# Can a general factor be derived from employees’ responses to items on the Individual Work Performance Review?

## TL;DR

This study shows that a general performance factor can be derived from employee reviews in South Africa, explaining most of the variance in their responses.

## Contribution

The study provides initial evidence for a general performance factor in a South African context using advanced statistical methods.

## Key findings

- A general factor explained 65% of the common variance in 80 IWPR items.
- Job level correlated with general job performance, but tenure did not.
- An overall performance score could help identify top performers and evaluate training ROI.

## 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.

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.