A log‐linear multidimensional Rasch model for capture–recapture

Statistical Inference for the Multidimensional Mixed Rasch Model

Inference in Generalized linear mixed models with multivariate random effects is often made cumbersome by the high-dimensional intractable integrals involved in the marginal likelihood. This article presents an inferential methodology based on the GEE approach. This method involves the approximations of the marginal likelihood and joint moments of the variables. It is also proposed an approxima...

Psychometric properties of Geriatric Depression Scale (GDS) among elderlies in Tehran using multidimensional Rasch model

Introduction and purpose: The purpose of this study was to examine the psychometric properties of the Geriatric Depression Scale (GDS) when applied to the elderly of Tehran. This research is applied-developmental, descriptive and quantitative. Materials and Methods: The research population was Tehrani elderlies, among which 400 people responded to the Geriatric Depression Scale voluntarily and...

Multidimensional Rasch Models for Partial - Credit Scoring

Introducing Multidimensional Rasch Model in Measuring Traffic Police Officers’ Behavior

This study applied a multidimensional item response model to measure police officers’ ability on conducting red-light running enforcement. The ability to conduct red-light running enforcement was found to consist of five component constructs, including ability on mental demands (AMD), on stopping and chasing vehicle (ASC), on psychological demands (APD), on physical demands (APHD), and under ab...

Stepwise Item Selection Procedures for Rasch Scales Using Quasi - Loglinear Models

Two iterative procedures for constructing Rasch scales are presented. A log-likelihood ratio test based on a quasi-loglinear formulation of the Rasch model is given by which one item at a time can be deleted from or added to an initial item set. In the so-called "top-down" algorithm, items are stepwise deleted from a relatively large initial item set, whereas in the "bottom-up" algorithm items ...