Add to ORKGThis article showed how and why the Rasch model can be fitted under the logistic regression framework. Then a penalized maximum likelihood (Firth 1993) for logistic regression models can also be used to reduce ML biases when fitting the Rasch model. These conclusions are supported by a simulation study
The present study takes a closer look at the principles of estimating person parameters in the Rasch...
Results of simulation studies indicate that the unconditional maximum likelihood method is commonly...
Regression models are commonly used in psychological research. In most studies, regression coefficie...
This study examines the effect of two different techniques of bias reduction in the case of the fixe...
It is shown in this paper that the unconditional or simultaneous maximum likelihood estimation proc...
For the parameters of a multinomial logistic regression, it is shown how to obtain the bias-reducing...
The Rasch family of models considered in this paper includes models for polytomous items and multipl...
tic model; Maximum likelihood estimation, condi-tional; Maximum likelihood estimation, unconditional...
Building on the Kelley and Gulliksen versions of classical test theory, this article shows that a lo...
Since its introduction, the joint maximum likelihood (JML) has been widely used as an estimation met...
The Rasch family of models considered in this paper includes models for polytomous items and multipl...
The large samples and item pools required for accurate parameter estimation under the three-paramete...
The large samples and item pools required for accurate parameter estimation under the three-paramete...
The large samples and item pools required for accurate parameter estimation under the three-paramete...
The purpose of this study is to determine an efficient way to reduce the bias in estimates of the Ra...
The present study takes a closer look at the principles of estimating person parameters in the Rasch...
Results of simulation studies indicate that the unconditional maximum likelihood method is commonly...
Regression models are commonly used in psychological research. In most studies, regression coefficie...
This study examines the effect of two different techniques of bias reduction in the case of the fixe...
It is shown in this paper that the unconditional or simultaneous maximum likelihood estimation proc...
For the parameters of a multinomial logistic regression, it is shown how to obtain the bias-reducing...
The Rasch family of models considered in this paper includes models for polytomous items and multipl...
tic model; Maximum likelihood estimation, condi-tional; Maximum likelihood estimation, unconditional...
Building on the Kelley and Gulliksen versions of classical test theory, this article shows that a lo...
Since its introduction, the joint maximum likelihood (JML) has been widely used as an estimation met...
The Rasch family of models considered in this paper includes models for polytomous items and multipl...
The large samples and item pools required for accurate parameter estimation under the three-paramete...
The large samples and item pools required for accurate parameter estimation under the three-paramete...
The large samples and item pools required for accurate parameter estimation under the three-paramete...
The purpose of this study is to determine an efficient way to reduce the bias in estimates of the Ra...
The present study takes a closer look at the principles of estimating person parameters in the Rasch...
Results of simulation studies indicate that the unconditional maximum likelihood method is commonly...
Regression models are commonly used in psychological research. In most studies, regression coefficie...