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Policy Capturing

Policy capturing has its roots in activities central to industrial/organizational (I/O) psychology. Its origins lie in the work of the Personnel Research Laboratory at Lackland Air Force Base in the 1950s, and it achieved prominence in the broader field of psychology with the publication in 1960 of Paul Hoffman's Psychological Bulletin paper, “The Paramorphic Representation of Clinical Judgment.” Although policy capturing is not derivative of Egon Brunswik's probabilistic functionalism, scholars in the Brunswikian tradition have been attracted to policy capturing as a method to address certain research questions. This attraction is based on the practice in good policy capturing research of faithfully representing the situation to which generalization is aimed. Hence, policy capturing is often loosely associated with social judgment theory, which is the contemporary manifestation of Brunswikian theory.

Tasks

Many cognitive tasks require decision makers to make inferences or decisions based on multiple, often conflicting, pieces of information. Such tasks include performance assessment and salary assignments, employment interviewing, investment decisions, medical diagnosis and prognosis, evaluation of charges of discrimination, assessment of the desirability of employment contracts, and even the selection of the most appropriate bullet for use by an urban police force—the list is endless. Such tasks abound in organizations! Policy capturing is used to investigate what factors influence the decision maker, and how heavily each is weighted. Environmental outcomes are not part of the policy capturing procedure.

Data Collection

The essence of the data-gathering procedure is to have an individual respondent make a substantial number of judgments on multiattribute bundles, often paper-and-pencil or computer-presented profiles, but the judgments can be made on actual people, files, or abstracts of files or anything that can be represented by a set of quantitative variables. Typically, the attributes and the judgments are treated as interval scales, although dichotomous data such as gender are often found among quantitative variables including age, length of experience, or rating scales. The phrase individual respondent was not an accident, in that policy capturing entails an idiographic analysis, which may be followed by nomothetic analyses of the idiographic indexes describing the individual respondents.

Data Analysis

The appropriate data analysis depends on a number of factors, including the level of measurement of the predictors and the judgments, the function forms relating predictors to judgments, predictor intercorrelation, the presumed aggregation rule, and so forth. The common default procedure is multiple regression, but mathematical models that reflect noncompensatory rules such as conjunctive or disjunctive decision rules might also be used. Given that multiple regression is the most commonly used analytic procedure, we'll concentrate on it.

Multiple Regression

Given a sufficient number of multiattribute judgments, the investigator can use ordinary least squares regression to ascertain the degree to which each attribute accounts for variance in the judgments. Doing so requires the usual assumptions underlying regression, some of which can be violated without affecting the investigator's inferences too severely. For example, if the linear function form assumed in the regression algorithm does not correspond exactly to that used by the judge but is monotonically related thereto, the model misspecification tends to be inconsequential. Furthermore, appropriate cross-validation within subjects provides some sense of the consequences of violations of assumptions.

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