Fitting the Rasch model under the logistic regression framework to reduce estimation bias
Journal of Modern Applied Statistical MethodsPublished 26 June 2018Open access
Tianshu Pan
Citations2
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
This 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.
Keywords
Decision SciencesMathematics
Applications of Item Response Theory To Practical Testing Problems
5,024 Citations2012F. Lord
PsychometrikaMarginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm
2,340 Citations1981R. Darrell Bock, Murray Aitkin
The Em procedure is shown to apply to general item-response models lacking simple sufficient statistics for ability, including models with more than one latent dimension, when computing procedures based on an EM algorithm are used.
Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models
1,511 Citations2004Anders Skrondal, Sophia Rabe‐Hesketh
PsychometrikaWeighted Likelihood Estimation of Ability in Item Response Theory
1,208 Citations1989Thomas A. Warm
Explanatory Item Response Models
562 Citations2004Paul De Boeck, Mark Wilson
BiometricsA Solution to the Problem of Monotone Likelihood in Cox Regression
367 Citations2001Georg Heinze, M. Schemper
The solution is an adaptation of a procedure by Firth originally developed to reduce the bias of maximum likelihood estimates, which produces finite parameter estimates by means of penalized maximum likelihood estimation and is exemplified in the analysis of a breast cancer study.
Introducing ANOVA and ANCOVA : a GLM approach
325 Citations2000Andrew Rutherford
Journal of Educational MeasurementItem Analysis by the Hierarchical Generalized Linear Model
254 Citations2001Akihito Kamata
The HGLM model can be extended to a three-level latent regression model that permits investigation of the variation of students' performance across groups, such as is found in classrooms and schools, and of the interactive effect of person-and group-characteristic variables.
Applied Psychological MeasurementMonte Carlo Studies in Item Response Theory
218 Citations1996Michael R. Harwell, Clement A. Stone +2 more
Applied Psychological MeasurementBest Procedures For Sample-Free Item Analysis
89 Citations1977Benjamin D. Wright, Graham Douglas
Applied Psychological MeasurementPrecision of Warm’s Weighted Likelihood Estimates for a Polytomous Model in Computerized Adaptive Testing
81 Citations2001Shudong Wang, Tianyou Wang
Applied Psychological MeasurementAn Evaluation of Marginal Maximum Likelihood Estimation for the Two-Parameter Logistic Model
72 Citations1989Fritz Drasgow
Statistics & Probability LettersInconsistent maximum likelihood estimators for the Rasch model
50 Citations1995Malay Ghosh
Applied Psychological MeasurementCorrecting Unconditional Parameter Estimates in the Rasch Model for Inconsistency
17 Citations1988Paul Jansen, Arnold L. van den Wollenberg +1 more
