Measures of compensatory and noncompensatory models of decision behavior: Process tracing versus policy capturing

Abstract While a variety of techniques have been used to infer compensatory versus noncompensatory decision making, few studies have used multiple measures in order to evaluate their validity. Using a sample of 48 college students, this study examines two measures from a search task (the variability and pattern of search on an information board) and three measures from a rating or judgment task (nonlinear regression modeling, ANOVA measures of interaction and curvilinearity). The validity of these measures is assessed by their sensitivity to a manipulation of information load and their extent of convergence with one another. The variability of search on the information board and the ANOVA measures of interaction and curvilinearity all indicated an increase in noncompensatory decision making under high information load, while the regression modeling measure did not. There was some convergence between the regression and ANOVA indices, but no relation between the search task and rating task measures. It is concluded that the ANOVA measures of interaction and curvilinearity are more sensitive measures than the nonlinear regression procedure. The information board and ANOVA measures are apparently both valid indices of noncompensatory decision making; they may lack convergence because they represent different parts of the decision process (information acquisition vs combination) or require different responses (choice vs judgment).

[1]  N. Anderson.  Application of an Additive Model to Impression Formation , 1962, Science.

[2]  Arthur L. Dudycha,et al.  The Effect of Variations in the Cue R Matrix Upon the Obtained Policy Equation of Judges , 1966 .

[3]  N. Anderson.  Test of a model for number-averaging behavior , 1964 .

[4]  J. E. Russo,et al.  The Value of Unit Price Information , 1977 .

[5]  O. Svenson.  Process descriptions of decision making. , 1979 .

[6]  John W. Payne,et al.  Task complexity and contingent processing in decision making: An information search and protocol analysis☆ , 1976 .

[7]  Lewis R. Goldberg,et al.  Five models of clinical judgment: An empirical comparison between linear and nonlinear representations of the human inference process , 1971 .

[8]  M. Tatsuoka.  Multivariate Analysis Techniques for Educational and Psychological Research , 1971 .

[9]  Richard W. Olshavsky,et al.  Task complexity and contingent processing in decision making: A replication and extension , 1979 .

[10]  A. Tversky.  Intransitivity of preferences. , 1969 .

[11]  Jennifer Crocker,et al.  Judgment of Covariation by Social Perceivers , 1981 .

[12]  R. Hogarth,et al.  BEHAVIORAL DECISION THEORY: PROCESSES OF JUDGMENT AND CHOICE , 1981 .

[13]  H. J. Einhorn,et al.  Linear regression and process-tracing models of judgment. , 1979 .

[14]  P. Slovic,et al.  An analysis-of-variance model for the assessment of configural cue utilization in clinical judgment. , 1968, Psychological bulletin.

[15]  H. J. Einhorn.  Use of nonlinear, noncompensatory models as a function of task and amount of information , 1971 .

[16]  Paul Slovic,et al.  Analyzing the Use of Information in Investment Decision Making: A Methodological Proposal , 1972 .

[17]  P. Slovic.  Analyzing the expert judge: A descriptive study of a stockbroker's decision process. , 1969 .

[18]  L. A. Marascuilo,et al.  Nonparametric and Distribution-Free Methods for the Social Sciences , 1977 .

[19]  A. Tversky.  Elimination by aspects: A theory of choice. , 1972 .

[20]  R. Dawes,et al.  Linear models in decision making. , 1974 .

[21]  J. Bettman,et al.  Effects of Information Presentation Format on Consumer Information Acquisition Strategies , 1977 .

[22]  H. J. Einhorn.  The use of nonlinear, noncompensatory models in decision making. , 1970, Psychological bulletin.

[23]  Norman H. Anderson,et al.  Weak inference with linear models. , 1977 .