Traits versus Issues: Factor versus Ideal-Point Analysis of Candidate Thermometer Ratings

Abstract

This article describes a computationally simple, statistically consistent, reasonably efficient, and statistically informative generalized least squares (GLS) estimator for a general class of nonlinear, multidimensional scaling (MDS) models including the “ideal-point” models of voters' and legislators' behavior proposed by Melvin Hinich, Keith Poole, and others. Unlike other methods, the method described in this article provides a statistical framework for testing a wide range of hypotheses about these models including their functional form, their dimensionality, and the values of specific parameters. The Hinich ideal-point model is estimated using this method. It fits the data remarkably well compared to a standard factor analysis model that does not provide a reasonable fit to the data. This has the substantive implication of suggesting that voters base their voting decisions upon ideal-point dimensions like liberalism-conservatism and not upon factor analysis dimensions like competence and leadership.