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首页> 外文期刊>Educational and Psychological Measurement >Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations
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Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations

机译：多层次验证性因素分析中的共线潜在变量：最大似然和贝叶斯估计的比较

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摘要

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation coefficient (ICC) and estimation method; maximum likelihood estimation with robust chi-squares and standard errors and Bayesian estimation, on the convergence rate are investigated. The other variables of interest were rate of inadmissible solutions and the relative parameter and standard error bias on the between level. The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood. In the within level multicollinearity condition, all of the solutions were admissible but the bias values were higher compared with the between level collinearity condition. Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation. Finally, as expected, high ICC produced less biased results compared to medium ICC conditions.
机译：因为变量在社会科学和行为科学中可能是相关的，所以多重共线性可能是有问题的。这项研究调查了蒙特卡洛模拟法在两级确认因子分析的水平之内和之间对共线性的影响。此外，类内相关系数（ICC）的大小和估计方法的影响;研究了具有鲁棒卡方和标准误差的最大似然估计以及贝叶斯估计对收敛速度的影响。感兴趣的其他变量是不可接受的解决方案的速率以及相对参数和水平之间的标准误差偏差。结果表明，在水平共线性之间，且估计方法为最大似然时，得到了不可接受的解。在级别内多重共线性条件下，所有解均可接受，但与级别之间共线性条件相比，偏差值更高。贝叶斯估计在获得可接受的参数方面似乎很健壮，但是相对偏差比最大似然估计要高。最后，正如预期的那样，与中等ICC条件相比，高ICC产生的偏差较小。

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