Stochastic Approximation Methods for Latent Regression Item Response Models

Matthias von Davier and Sandip Sinharay

Journal of Educational and Behavioral Statistics, 2010, vol. 35, issue 2, 174-193

Abstract: This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates serving as predictors of the conditional distribution of ability. Applications to estimating latent regression models for National Assessment of Educational Progress (NAEP) data from the 2000 Grade 4 mathematics assessment and the Grade 8 reading assessment from 2002 are presented and results of the proposed method are compared to results obtained using current operational procedures.

Keywords: stochastic EM; stochastic approximation; latent regression; item response theory (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:35:y:2010:i:2:p:174-193

DOI: 10.3102/1076998609346970

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