ASYMPTOTIC LIMITS OF ITEM PARAMETERS IN JOINT MAXIMUM-LIKELIHOOD ESTIMATION FOR THE RASCH MODEL

2008 ◽  
Vol 2008 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Shelby J. Haberman
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Rasch Model ◽  
Likelihood Estimation ◽  
Joint Maximum Likelihood Estimation ◽  
Item Parameters ◽  
The Rasch Model

A Review of Estimation Procedures for the Rasch Model with an Eye toward Longish Tests

1980 ◽  
Vol 5 (1) ◽  
pp. 35-64 ◽  
Author(s):  
Howard Wainer ◽  
Anne Morgan ◽  
Jan-Eric Gustafsson
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Rasch Model ◽  
Likelihood Estimation ◽  
New Developments ◽  
Conditional Maximum Likelihood ◽  
English Writing ◽  
Estimation Procedures ◽  
Conditional Maximum ◽  
The Rasch Model

Two estimation procedures for the Rasch Model are reviewed in detail, particularly with respect to new developments that make the more statistically rigorous Conditional Maximum Likelihood estimation practical for use with longish tests. Emphasis of the review is on European developments which are not well known in the English writing world.

scholarly journals A Comparison of Estimation Methods for the Rasch Model

2021 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Rasch Model ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Item Parameter ◽  
Estimation Methods ◽  
Limited Information ◽  
The Impact ◽  
The Rasch Model

The Rasch model is one of the most prominent item response models. In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.

On the Relationships between Sum Score Based Estimation and Joint Maximum Likelihood Estimation

Psychometrika ◽  
2007 ◽  
Vol 73 (1) ◽  
pp. 145-151 ◽  
Author(s):  
Guido del Pino ◽  
Ernesto San Martín ◽  
Jorge González ◽  
Paul De Boeck
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Joint Maximum Likelihood Estimation ◽  
Sum Score
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