Although this thesis has an initial analytical framework, its purpose is still to develop a theory. Therefore, its data analysis is also of an inductive nature. Inductive data analysis may be defined most simply as a process for “making sense” of field data (Lincoln and Guba, 1985, p.203). The data analysis strategy adopted here is the one proposed by Miles and Huberman (1984; 1994).
Miles and Huberman (1984, pp.21-23) describe data analysis as consisting of three concurrent activities. Data reduction refers to the process of selecting, simplifying, abstracting and transforming the raw case data. Data displays include narratives, matrices, graphs, tables and various charts. Conclusion drawing/verification involves drawing meaning from data and building a logical chain of evidence. Various types of matrices, clustering diagrams and causal networks are used. Several techniques are similar to those of grounded theory; these include coding of data segments into categories identified from the study’s initial conceptual framework or hypotheses, subsequent pattern coding to identify patterns or repeatable regularities in the data, and memoing (making notes) as a step towards producing a conceptually coherent explanation of the phenomenon being studied.
(1) The analytic progression
Following Carney (1990), Miles and Huberman (1994) see the analytic progression as a sort of “ladder of abstraction” (see Figure 3.2). A researcher begins with a text, trying out coding categories on it, then moving to identify themes and trends, and then to testing hunches and findings, aiming first to delineate the “deep structure” and then to integrate the data into an explanatory framework.
Figure 3.2 The ladder of analytical abstraction
Creating a text to work on
Trying out coding categories to find a set that fits
Identifying themes and trends in the data overall
Testing hypotheses and reducing the bulk of the data for analysis of trends in it
Delineating the deep structure
Synthesis: integrating the data into one explanatory framework Cross-checking
tentative findings Matrix analysis of major themes in data Searching for relationships
in the data: writing analytical memos Finding out where the emphases and gaps in the data are
Coding of data
Writing of analytical notes on linkages to various frameworks of interpretation
Reconstruction of interviews tapes as written notes
Synopses of individual interviews
1 Summarizing and packaging the data
2 Repackaging and aggregating the data LEVELS
3 Developing and testing propositions to construct an explanatory framework
1a
1b
3a
3b
Source: Miles and Huberman, 1994 (p.92) on the basis of Carney, 1990.
There is no clear or clean boundary between describing and explaining. The researcher typically moves through a series of analysis episodes that condense more and more data into a more and more coherent understanding of what, how, and why (Miles and Huberman, 1994).
(2) Codes and first-level coding
Codes are “tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study” (Miles and Huberman, 1994, p.56). Codes are used to retrieve and organize “chunks” of varying size – words, phrases, sentences, or whole paragraphs, connected or unconnected to a specific setting, so the researcher can quickly find, pull out, and cluster the segments relating to a particular research question, hypothesis, construct, or theme. Clustering, and, as we will see, display of condensed chunks, then sets the stage for drawing conclusions (Miles and Huberman, 1994).
Coding is analysis. The conventional advice is to go through transcripts or field notes with a pencil, marking off units that cohered because they dealt with the same topic and then dividing them into topics and subtopics at different levels of analysis. These identifiable topics (or themes or gestalts) presumably would recur with some regularity. They would be given a “name”, and instances of them would be marked with a shorthand label – a code (Miles and Huberman, 1994).
Codes can be at different levels of analysis, ranging from the descriptive to the inferential. First-level coding is a device for summarizing segments of data. Codes used at this level can be either descriptive or interpretive codes.
The method of creating codes suggested by Miles and Huberman (1994) is that of creating a provisional “start list” of codes. That list may come from the conceptual framework, literature review, professional definitions, local commonsense constructs, and researcher’s values and prior experiences (Miles and Huberman, 1994; Bulmer, 1979).
For all approaches to coding – predefined, accounting-scheme guided, or postdefined – codes will change and develop as field experience continues (Miles and
Huberman, 1994). Some codes decay, other codes flourish, still other codes emerge progressively.
Whether codes are created and revised early or late, Miles and Huberman (1994) emphasize that they should have some conceptual and structural order; clear operational definitions are also indispensable.
Table 4.4 in Chapter 4 (p.119) is the final code list for this thesis.
(3) Pattern coding
Pattern codes are explanatory or inferential codes, ones that identify an emergent theme, configuration, or explanation. They pull together a lot of material into more meaningful and parsimonious units of analysis. Pattern coding is a way of grouping those summaries into a smaller number of sets, themes, or constructs. For qualitative researchers, it is an analogue to the cluster-analytic and factor-analytic devices used in statistical analysis (Miles and Huberman, 1994).
Pattern codes usually turn around four summarizers: themes, causes/explanations, relationships among people, and more theoretical constructs.
(4) Memoing
A memo is “the theorizing write-up of ideas about codes and their relationships as they strike the analyst while coding … it can be a sentence, a paragraph or a few pages … it exhausts the analyst’s momentary ideation based on data with perhaps a little conceptual elaboration” (Glaser, 1978, pp.83-84).
Memoing helps the analyst move easily from empirical data to a conceptual level, refining and expanding codes further, developing key categories and showing their relationships, and building toward a more integrated understanding of events, processes, and interactions in the case (Miles and Huberman, 1994).
Miles and Huberman (1994) suggest that priority should always be given to memoing. When an idea strikes, a researcher should stop whatever else is doing and write the memo. Memoing should also begin as soon as the first field data start coming in, and usually should continue right up to production of the final report.
(5) Developing propositions
As a study proceeds, there is a greater need to formalize and systematize the researcher’s thinking into a coherent set of explanations. One way to do that is to generate propositions, or connected sets of statements, reflecting the findings and conclusions of the study (Miles and Huberman, 1994).
(6) Drawing and verifying conclusions
Miles and Huberman (1994) give two lists of tactics, the first for generating meaning in qualitative analysis, and the second for testing or confirming findings (see Table 3.10).
Table 3.10 Tactics for generating meaning and testing or confirming findings
Tactics for generating meaning Tactics for testing or confirming findings
1. noting patterns, themes 2. seeing plausibility 3. clustering 4. making metaphors 5. counting 6. making contrasts/comparisons 7. partitioning variables
8. subsuming particulars into the general 9. factoring
10. noting relations between variables 11. finding intervening variables 12. building a logical chain of evidence 13. making conceptual/theoretical coherence
1. checking for representativeness 2. checking for researcher effects 3. triangulating
4. weighting the evidence
5. checking the meaning of outliers 6. using extreme cases
7. following up surprises
8. looking for negative evidence 9. making if-then tests
10. ruling out spurious relations 11. replicating a finding
12. checking out rival explanations 13. getting feedback from informants
Source: Miles and Huberman, 1994.
Tactics for generating meaning are numbered from 1 to 13. They are arranged roughly from the descriptive to the explanatory, and the concrete to the more conceptual and abstract.
Tactics for testing or confirming findings are also numbered from 1 to 13, beginning with ones aimed at ensuring the basic quality of the data, then moving to those that check findings by examining exceptions to early patterns. They conclude with tactics that take a skeptical, demanding approach to emerging explanations (Miles and Huberman, 1994).