Covariation assessment in rank order data

Two experiments were conducted to investigate how individuals assess covariation with rank order data. In both studies, subjects were given sets of rank order data, each set consisting of ten items ranked on two characteristics, and were asked to estimate the degree of relationship for each set. Contrary to previous research, subjects' estimates of covariation in this task were quite sensitive to actual levels of correlation in the data and remained unaffected by simple variations in the way rank order data were presented. More importantly, it appeared that this sensitivity to covariation was due likely to the use of a simple heuristic referred to here as the total discrepancy heuristic. These findings are discussed in terms of the availability of simple heuristics in rank-ordered versus other types of data and the consequences of using such heuristics in decision-making contexts.

[1]  James R. Bettman,et al.  Sampling Data for Covariation Assessment: The Effect of Prior Beliefs on Search Patterns , 1986 .

[2]  G. Murphy,et al.  The utility of theories in intuitive statistics: The robustness of theory-based judgments. , 1984 .

[3]  L. J. Chapman,et al.  Genesis of popular but erroneous psychodiagnostic observations. , 1967, Journal of abnormal psychology.

[4]  Lee Roy Beach,et al.  Inferences about correlations , 1966 .

[5]  Michael H. Birnbaum,et al.  The devil rides again: Correlation as an index of fit. , 1973 .

[6]  L. Abramson,et al.  Judgment of contingency in depressed and nondepressed students: sadder but wiser? , 1979 .

[7]  R. Nisbett,et al.  The psychometrics of everyday life , 1986, Cognitive Psychology.

[8]  David M. Lane,et al.  Judging the Relatedness of Variables: The Psychophysics of Covariation Detection , 1985 .

[9]  L. Alloy,et al.  Assessment of covariation by humans and animals: The joint influence of prior expectations and current situational information. , 1984 .

[10]  H. J. Einhorn,et al.  Expression theory and the preference reversal phenomena. , 1987 .

[11]  L. J. Chapman,et al.  Illusory correlation as an obstacle to the use of valid psychodiagnostic signs. , 1969, Journal of abnormal psychology.

[12]  C. Peterson Recognition of noncontingency. , 1980 .

[13]  G. A. Miller,et al.  Book Review Nisbett, R. , & Ross, L.Human inference: Strategies and shortcomings of social judgment.Englewood Cliffs, N.J.: Prentice-Hall, 1980. , 1982 .

[14]  D. Erlick,et al.  Perceptual quantification of conditional dependency. , 1967, Journal of experimental psychology.

[15]  L. Abramson,et al.  Judgment of contingency in depressed and nondepressed students: sadder but wiser? , 1979, Journal of experimental psychology. General.

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

[17]  Hal R. Arkes,et al.  Estimates of contingency between two dichotomous variables. , 1983 .

[18]  H. M. Jenkins,et al.  The display of information and the judgment of contingency. , 1965, Canadian journal of psychology.

[19]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[20]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[21]  H. Shaklee,et al.  A rule analysis of judgments of covariation between events , 1980, Memory & cognition.

[22]  G. Pitz,et al.  Judgment and decision: theory and application. , 1984, Annual review of psychology.

[23]  John W. Payne,et al.  Contingent decision behavior. , 1982 .

[24]  Harriet Shaklee,et al.  Sources of error in judging event covariations: Effects of memory demands. , 1982 .