Modeling Ordinal Survey Data with Unfolding Models

TL;DR

This paper introduces a flexible ordinal probit unfolding model for survey data that can handle both monotonic and non-monotonic response functions, improving analysis of preference data.

Contribution

It proposes a novel unfolding model for ordinal survey data that relaxes the monotonicity assumption of traditional latent factor models.

Findings

Model effectively captures non-monotonic response patterns.

Application to immigration survey demonstrates model's practical utility.

Outperforms traditional models in flexibility and fit.

Abstract

Surveys that rely on ordinal polychotomous (Likert-like) items are widely employed to capture individual preferences because they allow respondents to express both the direction and strength of their preferences. Latent factor models traditionally used in this context implicitly assume that the response functions (the cumulative distribution of the ordinal outcome) are monotonic on the latent trait. This assumption can be too restrictive in several application areas, including in political science and marketing. In this work, we propose a novel ordinal probit unfolding model that can accommodate both monotonic and non-monotonic response functions. The advantages of the model are illustrated by analyzing an immigration attitude survey conducted in the United States.