Hidden Markov Models for Longitudinal Rating Data with Dynamic Response Styles

TL;DR

This paper introduces a hidden Markov model that jointly captures the evolution of a latent trait and response styles in longitudinal ordinal data, providing a nuanced analysis of behavioral and substantive changes over time.

Contribution

It proposes a novel HMM framework with two latent components to model both latent traits and response styles simultaneously in longitudinal ordinal data.

Findings

Model applied to Italian household data revealing trends in financial capability.

Method effectively separates substantive trait changes from response style influences.

Provides comprehensive estimation and diagnostic tools for the proposed HMM.

Abstract

This work deals with the analysis of longitudinal ordinal responses. The novelty of the proposed approach is in modeling simultaneously the temporal dynamics of a latent trait of interest, measured via the observed ordinal responses, and the answering behaviors influenced by response styles, through hidden Markov models (HMMs) with two latent components. This approach enables the modeling of (i) the substantive latent trait, controlling for response styles; (ii) the change over time of latent trait and answering behavior, allowing also dependence on individual characteristics. For the proposed HMMs, estimation procedures, methods for standard errors calculation, measures of goodness of fit and classification, and full-conditional residuals are discussed. The proposed model is fitted to ordinal longitudinal data from the Survey on Household Income and Wealth (Bank of Italy) to give insights on the evolution of the Italian households financial capability.