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首页> 外文期刊>Structural equation modeling >Comparing Multilevel and Classical Confirmatory Factor Analysis Parameterizations of Multirater Data: A Monte Carlo Simulation Study
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Comparing Multilevel and Classical Confirmatory Factor Analysis Parameterizations of Multirater Data: A Monte Carlo Simulation Study

机译：多评分者数据的多级和经典验证性因子分析参数化比较：蒙特卡洛模拟研究

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摘要

This simulation study assesses the statistical performance of two mathematically equivalent parameterizations for multitrait-multimethod data with interchangeable ratersa multilevel confirmatory factor analysis (CFA) and a classical CFA parameterization. The sample sizes of targets and raters, the factorial structure of the trait factors, and rater missingness are varied. The classical CFA approach yields a high proportion of improper solutions under conditions with small sample sizes and indicator-specific trait factors. In general, trait factor related parameters are more sensitive to bias than other types of parameters. For multilevel CFAs, there is a drastic bias in fit statistics under conditions with unidimensional trait factors on the between level, where root mean square error of approximation (RMSEA) and 2 distributions reveal a downward bias, whereas the between standardized root mean square residual is biased upwards. In contrast, RMSEA and 2 for classical CFA models are severely upwardly biased in conditions with a high number of raters and a small number of targets.
机译：这项模拟研究使用可互换的评分器，多级确认因子分析（CFA）和经典CFA参数化评估了多特征多方法数据的两个数学等效参数化的统计性能。目标和评估者的样本大小，特征因子的阶乘结构以及评估者缺失是变化的。经典CFA方法在小样本量和特定指标特征因子的条件下会产生很大比例的不正确溶液。通常，与性状因子相关的参数比其他类型的参数对偏差更敏感。对于多级CFA，在水平之间具有一维特征因子的条件下，拟合统计量存在严重偏差，其中近似均方根误差（RMSEA）和2分布显示出向下偏差，而标准均方根残差为向上偏。相比之下，经典CFA模型的RMSEA和2在有大量评估者和少量目标的情况下严重向上偏移。

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