STOCHASTIC APPROXIMATION METHODS FOR LATENT REGRESSION ITEM RESPONSE MODELS
نویسندگان
چکیده
منابع مشابه
Stochastic Approximation Methods for Latent Regression Item Response Models
This paper presents an application of a stochastic approximation EM-algorithm using a Metropolis-Hastings sampler to estimate the parameters of an item response latent regression model. Latent regression models are extensions of item response theory (IRT) to a 2-level latent variable model in which covariates serve as predictors of the conditional distribution of ability. Applications for estim...
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Modern statistical models are often intractable, and approximation methods can be required to perform inference on them. Many different methods can be employed in most contexts, but not all are fully understood. The current thesis is an investigation into the use of various approximation methods for performing inference on latent variable models. Composite likelihoods are used as surrogates for...
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The reporting methods used in large scale assessments such as the National Assessment of Educational Progress (NAEP) rely on a latent regression model. The first component of the model consists of a p-scale IRT measurement model that defines the response probabilities on a set of cognitive items in p scales depending on a p-dimensional latent trait variable θ = (θ1, . . . θp). In the second com...
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1. Introduction Commonly, item response models and paired comparison models are treated as different model classes, suited for different data situations. However, there is a great similarity between item response data and paired comparisons and, accordingly, between the respective modeling approaches. Item response data appear when test persons face a certain number of items which are designed ...
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ژورنال
عنوان ژورنال: ETS Research Report Series
سال: 2009
ISSN: 2330-8516
DOI: 10.1002/j.2333-8504.2009.tb02166.x