Three Models of Clinical Judgment

Wiggins, Nancy; Hoffman, Paul

doi:10.1037/e301192005-001

Cited by 27 publications

(44 citation statements)

References 0 publications

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“…They found that the procedures yielded similar results when the criterion was to match the expert=s decisions. Schmitt (1978) replicated this study and obtained similar findings, and further support was provided by Goldberg (1968Goldberg ( , 1971, Heeler, Kearney and Mehaffey (1973), Slovic, Fleissner and Bauman (1972), and Wiggins and Hoffman (1968). Since different methods produce similar results, one might focus on choosing simple procedures.…”

Section: • Use the Most Successful Expertssupporting

confidence: 68%

Judgmental Bootstrapping: Inferring Experts’ Rules for Forecasting

Armstrong

2001

International Series in Operations Research &Amp; Management Science

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Judgmental bootstrapping is a type of expert system. It translates an experts' rules into a quantitative model by regressing the experts' forecasts against the information that he used. Bootstrapping models apply an experts' rules consistently, and many studies have shown that decisions and predictions from bootstrapping models are similar to those from the experts. Three studies showed that bootstrapping improved the quality of production decisions in companies. To date, research on forecasting with judgmental bootstrapping has been restricted primarily to cross-sectional data, not time-series data. Studies from psychology, education, personnel, marketing, and finance, showed that bootstrapping forecasts were more accurate than forecasts made by experts using unaided judgment. They were more accurate for eight of eleven comparisons, less accurate in one, and there were two ties. The gains in accuracy were generally substantial. Bootstrapping can be useful when historical data on the variable to be forecast are lacking or of poor quality; otherwise, econometric models should be used. Bootstrapping is most appropriate for complex situations, where judgments are unreliable, and where experts' judgments have some validity. When many forecasts are needed, bootstrapping is cost-effective. If experts differ greatly in expertise, bootstrapping can allow one to draw upon the forecasts made by the best experts. Bootstrapping aids learning; it can help to identify biases in the way experts make predictions, and it can reveal how the best experts make predictions. Finally, judgmental bootstrapping offers the possibility of conducting ?experiments@ when the historical data for causal variables have not varied over time. Thus, it can serve as a supplement for econometric models. ABSTRACTJudgmental bootstrapping is a type of expert system. It translates an expert=s rules into a quantitative model by regressing the expert=s forecasts against the information that he used. Bootstrapping models apply an expert=s rules consistently, and many studies have shown that decisions and predictions from bootstrapping models are similar to those from the experts. Three studies showed that bootstrapping improved the quality of production decisions in companies. To date, research on forecasting with judgmental bootstrapping has been restricted primarily to cross-sectional data, not time-series data. Studies from psychology, education, personnel, marketing, and finance, showed that bootstrapping forecasts were more accurate than forecasts made by experts using unaided judgment. They were more accurate for eight of eleven comparisons, less accurate in one, and there were two ties. The gains in accuracy were generally substantial. Bootstrapping can be useful when historical data on the variable to be forecast are lacking or of poor quality; otherwise, econometric models should be used. Bootstrapping is most appropriate for complex situations, where judgments are unreliable, and where experts= judgments have some validity. When many for...

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Section: • Use the Most Successful Expertssupporting

confidence: 68%

Judgmental Bootstrapping: Inferring Experts’ Rules for Forecasting

Armstrong

2001

International Series in Operations Research &Amp; Management Science

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“…Among these studies, only Wiggins and Hoffman used product and quadratic terms. They found that for 8 of the 29 judges, a model that includes such terms gives a better fit to the data than a model that includes only linear terms (see Wiggins & Hoffman, 1968, Table 3, p. 75). However, their findings shed no light on the issue discussed in the current article, because they neither report the signs of the interaction and the curvilinear terms, nor do they provide any theory about the pattern of the signs of these terms.…”

Section: Theorymentioning

confidence: 99%

“…Several studies have investigated nonlinear relationships in the judgments of Meehl's data (Wiggins & Hoffman, 1968;Goldberg, 1971;Einhorn, 1974;Ganzach, 1995), and most of them did indeed find such relationships. (Even Goldberg, 1971, who disputes the eadier conclusions of Einhorn, 1970Einhorn, , 1971, concerning nonlinearity in judgment, concludes that there are nonlinear relationships in these data; see pp.…”

Section: Theorymentioning

confidence: 99%

Misleading interaction and curvilinear terms.

Ganzach

1997

Psychological Methods

253

269

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This article examines the relationships between interaction (product) terms and curvilinear (quadratic) terms in regression models in which the independent variables are correlated. The author uses 2 substantive examples to demonstrate the following outcomes: (a) If the appropriate quadratic terms are not added to the estimated model, then the observed interaction may indicate a synergistic (offsetting) relationship between the independent variables, whereas the true relationship is, in fact, offsetting (synergistic). (b) If the appropriate product terms are not added to the equation, then the estimated model may indicate concave (convex) relationships between the independent variables and the dependent variable, whereas the true relationship is, in fact, convex (concave). (c) If the appropriate product and quadratic terms are not examined simultaneously, then the observed interactive or curvilinear relationships may be nonsignificant when such relationships exist. The implications of these results for the examination of interaction and quadratic effects in multiple regression analysis are discussed.

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“…We have noted, however, that our method is also appropriate to removing configuralities in regression models for simulated clinical iudgment (Wiggins & Hoffman, 1968) and to removing orthogonal trend components, e.g., cubic, quartic, linear by cubic, from the main effects of interactions in experimental-design models. Although these last applications, such as removing the linear by cubic component by a scale transformation, appear at first glance to be remote from any practical psychologieal problems, they turn ou t to have a rather wide range of important applieations that we shall describe in future, more extensive reports on this method.…”

mentioning

confidence: 99%