Different Approaches to Covariate Inclusion in the Mixture Rasch Model
Datos Bibliográficos
| ID | 20284482 |
|---|---|
| Autores | Tongyun Li (0000-0003-4042-0328, University of Maryland, College Park, MD, USA, autor de correspondencia), Hong Jiao (0000-0001-5014-6698, University of Maryland, College Park, MD, USA), George B Macready (University of Maryland, College Park, MD, USA) |
| Año | 2016 |
| Volumen | 76 |
| Número | 5 |
| Páginas | 848-872 |
| Fecha de publicación | 2016-10-01 |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Educational and Psychological Measurement (JOURNAL) |
| Identificadores de la revista | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Editorial | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164415610380 |
| PMID | 29795891 |
| OpenAlex | W309703406 |
| Idioma | EN |
| Citas recibidas | 2 |
| Referencias citadas | 29 |
The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo simulation study is conducted in which data generated according to a two-class mixture Rasch model with both dichotomous and continuous covariates are fitted to several mixture Rasch models with misspecified covariates to examine the effects of covariate inclusion on model parameter estimation. In addition, both complete response data and incomplete response data with different types of missingness are considered in the present study in order to simulate practical assessment settings. Parameter estimation is carried out within a Bayesian framework vis-à-vis Markov chain Monte Carlo algorithms
Bayesian probability · Covariate · Differential item functioning · Econometrics · Item response theory · Markov chain Monte Carlo · Missing data · Mixture model · Monte Carlo method · Polytomous Rasch model · Psychometrics · Rasch model · Statistics · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
Rasch Models in Latent Classes
Model Selection and Multimodel Inference
Randomization-Based Inference about Latent Variables from Complex Samples
Investigating population heterogeneity with factor mixture models.
A new look at the statistical model identification
Estimating the Dimension of a Model
Application of Model-Selection Criteria to Some Problems in Multivariate Analysis
Bayesian Measures of Model Complexity and Fit
Scoring Omitted and Not-Reached Items
Multiple Imputation for Nonresponse in Surveys
| Citas por año | 0,22 |
|---|---|
| Intervalo de citas | 2017 - 2025 (9) |
| Altamente citado | No |