Improving measurement invariance assessments in survey research with missing data by novel artificial neural networks

Abstract

This study proposes the learning vector quantization estimated stratum weight (LVQ-ESW) method to interpolate missing group membership and weights in identifying the accuracy of measurement invariance (MI) in a stratified sampling survey. Survey data is rife with missing information, such as gender and race, which is critical for identifying MI, and in ensuring that conclusions from large-scale testing campaigns are accurate. In the current study, simulations were conducted to examine the accuracy and consistency of MI detection using multiple-group confirmatory factor analysis (MG-CFA) to compare different approaches for interpolating missing information. The results of the computerized simulations showed that the proposed method outperformed traditional methods, such as List-wise deletion, in terms of accurately and stably identifying MI. The implications for interpolating missing group membership and weights for survey research are discussed. (C) 2012 Elsevier Ltd. All rights reserved.