Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach
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- Fong Ted C. T.
- Centre on Behavioral Health, The University of Hong Kong
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- Ho Rainbow T. H.
- Centre on Behavioral Health, The University of Hong Kong Department of Social Work & Social Administration, The University of Hong Kong
Bibliographic Information
- Other Title
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- Dimensionality of the 9‐item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach
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Abstract
Objectives: The aim of this study was to reexamine the dimensionality of the widely used 9-item Utrecht Work Engagement Scale using the maximum likelihood (ML) approach and Bayesian structural equation modeling (BSEM) approach. Methods: Three measurement models (1-factor, 3-factor, and bi-factor models) were evaluated in two split samples of 1,112 health-care workers using confirmatory factor analysis and BSEM, which specified small-variance informative priors for cross-loadings and residual covariances. Model fit and comparisons were evaluated by posterior predictive p-value (PPP), deviance information criterion, and Bayesian information criterion (BIC). Results: None of the three ML-based models showed an adequate fit to the data. The use of informative priors for cross-loadings did not improve the PPP for the models. The 1-factor BSEM model with approximately zero residual covariances displayed a good fit (PPP>0.10) to both samples and a substantially lower BIC than its 3-factor and bi-factor counterparts. Conclusions: The BSEM results demonstrate empirical support for the 1-factor model as a parsimonious and reasonable representation of work engagement.(J Occup Health 2015; 57: 353–358)
Journal
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- Journal of Occupational Health
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Journal of Occupational Health 57 (4), 353-358, 2015
Japan Society for Occupational Health
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Details 詳細情報について
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- CRID
- 1390001204454439296
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- NII Article ID
- 40020547420
- 130005106130
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- NII Book ID
- AA11090645
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- ISSN
- 13489585
- 13419145
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- NDL BIB ID
- 026633910
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- PubMed
- 25958976
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- PubMed
- CiNii Articles
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- Abstract License Flag
- Disallowed