Yang Yanyun, Xia Yan
Florida State University, Tallahassee, FL, USA.
Arizona State University, Tempe, AZ, USA.
Educ Psychol Meas. 2019 Feb;79(1):19-39. doi: 10.1177/0013164417752008. Epub 2018 Jan 18.
When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a substantially biased estimate of categorical omega. In this study, we applied Bayesian estimation methods for computing categorical omega. The simulation study investigated the performance of categorical omega under a variety of conditions through manipulating the scale length, number of response categories, distributions of the categorical variable, heterogeneities of thresholds across items, and prior distributions for model parameters. The Bayes estimator appears to be a promising method for estimating categorical omega. M and SAS codes for computing categorical omega were provided.
当项目分数为有序分类变量时，可以基于因子分析模型的参数估计，使用诸如对角加权最小二乘法等频率学派估计方法来计算分类ω系数。当样本量相对较小时，且各项目的阈值不同，使用对角加权最小二乘法会对分类ω系数产生显著有偏估计。在本研究中，我们应用贝叶斯估计方法来计算分类ω系数。模拟研究通过操纵量表长度、反应类别数量、分类变量的分布、各项目阈值的异质性以及模型参数的先验分布，考察了在各种条件下分类ω系数的表现。贝叶斯估计器似乎是估计分类ω系数的一种有前景的方法。本文还提供了计算分类ω系数的M和SAS代码。
