Investigating faking effects on the construct validity through the Monte Carlo simulation study

Lee, Philseok; Joo, Seang‐Hwane; Jia, Zihao

doi:10.1016/j.paid.2019.07.001

Cited by 8 publications

(7 citation statements)

References 52 publications

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“…In addition, the mean discriminant validity correlation of Big Five scales in the honest condition was 0.19, about equal to the honest condition in Study 1, whereas in the faking condition it was 0.30, considerably smaller than the 0.56 value found in the faking condition of Study 1. This finding was consistent with Lee et al (2019) such that as the magnitude of faking was reduced in the incentive design, the intercorrelation shift was noticeably smaller.…”

Section: Study 2 Resultssupporting

confidence: 92%

Using bifactor models to identify faking on Big Five questionnaires

Hendy

Krammer

Schermer

et al. 2020

Int J Selection Assessment

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To identify faking, bifactor models were applied to Big Five personality data in three studies of laboratory and applicant samples using within‐subjects designs. The models were applied to homogenous data sets from separate honest, instructed faking, applicant conditions, and to simulated applicant data sets containing random individual responses from honest and faking conditions. Factor scores from the general factor in a bifactor model were found to be most highly related to response condition in both types of data sets. Domain factor scores from the faking conditions were found less affected by faking in measurement of Big Five domains than summated scale scores across studies. We conclude that bifactor models are efficacious in assessing the Big Five domains while controlling for faking.

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Section: Study 2 Resultssupporting

confidence: 92%

Using bifactor models to identify faking on Big Five questionnaires

Hendy

Krammer

Schermer

et al. 2020

Int J Selection Assessment

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“…Behavior is lower in high-stake settings (see also Lee et al, 2019;Niessen et al, 2017;O'Connell et al, 2011;Topping & O'Gorman, 1997).…”

Section: Conscientiousness Scores and Counter-productive Workmentioning

confidence: 99%

“…The authors found that the predictive power was higher in the honesty condition. Likewise, Petersen and colleagues (2011) reported that the association between applicants’ Conscientiousness scores and Counter-Productive Work Behavior is lower in high-stake settings (see also Lee et al, 2019; Niessen et al, 2017; O’Connell et al, 2011; Topping & O’Gorman, 1997).…”

mentioning

confidence: 99%

Conscientiousness Can Predict Academic Performance – Even in High-Stake Setting

Danner,

Höft

2024

Journal of Individual Differences

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We investigated, to what extent Conscientiousness can predict academic performance in a real application high-stakes setting. N = 267 applicants for a place at a university completed a Conscientiousness questionnaire during the selection process and 6 weeks after they commenced their studies. Students’ academic grades were used as criterion variables. The results suggest that the high-stakes setting increases the level of Conscientiousness reported, that not all applicants change their answers to the same extent and that the high-stakes setting decreases the criterion-related validity, but Conscientiousness remains a useful predictor for academic performance in high-stakes settings.

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“…While there is evidence suggesting that this factor might not affect test-criterion correlations (Ziegler & Bühner, 2009), the variance does distort construct validity and reliability (Huber et al, 2021;Lee et al, 2019). This means that selections only based on such questionnaire data might capture unintended results.…”

Section: Social Desirability and Personnel Selectionmentioning

confidence: 99%

Forewarned is forearmed: Using AI to detect SDR tendencies as personnel selection tools

Lee¹,

Ziegler²

2021

Preprint

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Current practices of personnel selection often use questionnaires and interviews to assess candidates’ personality, but the effectiveness of both approaches can be hampered if social desirable responding (SDR) occurs. Detecting biases like SDR is important to ensure valid personnel selection for any organization, yet current instruments for assessing SDR are either inefficient or insufficient. In this paper, we propose a novel approach to appraise job applicants’ SDR tendency by employing Artificial Intelligence (AI)-based techniques. Our study extracts thousands of image and voice features from the video presentation of 91 simulated applicants to train two deep learning models for predicting their SDR tendency. The result shows that our two models, namely the Deep Image Model and Deep Voice Model, can predict SDR tendency with 82.55% and 88.89% accuracy rate, respectively. The Deep Voice Model moreover outperformed the baseline model built on a popular deep learning algorithm ResNet by 4.35%. These findings suggest that organizations can use AI driven technologies to assess job applicants’ SDR tendency during recruitment and improve the performance of their personnel selection.

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Investigating faking effects on the construct validity through the Monte Carlo simulation study

Cited by 8 publications

References 52 publications

Using bifactor models to identify faking on Big Five questionnaires

Using bifactor models to identify faking on Big Five questionnaires

Conscientiousness Can Predict Academic Performance – Even in High-Stake Setting

Forewarned is forearmed: Using AI to detect SDR tendencies as personnel selection tools

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