Can People With Higher Versus Lower Scores on Impression Management or Self-Monitoring Be Identified Through Different Traces Under Faking?

According to faking models, personality variables and faking are related. Most prominently, people's tendency to try to make an appropriate impression (impression management; IM) and their tendency to adjust the impression they make (self-monitoring; SM) have been suggested to be associated with faking. Nevertheless, empirical findings connecting these personality variables to faking have been contradictory, partly because different studies have given individuals different tests to fake and different faking directions (to fake low vs.

IAT faking indices revisited: Aspects of replicability and differential validity.

Research demonstrates that IATs are fakeable. Several indices [either slowing down or speeding up, and increasing errors or reducing errors in congruent and incongruent blocks; Combined Task Slowing (CTS); Ratio 150-10000] have been developed to detect faking. Findings on these are inconclusive, but previous studies have used small samples, suggesting they were statistically underpowered.

Lying on the Dissection Table: Anatomizing Faked Responses.

Research has shown that even experts cannot detect faking above chance, but recent studies have suggested that machine learning may help in this endeavor. However, faking differs between faking conditions, previous efforts have not taken these differences into account, and faking indices have yet to be integrated into such approaches. We reanalyzed seven data sets (N = 1,039) with various faking conditions (high and low scores, different constructs, naïve and informed faking, faking with and without practice, different measures [self-reports vs.

Challenging response latencies in faking detection: The case of few items and no warnings.

AbstractFaking detection is an ongoing challenge in psychological assessment. A notable approach for detecting fakers involves the inspection of response latencies and is based on the congruence model of faking. According to this model, respondents who fake good will provide favorable responses (i.

A Diffusion Model Approach for Understanding the Impact of 17 Interventions on the Race Implicit Association Test.

Performance on implicit measures reflects construct-specific and nonconstruct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: Change in performance could reflect changes in the constructs of interest or changes in other mental processes. We reanalyzed data from six studies ( = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race implicit association test (IAT) performance.