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

Jessica Röhner, Philipp Thoss, Liad Uziel

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)594-631
Number of pages38
JournalEducational and Psychological Measurement
Volume84
Issue number3
DOIs
StatePublished - Jun 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partly funded by a grant from the equal opportunities office at the University of Bamberg. The funding source had no involvement in the study design or analyses.

FundersFunder number
University of Bamberg

    Keywords

    • faking
    • impression management
    • machine learning
    • response patterns
    • self-monitoring

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