Aggregation bias and the use of regression in evaluating models of human performance

Regression analyses are increasingly being used to provide confirmatory evidence for models of human performance. The amount of information made available to judge these models is reduced because clearly established standards in the techniques of performing and reporting regression analyses are lacking. This paper addresses two primary problems in regression analysis: aggregation of data and the aggregation of variables into composite models. We provide examples of the misuse of regression techniques and recommend ways in which the amount of information made available to evaluate the model being tested can be maximized in analysis and reporting.