Effects of malingering in self-report measures: A scenario analysis approach

In many psychological questionnaires (i.e., personnel selection surveys and diagnostic tests) the collected samples often include fraudulent records. This confronts the researcher with the crucial problem of biases yielded by the usage of standard statistical models. In this paper we generalize a recent combinato- rial perturbation procedure, called SGR (Sample Generation by Replacements; [Lombardi et al., 2004]), to the analysis of structured malingering scenarios for dichotomous data. Combinatorial aspects of the approach are discussed and an application to a simple data set on the drug addiction domain is presented. Fi- nally, the close relationships with Monte Carlo simulation studies are explored.