Recent Methods for the Study of Measurement Invariance With Many Groups

2017 ◽  
Vol 47 (4) ◽  
pp. 637-664 ◽  
Author(s):  
Bengt Muthén ◽  
Tihomir Asparouhov
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Item Response Theory ◽  
Item Response ◽  
Alignment Method ◽  
Random Loading ◽  
Bayesian Analyses ◽  
Factor Analytic ◽  
Random Intercept ◽  
Level Method

This article reviews and compares recently proposed factor analytic and item response theory approaches to the study of invariance across groups. Two methods are described and contrasted. The alignment method considers the groups as a fixed mode of variation, while the random-intercept, random-loading two-level method considers the groups as a random mode of variation. Both maximum likelihood and Bayesian analyses are applied. A survey of close to 50,000 subjects in 26 countries is used as an illustration. In addition, the two methods are studied by Monte Carlo simulations. A list of considerations for choosing between the two methods is presented.

scholarly journals IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters

2020 ◽  
Vol 44 (7-8) ◽  
pp. 566-567
Author(s):  
Shaoyang Guo ◽  
Chanjin Zheng ◽  
Justin L. Kern
Keyword(s):  
Maximum Likelihood ◽  
Item Response Theory ◽  
Item Response ◽  
R Package ◽  
Response Theory ◽  
Estimation Algorithms ◽  
Irt Models ◽  
Irt Estimation

A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G, and 1PL-AG models). IRTBEMM should be of interest to the researchers in IRT estimation and applying IRT models with the guessing and slipping effects to real datasets.

scholarly journals Factor analytic and item response theory evaluation of the Penn State Worry Questionnaire in women with cancer

2012 ◽  
Vol 22 (6) ◽  
pp. 1441-1449 ◽  
Author(s):  
Salene M. Wu ◽  
Tammy A. Schuler ◽  
Michael C. Edwards ◽  
Hae-Chung Yang ◽  
Brittany M. Brothers
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Response Theory ◽  
Penn State Worry Questionnaire ◽  
Theory Evaluation ◽  
Factor Analytic ◽  
Penn State ◽  
State Worry

scholarly journals Score-Based Measurement Invariance Checks for Bayesian Maximum-a-Posteriori Estimates in Item Response Theory

2020 ◽  
Author(s):  
Rudolf Debelak ◽  
Samuel Pawel ◽  
Carolin Strobl ◽  
Edgar C. Merkle
Keyword(s):  
Maximum Likelihood ◽  
Item Response Theory ◽  
Item Response ◽  
Statistical Tests ◽  
Maximum A Posteriori ◽  
Model Parameters ◽  
A Posteriori ◽  
Response Theory ◽  
Simulation Based ◽  
A Posteriori Estimates

A family of score-based tests has been proposed in the past years for assessing the invariance of model parameters in several models of item response theory. These tests were originally developed in a maximum likelihood framework. This study aims to extend the theoretical framework of these tests to Bayesian maximum-a-posteriori estimates and to multiple group IRT models. We propose two families of statistical tests, which are based on a) an approximation using a pooled variance method, or b) a simulation-based approach based on asymptotic results. The resulting tests were evaluated by a simulation study, which investigated their sensitivity against differential item functioning with respect to a categorical or continuous person covariate in the two- and three-parametric logistic models. Whereas the method based on pooled variance was found to be practically useful with maximum likelihood as well as maximum-a-posteriori estimates, the simulation-based approach was found to require large sample sizes to lead to satisfactory results.

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