Croon Marcel A, van Veldhoven Marc J P M
Department of Statistics and Methodology, Faculty of Social Sciences, Tilburg University, Tilburg, Netherlands.
Psychol Methods. 2007 Mar;12(1):45-57. doi: 10.1037/1082-989X.12.1.45.
In multilevel modeling, one often distinguishes between macro-micro and micro-macro situations. In a macro-micro multilevel situation, a dependent variable measured at the lower level is predicted or explained by variables measured at that lower or a higher level. In a micro-macro multilevel situation, a dependent variable defined at the higher group level is predicted or explained on the basis of independent variables measured at the lower individual level. Up until now, multilevel methodology has mainly focused on macro-micro multilevel situations. In this article, a latent variable model is proposed for analyzing data from micro-macro situations. It is shown that regression analyses carried out at the aggregated level result in biased parameter estimates. A method that uses the best linear unbiased predictors of the group means is shown to yield unbiased estimates of the parameters.
在多层模型中，人们通常区分宏观-微观和微观-宏观情况。在宏观-微观多层情况下，较低层次测量的因变量由该较低层次或更高层次测量的变量进行预测或解释。在微观-宏观多层情况下，在较高组层次定义的因变量基于在较低个体层次测量的自变量进行预测或解释。到目前为止，多层方法主要集中在宏观-微观多层情况。本文提出了一种用于分析微观-宏观情况数据的潜在变量模型。结果表明，在聚合层次进行的回归分析会导致参数估计有偏差。一种使用组均值的最佳线性无偏预测器的方法被证明能产生无偏的参数估计。
