Evaluating Bifactor Models: Calculating and Interpreting Statistical Indices
Evaluating Bifactor Models: Calculating and Interpreting Statistical Indices
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DOI：
10.1037/met0000045
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发表时间：
2016-06-01
影响因子：
7
通讯作者：
Haviland, Mark G.
中科院分区：
文献类型：
--
作者：
Rodriguez, Anthony;Reise, Steven P.;Haviland, Mark G.
双因素测量模型越来越多地应用于人格和精神病理学测量（Reise，2012）。在这项工作中，作者一般都强调模型拟合，他们的典型结论是，一个双因素模型提供了一个上级适合相对于其他从属模型。然而，通常未被探索的是重要的统计指标，这些指标可以大大改善对测量的心理测量分析。我们提供了一个审查的特别有价值的统计指标，可以从双因子模型。它们包括欧米茄可靠性系数，因素确定性，结构可靠性，解释共同方差，和未污染的相关性的百分比。我们描述了如何计算这些指数，并用于通知：（a）单位加权总分和分量表得分复合材料的质量，以及因子得分估计，（B）结构方程模型中测量模型的规格和质量。
Bifactor measurement models are increasingly being applied to personality and psychopathology measures (Reise, 2012). In this work, authors generally have emphasized model fit, and their typical conclusion is that a bifactor model provides a superior fit relative to alternative subordinate models. Often unexplored, however, are important statistical indices that can substantially improve the psychometric analysis of a measure. We provide a review of the particularly valuable statistical indices one can derive from bifactor models. They include omega reliability coefficients, factor determinacy, construct reliability, explained common variance, and percentage of uncontaminated correlations. We describe how these indices can be calculated and used to inform: (a) the quality of unit-weighted total and subscale score composites, as well as factor score estimates, and (b) the specification and quality of a measurement model in structural equation modeling.