| Abstract: | The principle of ‘divide and conquer’ (DAC) suggests that complex decision problems should be decomposed into smaller, more manageable parts, and that these parts should be logically aggregated to derive an overall value for each alternative. Decompositional procedures have been contrasted with holistic evaluations that require decision makers to simultaneously consider all the relevant attributes of the alternatives under consideration (Fischer, 1977 ). One area where decompositional procedures have a clear advantage over holistic procedures is in the reduction of random error (Ravinder, 1992 ; Ravinder and Kleinmuntz, 1991 ; Kleinmuntz, 1990 ). Adopting the framework originally developed by Ravinder and colleagues, this paper details the results of a study of the random error variances associated with another popular multi‐criteria decision‐making technique, the Analytic Hierarchy Process (AHP); (Saaty, 1977 , 1980 ), as well as the random error variances of a holistic version of the Analytic Hierarchy Process (Jensen, 1983 ). In addition, data concerning various psychometric properties (e.g. the convergent validity and temporal stability) and values of AHP inconsistency are reported for both the decompositional and holistic evaluations. The results of the study show that the Ravinder and Kleinmuntz ( 1991 ) error‐propagation framework extends to the AHP and decompositional AHP judgments are more consistent than their holistic counterparts. Copyright © 2001 John Wiley & Sons, Ltd. |
