Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12857/116021
Title: Empirical Scenarios of Fake Data Analysis: The Sample Generation by Replacement (SGR) Approach
Authors: Pastore, Massimiliano 
Nucci, Massimo 
Bobbio, Andrea 
Lombardi, Luigi
Keywords: fake data;monte carlo;sample generation by replacement;scenario-based methodology;self-report measures
Issue Date: 2017
Journal: Frontiers in psychology 
Abstract: 
Many self-report measures of attitudes, beliefs, personality, and pathology include items whose responses can be easily manipulated or distorted, as an example in order to give a positive impression to others, to obtain financial compensation, to avoid being charged with a crime, to get a job, or else. This fact confronts both researchers and practitioners with the crucial problem of biases yielded by the usage of standard statistical models. The current paper presents three empirical applications to the issue of faking of a recent probabilistic perturbation procedure called Sample Generation by Replacement (SGR; Lombardi and Pastore, 2012). With the intent to study the behavior of some statistics under fake perturbation and data reconstruction processes, ad-hoc faking scenarios were implemented and tested. Overall, results proved that SGR could be successfully applied both in the case of research designs traditionally proposed in order to deal with faking (e.g., use of fake-detecting scales, experimentally induced faking, or contrasting applicants vs. incumbents), and in the case of ecological research settings, where no information as regards faking could be collected by the researcher or the practitioner. Implications and limitations are presented and discussed.
URI: http://hdl.handle.net/20.500.12857/116021
ISSN: 1664-1078
DOI: 10.3389/fpsyg.2017.00482
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