The Accuracy of Significance Tests for Slope Variance Components in Multilevel Random Coefficient Models
通过蒙特卡洛模拟，比较了三种检验多水平随机系数模型中斜率方差显著性的方法，发现单侧似然比检验在统计功效和第一类错误之间取得最佳平衡，并给出了分析建议。
This study examines the behavior of three tests for significant slope variance in multilevel random coefficient (MRC) models: the Hierarchical Linear Modeling chi-square test, the likelihood ratio test (LRT), and the corrected LRT. Monte Carlo simulations are conducted varying the numbers of groups, group size, and effect size. Results suggest that neither the number of groups nor group size influenced Type I errors. Group size has a stronger effect on power compared with the number of groups. The one-tailed LRT demonstrates the best balance between power and Type I errors. Recommendations for conducting MRC analyses are presented.