General multi-level modeling with sampling weights

被引：206

作者：

Asparouhov, T ^{[1
]}

机构：

[1] Muthen & Muthen, Los Angeles, CA 90066 USA

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2006年 / 35卷 / 03期

关键词：

informative selection; multi-level mixture models; multi-level models; multi-level pseudo maximum likelihood; sampling weights; weights scaling;

D O I：

10.1080/03610920500476598

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this article we study the approximately unbiased multi-level pseudo maximum likelihood (MPML) estimation method for general multi-level modeling with sampling weights. We conduct a simulation study to determine the effect various factors have on the estimation method. The factors we included in this study are scaling method, size of clusters, invariance of selection, informativeness of selection, intraclass correlation, and variability of standardized weights. The scaling method is an indicator of how the weights are normalized on each level. The invariance of the selection is an indicator of whether or not the same selection mechanism is applied across clusters. The informativeness of the selection is an indicator of how biased the selection is. We summarize our findings and recommend a multi-stage procedure based on the MPML method that can be used in practical applications.

引用

页码：439 / 460

页数：22

共 15 条

[11] Weighting for unequal selection probabilities in multilevel models [J].