Deep Neural Networks for Detecting Statistical Model Misspecifications. The Case of Measurement Invariance

TL;DR

This paper introduces a novel Deep Neural Network approach for detecting local measurement invariance misspecifications, outperforming traditional methods in simulations and real data applications.

Contribution

The study develops and validates a DNN-based method for identifying measurement invariance issues, offering a more accurate alternative to existing traditional techniques.

Findings

DNN outperforms traditional methods in simulation scenarios

DNN accurately detects miss-translated items in real data

Proposed approach is at least as accurate as best traditional methods

Abstract

While in recent years a number of new statistical approaches have been proposed to model group differences with a different assumption on the nature of the measurement invariance of the instruments, the tools for detecting local misspecifications of these models have not been fully developed yet. In this study, we present a novel approach using a Deep Neural Network (DNN). We compared the proposed model with the most popular traditional methods: Modification Indices (MI) and Expected Parameter Change (EPC) indicators from the Confirmatory Factor Analysis (CFA) modeling, logistic DIF detection, and sequential procedure introduced with the CFA alignment approach. Simulation studies show that the proposed method outperformed traditional methods in almost all scenarios, or it was at least as accurate as the best one. We also provide an empirical example utilizing European Social Survey data including items known to be miss-translated, which are correctly identified with presented DNN approach.