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

Abstract

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. high scores). Importantly, whereas past research has focused on faking by examining test scores, recent advances have suggested that the faking process could be better understood by analyzing individuals’ responses at the item level (response pattern). Using machine learning (elastic net and random forest regression), we reanalyzed a data set ( N = 260) to investigate whether individuals’ faked response patterns on extraversion (features; i.e., input variables) could reveal their IM and SM scores. We found that individuals had similar response patterns when they faked, irrespective of their IM scores (excluding the faking of high scores when random forest regression was used). Elastic net and random forest regression converged in revealing that individuals higher on SM differed from individuals lower on SM in how they faked. Thus, response patterns were able to reveal individuals’ SM, but not IM. Feature importance analyses showed that whereas some items were faked differently by individuals with higher versus lower SM scores, others were faked similarly. Our results imply that analyses of response patterns offer valuable new insights into the faking process.

Impression management in daily life: an experience sampling test for the expression of impression management as interpersonally oriented self-control

“…IM may carry (substantive) adaptive rather than maladaptive qualities and removing variance associated with IM (or participants scoring high on IM scales) from analyses (or screening processes) is likely to reduce the quality of the measurement. The present findings are in line with and extend recent approaches (e.g., Ones et al, 1996;Uziel, 2010bUziel, , 2014MacCann et al, 2012;De Vries et al, 2014;Connelly and Chang, 2016;Müller and Moshagen, 2019;Röhner et al, 2023)…”