A Multidimensional Unfolding Latent Trait Model for Binary Data
28 Pages Posted: 24 Sep 2007
Date Written: November 2, 2005
Abstract
We introduce a multidimensional latent trait model for binary data with non-monotone item response functions. We assume that the conditional probability of endorsing an item is a normal probability density function, and that the latent traits are normally distributed. The model yields closed form expressions for the moments of the multivariate Bernoulli (MVB) distribution. As a result, cell probabilities can be computed also in closed form, regardless of the dimensionality of the latent traits. The model is an ideal point model in the sense that a respondent - precisely at the ideal point (the mode of the item response function) - endorses the item with probability one.
Keywords: item response theory, categorical data analysis
JEL Classification: C00
Suggested Citation: Suggested Citation
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