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

Authors:
Liang Ting Tsai;Chih-Chien Yang
Affiliations:
Cognitive NeuroMetrics Laboratory, Graduate Institute of Educational Measurement & Statistics, National Taichung University of Education, 140 MingSheng Road, Taichung 403, Taiwan;Cognitive NeuroMetrics Laboratory, Graduate Institute of Educational Measurement & Statistics, National Taichung University of Education, 140 MingSheng Road, Taichung 403, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 5
Cited 0

Statistical analysis with missing data

Statistical analysis with missing data
A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests

Expert Systems with Applications: An International Journal
Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network

Expert Systems with Applications: An International Journal
Chest diseases diagnosis using artificial neural networks

Expert Systems with Applications: An International Journal
Supervised learning vector quantization for projecting missing weights of hierarchical neural networks

WSEAS Transactions on Information Science and Applications

Quantified Score

Hi-index	12.05

Visualization

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.