Using bifactor models to identify faking on Big Five questionnaires. (22nd November 2020)

• Record Type: Journal Article
• Authors: Hendy, Nhung; Krammer, Georg; Schermer, Julie Aitken; Biderman, Michael D.
• Is Part Of: International journal of selection and assessment. Volume 29:Number 1(2021)
• Page Start: 81
• Page End: 99
• Publication Date: 2020-11-22
• Subjects: bifactor models -- faking -- response distortion -- simulated applicant data; Employee selection -- Periodicals; Personality and occupation -- Periodicals; Employment interviewing -- Periodicals; Prediction of occupational success -- Periodicals; Employees -- Rating of -- Periodicals; 658.3112
• DOI: 10.1111/ijsa.12316
• Languages: English
• ISSNs: 0965-075X

Abstract

Abstract: 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.