Testing For Informative Weights And Weights Trimming In Multivariate Modeling With Survey Data

Analyzing the informativeness of the sampling weights can lead to significant improvement in the precision of model estimation with survey data. A test for weights ignorability was proposed in Pfeffermann’s (1993). We propose a modification of this test which improves its performance for small and medium sample size problems. We also generalize the test to a test of equivalence between two different sets of sampling weights, which can be used to test the informativeness of individual weight components. We evaluate the performance of these techniques in simulation studies based on linear regression and multivariate factor analysis models. We also apply the test of equivalence to the problem of finding the optimal level of weight trimming and illustrate this approach with a practical example. We describe the implementation of these techniques in the software package Mplus.