Item Response Theory True Score Equatings and Their Standard Errors

2001 ◽  
Vol 26 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Haruhiko Ogasawara

The asymptotic standard errors of the estimates of the equated scores by several types of item response theory (IRT) true score equatings are provided. The first group of equatings do not use IRT equating coefficients. The second group of equatings use the IRT equating coefficients given by the moment or characteristic curve methods. The equating designs considered in this article cover those with internal or external common items and the methods with separate or simultaneous estimation of item parameters of associated tests. For the estimates of the asymptotic standard errors of the equated true scores, the method of marginal maximum likelihood estimation is employed for estimation of item parameters.

2017 ◽  
Vol 78 (5) ◽  
pp. 805-825 ◽  
Author(s):  
Dimiter M. Dimitrov

This article presents some new developments in the methodology of an approach to scoring and equating of tests with binary items, referred to as delta scoring (D-scoring), which is under piloting with large-scale assessments at the National Center for Assessment in Saudi Arabia. This presentation builds on a previous work on delta scoring and adds procedures for scaling and equating, item response function, and estimation of true values and standard errors of D scores. Also, unlike the previous work on this topic, where D-scoring involves estimates of item and person parameters in the framework of item response theory, the approach presented here does not require item response theory calibration.


Author(s):  
Brian Wesolowski

This chapter presents an introductory overview of concepts that underscore the general framework of item response theory. “Item response theory” is a broad umbrella term used to describe a family of mathematical measurement models that consider observed test scores to be a function of latent, unobservable constructs. Most musical constructs cannot be directly measured and are therefore unobservable. Musical constructs can therefore only be inferred based on secondary, observable behaviors. Item response theory uses observable behaviors as probabilistic distributions of responses as a logistic function of person and item parameters in order to define latent constructs. This chapter describes philosophical, theoretical, and applied perspectives of item response theory in the context of measuring musical behaviors.


2019 ◽  
Vol 80 (1) ◽  
pp. 91-125
Author(s):  
Stella Y. Kim ◽  
Won-Chan Lee ◽  
Michael J. Kolen

A theoretical and conceptual framework for true-score equating using a simple-structure multidimensional item response theory (SS-MIRT) model is developed. A true-score equating method, referred to as the SS-MIRT true-score equating (SMT) procedure, also is developed. SS-MIRT has several advantages over other complex multidimensional item response theory models including improved efficiency in estimation and straightforward interpretability. The performance of the SMT procedure was examined and evaluated through four studies using different data types. In these studies, results from the SMT procedure were compared with results from four other equating methods to assess the relative benefits of SMT compared with the other procedures. In general, SMT showed more accurate equating results compared with the traditional unidimensional IRT (UIRT) equating when the data were multidimensional. More accurate performance of SMT over UIRT true-score equating was consistently observed across the studies, which supports the benefits of a multidimensional approach in equating for multidimensional data. Also, SMT performed similarly to a SS-MIRT observed score method across all studies.


2019 ◽  
Vol 80 (3) ◽  
pp. 461-475
Author(s):  
Lianne Ippel ◽  
David Magis

In dichotomous item response theory (IRT) framework, the asymptotic standard error (ASE) is the most common statistic to evaluate the precision of various ability estimators. Easy-to-use ASE formulas are readily available; however, the accuracy of some of these formulas was recently questioned and new ASE formulas were derived from a general asymptotic theory framework. Furthermore, exact standard errors were suggested to better evaluate the precision of ability estimators, especially with short tests for which the asymptotic framework is invalid. Unfortunately, the accuracy of exact standard errors was assessed so far only in a very limiting setting. The purpose of this article is to perform a global comparison of exact versus (classical and new formulations of) asymptotic standard errors, for a wide range of usual IRT ability estimators, IRT models, and with short tests. Results indicate that exact standard errors globally outperform the ASE versions in terms of reduced bias and root mean square error, while the new ASE formulas are also globally less biased than their classical counterparts. Further discussion about the usefulness and practical computation of exact standard errors are outlined.


2019 ◽  
Vol 80 (3) ◽  
pp. 578-603
Author(s):  
HyeSun Lee ◽  
Weldon Z. Smith

Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response function and used a Markov Chain Monte Carlo procedure for simultaneous estimation of item and trait parameters. The simulation results demonstrated that the BRB IRT model performed well for the estimation of item and trait parameters and for screening those with relatively low scores on target traits. As found in the literature, the composition of item blocks was crucial for model performance; negatively keyed items were required for item blocks. The empirical application showed the performance of the BRB IRT model was equivalent to that of the Thurstonian IRT model. The potential advantage of the BRB IRT model as a base for more complex measurement models was also demonstrated by incorporating gender as a covariate into the BRB IRT model to explain response probabilities. Recommendations for the adoption of forced-choice formats were provided along with the discussion about using negatively keyed items.


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