Using SAS PROC NLMIXED to fit item response theory models

Sheu, Ching-Fan; Chen, Cheng-Te; Su, Ya-Hui; Wang, Wen Chung

doi:10.3758/bf03192688

Cited by 48 publications

(37 citation statements)

References 30 publications

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“…Members of the Society have developed some of the leading tools for Internet research (e.g., Reips, 2006), lab research (e.g., Schneider et al, 2007), and statistical analyses (e.g., Faul et al, 2006;Sheu et al, 2005). High-capacity and high-speed computers allow the exploration of high-dimensional semantic spaces for basic and applied research projects (Westbury, 2007).…”

Section: Discussionmentioning

confidence: 99%

An impoverished machine: Challenges to human learning and instructional technology

Taraban

2008

Behavior Research Methods

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Section: Discussionmentioning

confidence: 99%

An impoverished machine: Challenges to human learning and instructional technology

Taraban

2008

Behavior Research Methods

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“…It is therefore worth specifying the potential explicitly. For SAS PROC NLMIXED, suchlike descriptions can be found in De Boeck and Wilson (2004) and in Sheu et al (2005).…”

Section: Introductionmentioning

confidence: 99%

The Estimation of Item Response Models with the`lmer`Function from thelme4Package inR

Boeck¹,

Bakker²,

Zwitser³

et al. 2011

J. Stat. Soft.

257

254

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In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework refer to (a) the kind of covariates -their mode (person, item, person-by-item), and their being external vs. internal to responses, and (b) the kind of effects the covariates have -fixed vs. random, and if random, the mode across which the effects are random (persons, items). Based on this framework, three broad categories of models are described: Item covariate models, person covariate models, and person-by-item covariate models, and within each category three types of more specific models are discussed. The models in question are explained and the associated lmer code is given. Examples of models are the linear logistic test model with an error term, differential item functioning models, and local item dependency models. Because the lme4 package is for univariate generalized linear mixed models, neither the two-parameter, and three-parameter models, nor the item response models for polytomous response data, can be estimated with the lmer function.

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“…The readers may consult the article by Sheu et al (2005) for a quick start on how to implement mixed-effects models in PROC NLMIXED. A brief explanation of the statements in Listing 1 is provided here.…”

Section: Simulationsmentioning

confidence: 99%

“…An adaptive version of Gauss-Hermite quadrature (Abramowitz & Stegun, 1972) is used to approximate the likelihood, and the default maximization routine is a dual quasi-Newton algorithm (Pinheiro & Bates, 1995). A more detailed discussion of parameter estimation using the NLMIXED procedure can be found in Sheu, Chen, Su, and Wang (2005) in the context of fitting item response theory models.…”

Section: Mixed-effects Signal Detection Modelmentioning

confidence: 99%

Analyzing recognition performance with sparse data

Sheu

Lee

Shih

2008

Behavior Research Methods

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Using SAS PROC NLMIXED to fit item response theory models

Cited by 48 publications

References 30 publications

An impoverished machine: Challenges to human learning and instructional technology

An impoverished machine: Challenges to human learning and instructional technology

The Estimation of Item Response Models with the`lmer`Function from thelme4Package inR

Analyzing recognition performance with sparse data

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Using SAS PROC NLMIXED to fit item response theory models

Cited by 48 publications

References 30 publications

An impoverished machine: Challenges to human learning and instructional technology

An impoverished machine: Challenges to human learning and instructional technology

The Estimation of Item Response Models with thelmerFunction from thelme4Package inR

Analyzing recognition performance with sparse data

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Product

Resources

About

The Estimation of Item Response Models with the`lmer`Function from thelme4Package inR