On Identification and Non-normal Simulation in Ordinal Covariance and Item Response Models

Njål Foldnes () and Steffen Grønneberg ()
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Njål Foldnes: BI Norwegian Business School
Steffen Grønneberg: BI Norwegian Business School

Psychometrika, 2019, vol. 84, issue 4, No 4, 1000-1017

Abstract: Abstract A standard approach for handling ordinal data in covariance analysis such as structural equation modeling is to assume that the data were produced by discretizing a multivariate normal vector. Recently, concern has been raised that this approach may be less robust to violation of the normality assumption than previously reported. We propose a new perspective for studying the robustness toward distributional misspecification in ordinal models using a class of non-normal ordinal covariance models. We show how to simulate data from such models, and our simulation results indicate that standard methodology is sensitive to violation of normality. This emphasizes the importance of testing distributional assumptions in empirical studies. We include simulation results on the performance of such tests.

Keywords: simulation; ordinal data; structural equation models; polychoric correlations; IRT (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11336-019-09688-z

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