In many psychological inventories (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 generalized a recent probabilistic perturbation procedure, called SGR - Sample Generation by Replacement - (Lombardi & Pastore, 2012), to simulate fake data with correlational structures. To mimic this more complex faking data we proposed a novel extension of the SGR conditional replacement distribution which is based on on a discrete version of the truncated multivariate normal distribution. We also applied the new procedure to real behavioral data on the role of perceived affective self-efficacy in social contexts.

SGR modeling of fake ordinal data with correlational structures

PASTORE, MASSIMILIANO;NUCCI, MASSIMO;BOBBIO, ANDREA
2013

Abstract

In many psychological inventories (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 generalized a recent probabilistic perturbation procedure, called SGR - Sample Generation by Replacement - (Lombardi & Pastore, 2012), to simulate fake data with correlational structures. To mimic this more complex faking data we proposed a novel extension of the SGR conditional replacement distribution which is based on on a discrete version of the truncated multivariate normal distribution. We also applied the new procedure to real behavioral data on the role of perceived affective self-efficacy in social contexts.
2013
ASMDA 2013 Book of Abstracts
9786188069824
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2667053
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