Competing Confirmatory Factor Analysis Models in Management Research: Bifactor Modeling of the Employee Work Assessment Tool

Theophilus Ehidiamen Oamen
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Theophilus Ehidiamen Oamen: Texila American University, https://orcid.org/0000-0002-5114-064X

Management Dynamics in the Knowledge Economy, 2024, vol. 12, issue 2, 101-115

Abstract: Management researchers often use structural equation modeling to analyze data from questionnaire-based instruments. Usually, confirmatory factor analysis (CFA) is applied to confirm the hypothesized or theorized factor structure of the instrument. Most authors adopt a single CFA model without comparing it against other potentially valid models (general factor, correlated factor model, second-order hierarchical model, and bifactor model). Hence, the dimensionality and reliability of constructs using bifactor modeling to validate latent scores are often ignored. Also, this gap is widened by no unanimous agreement on the use of post hoc modification of CFA models to support fit to the data in covariance-based structural equation modeling (CB-SEM). The objective of the study was to explore model fit, dimensionality, and reliability of the Employee Work Assessment Tool (EWAT) using competing CFA models. The study used a published dataset on the EWAT instrument to illustrate the assessment of the dimensionality and model-based reliability of the tool using CB-SEM. Results showed that CFA statistics of the bifactor model were most adequate for the instrument (à ‡2=70.053, df=19, RMSEA=0.082 [90% confidence interval; 0.062, 0.103], SRMR=0.036, CFI=0.963). The bifactor model ancillary measures supported the unidimensional structure of EWAT with justification for the use of total scores. The study concludes that the instrument is best described and applied as a unidimensional construct, and therefore, a single score can be used to rate employees’ perceptions of their work conditions. The study presents both practical implications for management researchers and simplified reporting for bifactor modelling.

Date: 2024
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