More on Qualitative Data Collection and Data Analysis

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

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

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.

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

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)

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

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

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)

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

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.

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.

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

The Framework Approach (source: Pope et al The Framework Approach (source: Pope et al. 2000 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

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

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

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.

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.

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