them on document(s)
Compare codes to find consistencies and differences
Consistencies between codes (similar meanings or pointing to a basic idea) reveals categories. So need to categorize specific events
Create memos on the comparisons and emerging categories
Eventually category saturates when no new codes related to it are formed
Certain categories become more central focus - axial categories and perhaps even core category.

12. Analytic Induction
Look at an event or activity and develop a hypothetical statement of what is going on.
Look at an similar instance and see how it fits the hypothesis. Revise hypothesis.
Look for exceptions to hypothesis. Revise hypothesis to fit all examples encountered.
Eventually will develop a hypotheses that accounts for all observed cases.

13. Other Analytic Strategies
Narrative approach: detailed narrative of field experience (descriptive)
Ideal types: (Weber) compare ideal forms (i.e. suggested by theory) to empirical observations
Successive Approximation: move back and forth between theory and data until theory (or generalization) is perfected
Illustrative Method: find empirical examples in the data to support the theory

14. The Framework Approach (source: Pope et al
The Framework Approach (source: Pope et al Analysing Qualitative Data)
Stage 1
· Familiarisation—immersion in the raw data (or typically a pragmatic selection from the data) by listening to tapes, reading transcripts, studying notes and so on, in order to list key ideas and recurrent themes

15. Framework Stage 2
· Identifying a thematic framework—identifying all the key issues, concepts, and themes by which the data can be examined and referenced. This is carried out by drawing on a priori issues and questions derived from the aims and objectives of the study as well as issues raised/observed within the data and/or views or experiences that recur in the data. The end product of this stage is a detailed index of the data, which labels the data into manageable chunks for subsequent retrieval and exploration

16. Framework Stage 3
· Indexing—applying the thematic framework or index systematically to all the data in textual form by annotating the transcripts with numerical codes from the index, usually supported by short text descriptors to elaborate the index heading. Single passages of text can often encompass a large number of different themes, each of which has to be recorded, usually in the margin of the transcript

17. Framework Stage 4
· Charting—rearranging the data according to the appropriate part of the thematic framework to which they relate, and forming charts. For example, there is likely to be a chart for each key subject area or theme with entries for several respondents. Unlike simple cut and paste methods that group verbatim text, the charts contain distilled summaries of the text. The charting process involves a considerable amount of abstraction and synthesis.

18. Framework Stage 5
· Mapping and interpretation—using the charts to define concepts, map the range and nature of phenomena, create typologies and find associations between themes with a view to providing explanations for the findings. The process of mapping and interpretation is influenced by the original research objectives as well as by the themes that have emerged from the data themselves.

19. Content Analysis Strengths Weaknesses Virtually unobtrusive
Cost effective
Trend identification over time
Weaknesses
Limited to examining already recorded messages
Ineffective for testing causal relationships