Assessing the Utility of Item Response Models: Computerized Adaptive Testing

2005 ◽  
Vol 12 (1) ◽  
pp. 21-27 ◽  
Author(s):  
G. Gage Kingsbury ◽  
Ronald L. Houser
Keyword(s):  
Item Response ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Response Models ◽  
Item Response Models

scholarly journals On the complementarity of classical test theory and item response models: item difficulty estimates and computerized adaptive testing

2015 ◽  
Vol 23 (88) ◽  
pp. 593-610
Author(s):  
Patrícia Costa ◽  
Maria Eugénia Ferrão
Keyword(s):  
Item Response ◽  
Computerized Adaptive Testing ◽  
Item Difficulty ◽  
Classical Test Theory ◽  
Adaptive Testing ◽  
Test Theory ◽  
Partial Credit Model ◽  
Response Models ◽  
Item Response Models ◽  
Classical Test

This study aims to provide statistical evidence of the complementarity between classical test theory and item response models for certain educational assessment purposes. Such complementarity might support, at a reduced cost, future development of innovative procedures for item calibration in adaptive testing. Classical test theory and the generalized partial credit model are applied to tests comprising multiple choice, short answer, completion, and open response items scored partially. Datasets are derived from the tests administered to the Portuguese population of students enrolled in the 4th and 6th grades. The results show a very strong association between the estimates of difficulty obtained from classical test theory and item response models, corroborating the statistical theory of mental testing.

scholarly journals Improving Measurement Precision in Experimental Psychopathology Using Item Response Theory

2019 ◽  
Vol 80 (4) ◽  
pp. 695-725
Author(s):  
Leah M. Feuerstahler ◽  
Niels Waller ◽  
Angus MacDonald
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Computerized Adaptive Testing ◽  
Cognitive Task ◽  
Working Memory Capacity ◽  
Response Theory ◽  
Response Models ◽  
Item Response Models ◽  
Experimental Psychopathology ◽  
Assessment Tasks

Although item response models have grown in popularity in many areas of educational and psychological assessment, there are relatively few applications of these models in experimental psychopathology. In this article, we explore the use of item response models in the context of a computerized cognitive task designed to assess visual working memory capacity in people with psychosis as well as healthy adults. We begin our discussion by describing how item response theory can be used to evaluate and improve unidimensional cognitive assessment tasks in various examinee populations. We then suggest how computerized adaptive testing can be used to improve the efficiency of cognitive task administration. Finally, we explore how these ideas might be extended to multidimensional item response models that better represent the complex response processes underlying task performance in psychopathological populations.

scholarly journals Scale Alignment in the Between-Item Multidimensional Partial Credit Model

2021 ◽  
pp. 014662162110131
Author(s):  
Leah Feuerstahler ◽  
Mark Wilson
Keyword(s):  
Item Response ◽  
Kindergarten Readiness ◽  
Latent Trait ◽  
Individual Development ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Response Models ◽  
Item Response Models ◽  
Item Parameters ◽  
Polytomous Item Response

In between-item multidimensional item response models, it is often desirable to compare individual latent trait estimates across dimensions. These comparisons are only justified if the model dimensions are scaled relative to each other. Traditionally, this scaling is done using approaches such as standardization—fixing the latent mean and standard deviation to 0 and 1 for all dimensions. However, approaches such as standardization do not guarantee that Rasch model properties hold across dimensions. Specifically, for between-item multidimensional Rasch family models, the unique ordering of items holds within dimensions, but not across dimensions. Previously, Feuerstahler and Wilson described the concept of scale alignment, which aims to enforce the unique ordering of items across dimensions by linearly transforming item parameters within dimensions. In this article, we extend the concept of scale alignment to the between-item multidimensional partial credit model and to models fit using incomplete data. We illustrate this method in the context of the Kindergarten Individual Development Survey (KIDS), a multidimensional survey of kindergarten readiness used in the state of Illinois. We also present simulation results that demonstrate the effectiveness of scale alignment in the context of polytomous item response models and missing data.

Stochastic Approximation Methods for Latent Regression Item Response Models

2010 ◽  
Vol 35 (2) ◽  
pp. 174-193 ◽  
Author(s):  
Matthias von Davier ◽  
Sandip Sinharay
Keyword(s):  
Item Response ◽  
Stochastic Approximation ◽  
Latent Variable ◽  
Reading Assessment ◽  
Mathematics Assessment ◽  
National Assessment ◽  
Variable Model ◽  
Response Models ◽  
Item Response Models ◽  
Latent Regression

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.

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