A Meta-Analytic Method for Testing Hypotheses about Clusters of Decision Makers

Meta-analytic methods are adapted to the task of testing hypotheses about clusters of decision makers, and an empirical illustration of these methods is presented. These methods have a number of advantages. Meta-analytic methods enable the researcher to (a) hypothesize about factors which influence cue weights and empirically test their moderating influences, (b) estimate the true relative weight from the observed relative weight after correcting for statistical artifacts, and (c) partition the variability in the observed relative weights across decision makers into variability across decision makers caused by variations in the true relative weights and the variability across decision makers caused by statistical artifacts. Finally, these methods facilitate integration of the results of individual studies without the necessity for the raw data employed in the individual studies.