scholarly journals A comparison of Monte Carlo methods for computing marginal likelihoods of item response theory models

2019 ◽  
Vol 48 (4) ◽  
pp. 503-512 ◽  
Author(s):  
Yang Liu ◽  
Guanyu Hu ◽  
Lei Cao ◽  
Xiaojing Wang ◽  
Ming-Hui Chen
Keyword(s):  
Monte Carlo ◽  
Item Response Theory ◽  
Item Response ◽  
Monte Carlo Methods ◽  
Response Theory ◽  
Marginal Likelihoods ◽  
Item Response Theory Models

Using Item Response Theory Models to Evaluate the Practice Environment Scale

2014 ◽  
Vol 22 (2) ◽  
pp. 323-341 ◽  
Author(s):  
Dheeraj Raju ◽  
Xiaogang Su ◽  
Patricia A. Patrician
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Information Criterion ◽  
Partial Credit Model ◽  
Practice Environment ◽  
Partial Credit ◽  
Response Theory ◽  
Environment Scale ◽  
Graded Response ◽  
Item Response Theory Models

Background and Purpose: The purpose of this article is to introduce different types of item response theory models and to demonstrate their usefulness by evaluating the Practice Environment Scale. Methods: Item response theory models such as constrained and unconstrained graded response model, partial credit model, Rasch model, and one-parameter logistic model are demonstrated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) indices are used as model selection criterion. Results: The unconstrained graded response and partial credit models indicated the best fit for the data. Almost all items in the instrument performed well. Conclusions: Although most of the items strongly measure the construct, there are a few items that could be eliminated without substantially altering the instrument. The analysis revealed that the instrument may function differently when administered to different unit types.

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