Robust Estimation of Polychoric Correlation

TL;DR

This paper introduces a robust estimator for polychoric correlation that remains reliable under model misspecification, improving accuracy in rating data analysis and identifying careless respondents.

Contribution

A novel robust estimator for polychoric correlation that does not assume specific misspecification types, generalizes ML, and is computationally efficient.

Findings

Estimator is robust against model misspecification.

Simulation studies confirm improved robustness.

Empirical application detects careless respondents.

Abstract

Polychoric correlation is often an important building block in the analysis of rating data, particularly for structural equation models. However, the commonly employed maximum likelihood (ML) estimator is highly susceptible to misspecification of the polychoric correlation model, for instance through violations of latent normality assumptions. We propose a novel estimator that is designed to be robust against partial misspecification of the polychoric model, that is, when the model is misspecified for an unknown fraction of observations, such as careless respondents. To this end, the estimator minimizes a robust loss function based on the divergence between observed frequencies and theoretical frequencies implied by the polychoric model. In contrast to existing literature, our estimator makes no assumption on the type or degree of model misspecification. It furthermore generalizes ML estimation, is consistent as well as asymptotically normally distributed, and comes at no additional computational cost. We demonstrate the robustness and practical usefulness of our estimator in simulation studies and an empirical application on a Big Five administration. In the latter, the polychoric correlation estimates of our estimator and ML differ substantially, which, after further inspection, is likely due to the presence of careless respondents that the estimator helps identify.