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Procedural Analysis or structured approach

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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.

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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.

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Reasons for structured approach

Deficiencies in the analyst1. Data overload (too much to remember, process

etc.)2. First Impressions (bias to first things read)3. Information availability (easy information gets

more attention)4. Positive instances (stress what confirms ideas)5. Internal consistency (discount the novel)6. Uneven reliability (not all sources equally good)

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Reasons for structured approach 2

7. Missing information (don’t try hard enough to fill gaps)8. Revision of hypotheses (over- or under- react to new

info)9. Fictional base (compare with assumed average)10. Confidence in judgement (too much when made)11. Non-occurrence (seen as evidence for strong

correlation)12. Inconsistency (different interpretations of same data)All this means threats to: reliability and generalizability validity

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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)

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For generalizability

Limited to: group studied setting it took place in time it happened particular constructs of the group

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Analytic Induction process

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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.

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Time series

Like time series in a quasi experiment.

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Problem Changes may have no clear start or end point.

Must compare1. Theoretically significant trend predicted before

research,2. Rival trend predicted before research,3. Any trend based on artefacts or threats to

internal validity.

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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.