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Procedural Analysis or structured approach

Procedural Analysis or structured approach. Procedural Analysis or structured approach. Sometimes known as Analytic Induction Used more commonly in evaluation and policy studies. Uses a set of procedures as a way of establishing explanations and causal links

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Procedural Analysis or structured approach

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  1. Procedural Analysis or structured approach

  2. Procedural Analysis or structured approach • Sometimes known as Analytic Induction • Used more commonly in evaluation and policy studies. • Uses a set of procedures as a way of establishing explanations and causal links • The approach tends to assume that meanings are transparent, obvious and unambiguous, and instead concentrates on validating explanations.

  3. References • Robson, C. (1993) Real World Research (First Ed) • Yin, R. (1994) Case Study Research: Design and Method • Bryman,A (2008) Social Research Methods pp. 539-541. • Gomm, R, Hammersley, M, & Foster P (eds) (2000) Case Study Method – See esp. chapter 8.

  4. Reasons for structured approach Deficiencies in the analyst • Data overload (too much to remember, process etc.) • First Impressions (bias to first things read) • Information availability (easy information gets more attention) • Positive instances (stress what confirms ideas) • Internal consistency (discount the novel) • Uneven reliability (not all sources equally good)

  5. Reasons for structured approach 2 • Missing information (don’t try hard enough to fill gaps) • Revision of hypotheses (over- or under- react to new info) • Fictional base (compare with assumed average) • Confidence in judgement (too much when made) • Non-occurrence (seen as evidence for strong correlation) • Inconsistency (different interpretations of same data) All this means threats to: • reliability and generalizability • validity

  6. Especially for validity • History (background changes during study) • Mortality (people dropping out unrepresentative) • Maturation (people changed by the study) • ambiguity about causal direction (unclear what causes what)

  7. For generalizability Limited to: • group studied • setting it took place in • time it happened • particular constructs of the group

  8. Pattern Matching • Compare an empirical based pattern with a predicted one (and with several alternatives) • Looking for causal connections • If patterns coincide this strengthens internal validity.

  9. 1. Non-equivalent dependent variables as a pattern Like quasi-experimental designs: hypothesis testing • Use where theory suggests multiple dependent variables or a variety of outcomes. • If for each outcome, initially predicted outcomes are found and alternative are not found (i.e. no threats to validity found) then can infer causal influence.

  10. e.g. Effects of decentralized approach to office automation from Yin • Decentralization theory suggests 4 outcomes • Employees create new applications • Traditional supervisory links are threatened • Organizational conflicts increase • Productivity will increase • If results in the case are as these 4 predictions then draw conclusions that it is decentralization that caused these effects.

  11. Replication • Literal replication = more cases of the same kind • Theoretical replication = case where automation is centralized. Then there will be 4 different effects. Causal link is confirmed if this is what happened. • BUT must be aware of threats to validity. Identify and eliminate all reasonable threats.

  12. 2. Rival Explanations • Test contrasting theories • i.e. we have several independent variables or starting circumstances. Examine cases for characteristics of the precursors to see which fits best.

  13. Problems with pattern matching • How do we know a pattern fits? Matter of judgement. • Need to balance overly restrictive application of pattern matching with too loose a match. • “eyeballing” the pattern is good enough.

  14. Analytic Induction A.k.a. • Explanation building • i.e. build up and confirm a set of causal links between events, actions etc in the case.

  15. Analytic Induction process

  16. Problems • Need constantly to entertain other plausible, rival explanations. = HARD WORK • With each iteration, there may be a drift from the original question or lots of work to re-analyze data. • Only establishes sufficient conditions (not necessary) • No guide as to how many negative cases are needed for validity.

  17. Time series • Like time series in a quasi experiment.

  18. Problem • Changes may have no clear start or end point. Must compare • Theoretically significant trend predicted before research, • Rival trend predicted before research, • Any trend based on artefacts or threats to internal validity.

  19. Chronologies Look for sequences and patterns of events. e.g. • some events always happen before others, and the reverse is impossible • some events always follow others, • some events always follow others after the passage of time • some time periods differ from other time periods in the type of events that occur.

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