The multilevel latent covariate model:: A new, more reliable approach to group-level effects in contextual studies
The multilevel latent covariate model:: A new, more reliable approach to group-level effects in contextual studies
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DOI：
10.1037/a0012869
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发表时间：
2008-09-01
影响因子：
7
通讯作者：
Muthen, Bengt
中科院分区：
文献类型：
--
作者：
Luedtke, Oliver;Marsh, Herbert W.;Muthen, Bengt
在多层次建模（MLM）中，群体水平（L2）特征通常通过汇总每个群体内的个人水平（L1）特征来衡量，从而评估情境效应（例如，社会经济地位、成就、气候的群体平均效应）。大多数以前的应用都使用了多层显变量（MMC）方法，其中假设观察到的（显变量）组均值是完全可靠的。本文用数学方法和模拟结果证明，这种MMC方法可能会导致对上下文效应的严重偏差估计，并可能严重低估相关的标准误差，这取决于每组L I个体的数量、组的数量、类内相关性、抽样比率（每组抽样中病例的百分比）和数据的性质。为了解决这个普遍存在的问题，作者引入了一种新的多水平潜在协变量（MLC）方法，该方法纠正了L2的不可靠性，并在适当的条件下对L2结构进行无偏估计。然而，在某些情况下，当采样比接近100%时，MMC方法提供了更准确的估计。基于3个模拟和2个实际数据应用，作者评估了MMC和MLC方法，并建议研究人员何时应该最适当地使用一种方法，另一种方法，或两种方法的组合。
In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach, in which the observed (manifest) group mean is assumed to be perfectly reliable. This article demonstrates mathematically and with simulation results that this MMC approach can result in substantially biased estimates of contextual effects and can substantially underestimate the associated standard errors, depending on the number of L I individuals per group, the number of groups, the intraclass correlation, the sampling ratio (the percentage of cases within each group sampled), and the nature of the data. To address this pervasive problem, the authors introduce a new multilevel latent covariate (MLC) approach that corrects for unreliability at L2 and results in unbiased estimates of L2 constructs under appropriate conditions. However, under some circumstances when the sampling ratio approaches 100%, the MMC approach provides more accurate estimates. Based on 3 simulations and 2 real-data applications, the authors evaluate the MMC and MLC approaches and suggest when researchers should most appropriately use one, the other, or a combination of both approaches.