scholarly journals Parameter Estimation with Mixture Item Response Theory Models: A Monte Carlo Comparison of Maximum Likelihood and Bayesian Methods

2012 ◽  
Vol 11 (1) ◽  
pp. 167-178 ◽  
Author(s):  
W. Holmes Finch ◽  
Brian F. French
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Item Response Theory ◽  
Item Response ◽  
Bayesian Methods ◽  
Response Theory ◽  
Item Response Theory Models

scholarly journals Recent developments of parameter estimation methods in item response theory models

2021 ◽  
Author(s):  
Kazuhiro Yamaguchi
Keyword(s):  
Parameter Estimation ◽  
Item Response Theory ◽  
Item Response ◽  
Marginal Likelihood ◽  
Theoretical Development ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Response Theory ◽  
Parameter Estimation Methods ◽  
Item Response Theory Models

This research reviewed the recent development of parameter estimation methods in item response theory models. Various new methods to manage the computational burden problem with respect to the item factor analysis and multidimensional item response models, which have high dimensional factors, were introduced. Monte Carlo integral methods, approximation methods for marginal likelihood, new optimization methods, and techniques used in the machine learning field were employed for the estimation methods. Theoretically, a new type of asymptotical setting, that assumes infinite number of sample sizes and items, was considered. Several methods were classified apart from the maximum likelihood method or Bayesian method. Theoretical development of interval estimation methods for individual latent traits were also proposed and they provided highly accurate intervals

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