6 Naturalistic and ethnographic research
Step 7 Theory generation
angle lens to gather data, and then, by sifting, sorting, reviewing and reflecting on them the salient features of the situation emerge. These are then used as the agenda for subsequent fo-cusing. The process is akin to funnelling from the wide to the narrow.
At a theoretical level a major feature of quali-tative research is that analysis commences early on in the data collection process so that theory generation can be undertaken (LeCompte and Preissle, 1993:238). The authors (pp. 237–53) advise that researchers should set out the main outlines of the phenomena that are under inves-tigation. They then should assemble chunks or groups of data, putting them together to make a coherent whole (e.g. through writing summa-ries of what has been found). Then they should painstakingly take apart their field notes, match-ing, contrastmatch-ing, aggregatmatch-ing, comparing and ordering notes made. The intention is to move from description to explanation and theory gen-eration.
Becker and Geer (1960) indicate how this might proceed:
• comparing different groups simultaneously and over time;
• matching the responses given in interviews to observed behaviour;
• an analysis of deviant and negative cases;
• calculating frequencies of occurrences and responses;
• assembling and providing sufficient data that keeps separate raw data from analysis.
For clarity, the process of data analysis can be portrayed in a sequence of seven steps:
Step 1 Establish units of analysis of the data,
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data, though clearly, there is a process of itera-tion and reiteraitera-tion whereby some codes that are used in the early stages of coding might be modified subsequently and vice versa, necessi-tating the researcher to go through a data set more than once to ensure consistency, refine-ment, modification and exhaustiveness of cod-ing (some codes might become redundant, oth-ers might need to be broken down into finer codes). By coding up the data the researcher is able to detect frequencies (which codes are oc-curring most commonly) and patterns (which codes occur together).
Hammersley and Atkinson (1983:177–8) pro-pose that the first activity here is to read and re-read the data to become thoroughly familiar with them, noting also any interesting patterns, any surprising, puzzling or unexpected features, any apparent inconsistencies or contradictions (e.g. between groups, within and between indi-viduals and groups, between what people say and what they do).
Step 2: create a ‘domain analysis’
This involves grouping the units into domains, clusters, groups, patterns, themes and coherent sets to form domains. A domain is any symbolic category that includes other categories (Spradley, 1979:100). At this stage it might be useful for the researcher to recode the data into domain codes, or to review the codes used to see how they naturally fall into clusters, perhaps creat-ing overarchcreat-ing codes for each cluster.
Hammersley and Atkinson (1983) show how items can be assigned to more than one category, and, indeed, see this as desirable as it maintains the richness of the data. This is akin to the proc-ess of ‘categorization’ (Lincoln and Guba, 1985), putting ‘unitized’ data to provide descriptive and inferential information.
Spradley (1979) suggests that establishing domains can be achieved by four analytic tasks:
(a) selecting a sample of verbatim interview and field notes; (b) looking for the names of things;
(c) identifying possible terms from the sample;
(d) searching through additional notes for other
items to include. He identifies six steps to achieve these tasks: (i) select a single semantic relation-ship; (ii) prepare a domain analysis sheet; (iii) select a sample of statements from respondents;
(iv) search for possible cover terms and included terms that fit the semantic relationship identi-fied; (v) formulate structural questions for each domain identified; (vi) list all the hypothesized domains. Domain analysis, then, strives to dis-cover relationships between symbols (ibid.: 157).
Step 3: establish relationships and linkages between the domains
This process ensures that the data, their rich-ness and ‘context-groundedrich-ness’ are retained.
Linkages can be found by identifying confirm-ing cases, by seekconfirm-ing ‘underlyconfirm-ing associations’
(LeCompte and Preissle, 1993:246) and connec-tions between data subsets.
Step 4: making speculative inferences
This is an important stage, for it moves the re-search from description to inference. It requires the researcher, on the basis of the evidence, to posit some explanations for the situation, some key elements and possibly even their causes. It is the process of hypothesis generation or the setting of working hypotheses that feeds into theory generation.
Step 5: summarizing
By this stage the researcher will be in a position to write a summary of the main features of the situation that have been researched so far. The summary will identify key factors, key issues, key concepts and key areas for subsequent in-vestigation. It is a watershed stage during the data collection, as it pinpoints major themes, issues and problems that have arisen from the data to date (responsively) and suggests avenues for further investigation. The concepts used will have been a combination of those derived from the data themselves and those inferred by the re-searcher (Hammersley and Atkinson, 1983:178).
By this stage the researcher will have gone through the preliminary stages of theory
PLANNING NATURALISTIC RESEARCH
generation. Patton (1980) sets these out for qualitative data:
• finding a focus for the research and analysis;
• organizing, processing, ordering and check-ing data;
• writing a qualitative description or analysis;
• inductively developing categories, typologies, and labels;
• analysing the categories to identify where further clarification and cross-clarification are needed;
• expressing and typifying these categories through metaphors (see also Pitman and Maxwell, 1992:747);
• making inferences and speculations about relationships, causes and effects.
Bogdan and Biklen (1992:154–63) identify sev-eral important items that researchers need to address at this stage, including: forcing yourself to take decisions that will focus and narrow the study and decide what kind of study it will be;
developing analytical questions; using previous observational data to inform subsequent data col-lection; writing reflexive notes and memos about observations, ideas, what you are learning; trying out ideas with subjects; analysing relevant litera-ture whilst you are conducting the field research;
generating concepts, metaphors and analogies and visual devices to clarify the research.
Step 6: seeking negative and discrepant cases
In theory generation it is important to seek not only confirming cases but to weigh the signifi-cance of discontinuing cases. LeCompte and Preissle (1993:270) suggest that because inter-pretations of the data are grounded in the data themselves, results that fail to support an origi-nal hypothesis are neither discarded nor dis-cred-ited; rather, it is the hypotheses themselves that must be modified to accommodate these data.
Indeed Erickson (1992:208) identifies progres-sive problem-solving as one key aspect of eth-nographic research and data analysis. LeCompte
and Preissle (1993:250–1) define a negative case as an exemplar which disconfirms or refutes the working hypothesis, rule or explanation so far.
It is the qualitative researcher’s equivalent of the positivist’s null hypothesis. The theory that is being developed becomes more robust if it ad-dresses negative cases, for it sets the boundaries to the theory; it modifies the theory, it sets pa-rameters to the applicability of the theory.
Discrepant cases are not so much exceptions to the rule (as in negative cases) as variants of the rule (ibid.: 251). The discrepant case leads to the modification or elaboration of the con-struct, rule or emerging hypothesis. Discrepant case analysis requires the researcher to seek out cases for which the rule, construct or explana-tion cannot account or with which they will not fit, i.e. they are neither exceptions nor contra-dictions, they are simply different!
Step 7: theory generation
Here the theory derives from the data—it is grounded in the data and emerges from it. As Lincoln and Guba (1985:205) argue, grounded theory must fit the situation that is being re-searched. By going through the previous sections, particularly the search for confirming, negative and discrepant cases, the researcher is able to keep a ‘running total’ of these cases for a par-ticular theory. The researcher also generates al-ternative theories for the phenomena under in-vestigation and performs the same count of con-firming, negative and discrepant cases. Lincoln and Guba (ibid.: 253) argue that the theory with the greatest incidence of confirming cases and the lowest incidence of negative and discrepant cases is the most robust.
There are several procedural tools for ana-lysing qualitative data. LeCompte and Preissle (ibid.: 253) see analytic induction, constant com-parison, typological analysis and enumeration (discussed above) as valuable tools for the quali-tative researcher to use in analysing data and generating theory.
Analytic induction is a term and process that was introduced by Znaniecki (1934) deliberately
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in opposition to statistical methods of data analy-sis. LeCompte and Preissle (1993:254) suggest that the process is akin to the several steps set out above, in that: (a) data are scanned to gener-ate cgener-ategories of phenomena; (b) relationships between these categories are sought; (c) working typologies and summaries are written on the ba-sis of the data examined; (d) these are then re-fined by subsequent cases and analysis; (e) nega-tive and discrepant cases are deliberately sought to modify, enlarge or restrict the original expla-nation/theory. Denzin (1970:192) uses the term
‘analytical induction’ to describe the broad strat-egy of participant observation that is set out be-low:
• A rough definition of the phenomenon to be explained is formulated.
• A hypothetical explanation of that phenom-enon is formulated.
• One case is studied in the light of the hypoth-esis, with the object of determining whether or not the hypothesis fits the facts in that case.
• If the hypothesis does not fit the facts, either the hypothesis is reformulated or the phenom-enon to be explained is redefined, so that the case is excluded.
• Practical certainty may be attained after a small number of cases has been examined, but the discovery of negative cases disproves the explanation and requires a reformulation.
• This procedure of examining cases, redefin-ing the phenomenon, and reformulatredefin-ing the hypothesis is continued until a universal re-lationship is established, each negative case calling for a redefinition of a reformulation.
A more deliberate seeking of discontinuing cases is advocated by Bogdan and Biklen (1992:72) where they enumerate five main stages in ana-lytic induction:
Step 1 In the early stages of the research a rough