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SGR Modeling of Correlational Effects in Fake Good Self-report Measures

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Abstract

In many self-report measures (i.e., personality survey items and diagnostic test items) the collected samples often include fake records. A case of particular interest in selfreport measures is the presence of caricature effects in participants’ responses under faking good motivation conditions. We say that a pattern of fake responses is a caricature pattern if it shows higher structural intercorrelations among faked items relative to the expected intercorrelations under the corresponding uncorrupted responses. In this paper we generalized a recent probabilistic perturbation procedure, called SGR - Sample Generation by Replacements - (Lombardi and Pastore (2012) Multivar Behav Res 47:519–546), to simulate caricature effects in fake good responses. To represent this particular faking behavior we proposed a novel extension of the SGR conditional replacement distribution which is based 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 and on self-report behaviors in reckless driving.

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Authors and Affiliations

  1. Department of Psychology and Cognitive Science, University of Trento, corso Bettini, 31, 38068, Rovereto, (TN), Italy

    Luigi Lombardi

  2. Department of Developmental and Social Psychology, University of Padova, via Venezia, 8, 35131, Padova, Italy

    Massimiliano Pastore

  3. Department of General Psychology, University of Padova, via Venezia, 8, 35131, Padova, Italy

    Massimo Nucci

  4. Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, via Venezia, 8, 35131, Padova, Italy

    Andrea Bobbio

Authors
  1. Luigi Lombardi
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  2. Massimiliano Pastore
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  3. Massimo Nucci
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  4. Andrea Bobbio
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Correspondence to Luigi Lombardi.

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Lombardi, L., Pastore, M., Nucci, M. et al. SGR Modeling of Correlational Effects in Fake Good Self-report Measures. Methodol Comput Appl Probab 17, 1037–1055 (2015). https://doi.org/10.1007/s11009-014-9427-2

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  • DOI: https://doi.org/10.1007/s11009-014-9427-2

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