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Original Research
Dimensionality of the UWES-17: An item response modelling analysis
Submitted: 08 May 2013 | Published: 23 October 2013
About the author(s)
Deon P. de Bruin, Department of Industrial Psychology and People Management, University of Johannesburg, South AfricaCarin Hill, Department of Industrial Psychology and People Management, University of Johannesburg
Carolina M. Henn, Department of Industrial Psychology and People Management, University of Johannesburg
Klaus-Peter Muller, Department of Industrial Psychology and People Management, University of Johannesburg
Abstract
Research purpose: The main focus of this study was to use the Rasch model to provide insight into the dimensionality of the UWES-17, and to assess whether work engagement should be interpreted as one single overall score, three separate scores, or a combination.
Motivation for the study: It is unclear whether a summative score is more representative of work engagement or whether scores are more meaningful when interpreted for each dimension separately. Previous work relied on confirmatory factor analysis; the potential of item response models has not been tapped.
Research design: A quantitative cross-sectional survey design approach was used. Participants, 2429 employees of a South African Information and Communication Technology (ICT) company, completed the UWES-17.
Main findings: Findings indicate that work engagement should be treated as a unidimensional construct: individual scores should be interpreted in a summative manner, giving a single global score.
Practical/managerial implications: Users of the UWES-17 may interpret a single, summative score for work engagement. Findings of this study should also contribute towards standardising UWES-17 scores, allowing meaningful comparisons to be made.
Contribution/value-add: The findings will benefit researchers, organisational consultants and managers. Clarity on dimensionality and interpretation of work engagement will assist researchers in future studies. Managers and consultants will be able to make better-informed decisions when using work engagement data.
Keywords
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