Random Effect Differential Item Functioning Via Hierarchical Generalized Linear Model And Generalized Linear Latent Mixed Model
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Quantitative Psychology Research
The 78th Annual Meeting of the Psychometric Society (IMPS) builds on the Psychometric Society's mission to share quantitative methods relevant to psychology. The chapters of this volume present cutting-edge work in the field. Topics include studies of item response theory, computerized adaptive testing, cognitive diagnostic modeling, and psychological scaling. Additional psychometric topics relate to structural equation modeling, factor analysis, causal modeling, mediation, missing data methods, and longitudinal data analysis, among others. The papers in this volume will be especially useful for researchers in the social sciences who use quantitative methods. Prior knowledge of statistical methods is recommended. The 78th annual meeting took place in Arnhem, The Netherlands between July 22nd and 26th, 2013. The previous volume to showcase work from the Psychometric Society’s Meeting is New Developments in Quantitative Psychology: Presentations from the 77th Annual Psychometric Society Meeting (Springer, 2014).
Random-effect Differential Item Functioning Via Hierarchical Generalized Linear Model and Generalized Linear Latent Mixed Model
ABSTRACT: This study treated DIF as a random parameter varying over group units and formulated it following the Generalized Linear Latent and Mixed Model (GLLAMM) and Hierarchical Liner Model (HLM) frameworks. Such an alternative formulation was used to compare the HLM and GLLAMM estimates across several simulation conditions, since HLM and GLLAMM utilize different estimation methods to approximate the marginal maximum likelihood. HLM employs Penalized Quasi Maximum Likelihood (PQL) and Laplace approximations, while GLLAMM uses the Adaptive Gaussian Quadrature (AGQ) method.
Handbook of Item Response Theory
Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.