A Multilevel Weighted Bootstrap Procedure to Handle Sampling Weights

• In Event: Statistical Methods for Complex Multilevel Structures and Data Types

Abstract

Multilevel modeling is often used to analyze survey data collected with a multistage sampling design. When the selection is informative, sampling weights need to be incorporated in the estimation. We propose a weighted residual bootstrap method as an alternative to the multilevel pseudo-maximum likelihood (MPML) estimators. In a Monte Carlo experiment, the bootstrap method showed advantages over MPML when sample size is small, selection mechanism is informative, intraclass correlation is low, and distributions are non-normal. As an illustration, the bootstrap method is applied to the American PISA 2000 survey data on math achievement using the R package bootmlm, and the results will be presented in the final paper.

Authors

• Wen Luo, Texas A&M University - College Station

• Hok Chio (Mark) Lai, University of Southern California