In the context of Bayesian factor analysis, it is possible to compute mean plausible values, which might be used as covariates or predictors or in order to provide individual scores for the Bayesian latent variables. Previous simulation studies ascertained the validity of the plausible values by the mean squared difference of the plausible values and the generating factor scores. However, the generating factor scores are unknown in empirical studies so that an indicator that is solely based on model parameters is needed in order to evaluate the validity of factor score estimates in empirical studies. The coefficient of determinacy is based on model parameters and can be computed whenever Bayesian factor analysis is performed in empirical settings. Therefore, the central aim of the present simulation study was to compare the coefficient of determinacy based on model parameters with the correlation of mean plausible values with the generating factors. It was found that the coefficient of determinacy yields an acceptable estimate for the validity of mean plausible values. As for small sample sizes and a small salient loading size the coefficient of determinacy overestimates the validity, it is recommended to report the coefficient of determinacy together with a bias-correction in order to estimate the validity of mean plausible values in empirical settings.
翻译:在Bayesian系数分析中,有可能计算出可能作为共变或预测器或为提供Bayesian潜伏变量的个别分数而使用的表面合理值; 先前的模拟研究通过合理值和生成系数分数的正方差平均值确定合理值的有效性; 然而,在经验研究中,产生系数的分数并不为人所知,因此需要一个完全以模型参数为基础的指标来评价经验研究中系数分数估计系数的有效性; 确定系数以模型参数为基础,在经验环境中进行Bayesian系数分析时可以计算。 因此,目前模拟研究的中心目的是比较基于模型参数的确定系数与平均合理值与生成系数的相互关系; 发现确定系数对合理值值的有效性得出了可接受的估计值; 对于小样本大小和小突出的装载大小,确定系数高估了有效性,建议报告确定系数,同时报告判断误差系数,以便估计平均数值在实际假设中的有效性。