scholarly journals Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

2017 ◽  
Vol 17 (2) ◽  
pp. 330-342 ◽  
Author(s):  
Robert L. Grant ◽  
Bob Carpenter ◽  
Daniel C. Furr ◽  
Andrew Gelman
Keyword(s):  
Bayesian Inference ◽  
Item Response Theory ◽  
Open Source ◽  
Item Response ◽  
Bayesian Modeling ◽  
Inference Engine ◽  
Software Components ◽  
Statistical Software ◽  
Simulation Based ◽  
Item Response Theory Models

In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.

Item Response Theory Models Applied to Data Allowing Examinee Choice

1998 ◽  
Vol 23 (3) ◽  
pp. 236-243 ◽  
Author(s):  
Eric T. Bradlow ◽  
Neal Thomas
Keyword(s):  
Bayesian Inference ◽  
Item Response Theory ◽  
Item Response ◽  
Estimation Methods ◽  
Response Theory ◽  
Standard Methods ◽  
Irt Models ◽  
Item Responses ◽  
Low Dimensional ◽  
Item Response Theory Models

Examinations that permit students to choose a subset of the items are popular despite the potential that students may take examinations of varying difficulty as a result of their choices. We provide a set of conditions for the validity of inference for Item Response Theory (IRT) models applied to data collected from choice-based examinations. Valid likelihood and Bayesian inference using standard estimation methods require (except in extraordinary circumstances) that there is no dependence, after conditioning on the observed item responses, between the examinees choices and their (potential but unobserved) responses to omitted items, as well as their latent abilities. These independence assumptions are typical of those required in much more general settings. Common low-dimensional IRT models estimated by standard methods, though potentially useful tools for educational data, do not resolve the difficult problems posed by choice-based data.

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.

scholarly journals A Short Note on Estimating the Testlet Model With Different Estimators in Mplus

2017 ◽  
Vol 78 (3) ◽  
pp. 517-529 ◽  
Author(s):  
Yong Luo
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Latent Variable ◽  
Short Note ◽  
Bifactor Model ◽  
Estimation Methods ◽  
Full Information ◽  
Modeling Software ◽  
Two Parameter ◽  
Item Response Theory Models

Mplus is a powerful latent variable modeling software program that has become an increasingly popular choice for fitting complex item response theory models. In this short note, we demonstrate that the two-parameter logistic testlet model can be estimated as a constrained bifactor model in Mplus with three estimators encompassing limited- and full-information estimation methods.

scholarly journals Examination of Different Item Response Theory Models on Tests Composed of Testlets

2017 ◽  
Vol 6 (4) ◽  
pp. 113
Author(s):  
Esin Yilmaz Kogar ◽  
Hülya Kelecioglu
Keyword(s):  
Item Response Theory ◽  
Sample Size ◽  
Item Response ◽  
Data Sets ◽  
Meaningful Difference ◽  
Response Theory ◽  
Size Change ◽  
Data Set ◽  
Testlet Response Theory ◽  
Item Response Theory Models

The purpose of this research is to first estimate the item and ability parameters and the standard error values related to those parameters obtained from Unidimensional Item Response Theory (UIRT), bifactor (BIF) and Testlet Response Theory models (TRT) in the tests including testlets, when the number of testlets, number of independent items, and sample size change, and then to compare the obtained results. Mathematic test in PISA 2012 was employed as the data collection tool, and 36 items were used to constitute six different data sets containing different numbers of testlets and independent items. Subsequently, from these constituted data sets, three different sample sizes of 250, 500 and 1000 persons were selected randomly. When the findings of the research were examined, it was determined that, generally the lowest mean error values were those obtained from UIRT, and TRT yielded a mean of error estimation lower than that of BIF. It was found that, under all conditions, models which take into consideration the local dependency have provided a better model-data compatibility than UIRT, generally there is no meaningful difference between BIF and TRT, and both models can be used for those data sets. It can be said that when there is a meaningful difference between those two models, generally BIF yields a better result. In addition, it has been determined that, in each sample size and data set, item and ability parameters and correlations of errors of the parameters are generally high.

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