Original Research
Can a general factor be derived from employees’ responses to items on the Individual Work Performance Review?
Submitted: 02 December 2022 | Published: 22 January 2024
About the author(s)
Xander van Lill, Department of Industrial Psychology and People Management, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa; and, Department of Product and Research, JVR Africa Group, Johannesburg, South Africa; and, Consulting, Peter Berry Consultancy, Sydney, AustraliaLeoni van der Vaart, Opentia Research Unit, School of Industrial Psychology and Human Resource Management, Economic and Management Sciences, North-West University, Vanderbijlpark, South Africa; and, Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
Abstract
This study aimed to investigate whether permissible inferences can be derived from employees’ standing on a general performance factor from their responses to the Individual Work Performance Review (IWPR) items. The performance of 448 employees was rated (by their managers) using the IWPR. Latent variable modelling was performed through a bifactor exploratory structural equation model with the robust version of the maximum likelihood estimator. The general factor’s score was also used to inspect correlations with two work performance correlates: tenure and job level. In line with international findings, the results suggested that a general factor could explain 65% of the common variance in the 80 items of the IWPR. Job level, but not tenure, correlated with general job performance. The results support calculating an overall score for performance, which might be a suitable criterion to differentiate top performers, conduct criterion validity studies, and calculate the return on investment of selection procedures or training programmes.
Contribution: The present study provides initial evidence for a general factor influencing employees’ responses to items on a generic performance measure in South Africa. In addition, the study showcases the application of advanced statistical methods in factor analyses, demonstrating their efficacy in evaluating the psychometric properties of hierarchical factor models derived from data provided on performance measures.
Keywords
Sustainable Development Goal
Metrics
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