Abstract

Multilevel modeling (MLM) is widely used for the multilevel data structure in social science. Two MLM limitations involve partitioning variance for a within-level predictor. First, MLM does not automatically partial out the between-level variance for a within-level predictor. Second, MLM assumes a within-level predictor to be measurement-error free. Thus, the error variance cannot be separated out. The study evaluated the impact of improperly partitioning two-level variances and measurement error variances for a within-level predictor in MLM. Our findings suggested (a) whether the research interest lies in a within-level or a between-level predictor, group-mean centering or latent-mean centering must be used to partition two-level variances; (b) for the measurement error variance, the within-level factor loadings should be as high as possible (≥0.80 in our simulation settings). If these two requirements are not met, multilevel structural equation modeling (MSEM) should always be adopted for the analysis.

摘要

多级建模（MLM）广泛用于社会科学中的多级数据结构。两个 MLM 限制涉及级别内预测变量的分区方差。首先，MLM 不会自动将级别内预测变量的级别间方差分出。其次，MLM 假设层内预测器是无测量误差的。因此，无法分离出误差方差。该研究评估了对 MLM 中的级别内预测器不正确划分两级方差和测量误差方差的影响。我们的研究结果表明（a）无论研究兴趣在于水平内预测还是水平间预测，必须使用组均值居中或潜在均值居中来划分两级方差；(b) 对于测量误差方差，水平内因子载荷应尽可能高（≥0. 在我们的模拟设置中为 80）。如果不满足这两个要求，则应始终采用多级结构方程建模 (MSEM) 进行分析。