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General multi-level modeling with sampling weights. (English) Zbl 1084.62053
Summary: We study approximately unbiased multi-level pseudo maximum likelihood (MPML) estimation methods 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.

MSC:
62H99 Multivariate analysis
62D05 Sampling theory, sample surveys
62H12 Estimation in multivariate analysis
Software:
Mplus
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