Assessing the Impact of Faking on Binary Personality Measures: An IRT-Based Multiple-Group Factor Analytic Procedure

The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results

Correlational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also correlational measures can be seriously affected by specific sources of bias which constitute serious threats to the final observed results. In this contribution, we will focus on the impact of the fake data threat on the interpretation of statistical results for two well-know correlational measures (the Pearson product-moment correlation and the Spearman rank-order correlation). By using the Sample Generation by Replacement (SGR) approach, we analyze uncertainty in inferences based on possible fake data and evaluate the implications of fake data for correlational results. A population-level analysis and a Monte Carlo simulation are performed to study different modulations of faking on bivariate discrete variables with finite supports and varying sample sizes. We show that by using our paradigm it is always possible, under specific faking conditions, to increase (resp. decrease) the original correlation between two discrete variables in a predictable and systematic manner.

Empirical Scenarios of Fake Data Analysis: The Sample Generation by Replacement (SGR) Approach

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.

The Psychometric Costs of Applicants' Faking: Examining Measurement Invariance and Retest Correlations Across Response Conditions

This study examines the stability of the response process and the rank-order of respondents responding to 3 personality scales in 4 different response conditions. Applicants to the University College of Teacher Education Styria (N = 243) completed personality scales as part of their college admission process. Half a year later, they retook the same personality scales in 1 of 3 randomly assigned experimental response conditions: honest, faking-good, or reproduce. Longitudinal means and covariance structure analyses showed that applicants' response processes could be partially reproduced after half a year, and respondents seemed to rely on an honest response behavior as a frame of reference. Additionally, applicants' faking behavior and instructed faking (faking-good) caused differences in the latent retest correlations and consistently affected measurement properties. The varying latent retest correlations indicated that faking can distort respondents' rank-order and thus the fairness of subsequent selection decisions, depending on the kind of faking behavior. Instructed faking (faking-good) even affected weak measurement invariance, whereas applicants' faking behavior did not. Consequently, correlations with personality scales-which can be utilized for predictive validity-may be readily interpreted for applicants. Faking behavior also introduced a uniform bias, implying that the classically observed mean raw score differences may not be readily interpreted.