Fong Ted C T, Ho Rainbow T H
Centre on Behavioral Health, The University of Hong Kong.
J Occup Health. 2015;57(4):353-8. doi: 10.1539/joh.15-0057-OA. Epub 2015 May 8.
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.
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).
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.
The BSEM results demonstrate empirical support for the 1-factor model as a parsimonious and reasonable representation of work engagement.
本研究旨在使用最大似然（ML）方法和贝叶斯结构方程模型（BSEM）方法，重新检验广泛使用的9项乌得勒支工作投入量表的维度。
使用验证性因子分析和BSEM，在1112名医护人员的两个分割样本中评估了三个测量模型（单因素、三因素和双因素模型），其中为交叉负荷和残差协方差指定了小方差信息先验。通过后验预测p值（PPP）、偏差信息准则和贝叶斯信息准则（BIC）评估模型拟合和比较。
三个基于ML的模型均未显示出对数据的充分拟合。对交叉负荷使用信息先验并未改善模型的PPP。具有近似零残差协方差的单因素BSEM模型对两个样本均显示出良好的拟合（PPP>0.10），并且其BIC明显低于其三因素和双因素对应模型。
BSEM结果为单因素模型作为工作投入的简约且合理表示提供了实证支持。
