Modeling hyper-priors for Bayesian IRT equating: Fixed hyper-parameters or Hierarchical hyper-priors

2019 ◽  
Vol 32 (4) ◽  
pp. 777-795
Author(s):  
Hyun Woo Nam
Keyword(s):  
Irt Equating ◽  
Bayesian Irt

Item Response Theory True Score Equatings and Their Standard Errors

2001 ◽  
Vol 26 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Haruhiko Ogasawara
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Standard Errors ◽  
Simultaneous Estimation ◽  
True Score ◽  
Response Theory ◽  
Asymptotic Standard Errors ◽  
Item Parameters ◽  
Irt Equating ◽  
Equating Coefficients

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.

Irt Versus Conventional Equating Methods: A Comparative Study of Scale Stability

1983 ◽  
Vol 8 (2) ◽  
pp. 137-156 ◽  
Author(s):  
Nancy S. Petersen ◽  
Linda L. Cook ◽  
Martha L. Stocking
Keyword(s):  
Item Response ◽  
Characteristic Curve ◽  
Calibration Method ◽  
Transformation Method ◽  
Scholastic Aptitude Test ◽  
Irt Model ◽  
Scholastic Aptitude ◽  
Reliable Model ◽  
Parallel Tests ◽  
Irt Equating

Scale drift for the verbal and mathematical portions of the Scholastic Aptitude Test (SAT) was investigated using linear, equipercentile and item response theory (IRT) equating methods. The linear methods investigated were the Tucker, Levine Equally Reliable and Levine Unequally Reliable models. Three IRT calibration designs were employed. These designs are referred to as (1) concurrent, (2) fixed b’s method, and (3) characteristic curve transformation method. The results of the various equating methods were compared both graphically and analytically. These results indicated that for reasonably parallel tests, linear equating methods perform adequately. However, when tests differ somewhat in content and length, methods based on the three-parameter logistic IRT model lead to greater stability of equating results. Of the conventional equating methods investigated, the Levine Equally Reliable model appears to be the most robust for the type of equating situation used in this study. The IRT method that provided the most stable equating results overall was the concurrent calibration method.

Post-hoc IRT equating of previously administered English tests for comparison of test scores

2008 ◽  
Vol 25 (2) ◽  
pp. 187-210 ◽  
Author(s):  
Chisato Saida ◽  
Tamaki Hattori
Keyword(s):  
Test Scores ◽  
Post Hoc ◽  
Irt Equating

scholarly journals The Bayesian Multilevel Trifactor Item Response Theory Model

2018 ◽  
Vol 79 (3) ◽  
pp. 462-494 ◽  
Author(s):  
Ken A. Fujimoto
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Latent Trait ◽  
Theory Model ◽  
Response Theory ◽  
Irt Model ◽  
Response Data ◽  
Proposed Model ◽  
Method Effects ◽  
Bayesian Irt

Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the aspects of another source (i.e., a nested method–source interaction). For this study, then, a Bayesian IRT model is proposed, one that accounts for such interaction among method sources while controlling for the clustering of individuals within the sample. The proposed model accomplishes these tasks by specifying a multilevel trifactor structure for the latent trait space. Details of simulations are also reported. These simulations demonstrate that this model can identify when item response data represent a multilevel trifactor structure, and it does so in data from samples as small as 250 cases nested within 50 clusters. Additionally, the simulations show that misleading estimates for the item discriminations could arise when the trifactor structure reflected in the data is not correctly accounted for. The utility of the model is also illustrated through the analysis of empirical data.

Item selection via Bayesian IRT models

2014 ◽  
Vol 34 (3) ◽  
pp. 487-503 ◽  
Author(s):  
Serena Arima
Keyword(s):  
Item Selection ◽  
Irt Models ◽  
Bayesian Irt

IRT Equating Methods

1991 ◽  
Vol 10 (3) ◽  
pp. 37-45 ◽  
Author(s):  
Linda L. Cook ◽  
Daniel R. Eignor
Keyword(s):  
Irt Equating
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