Application of MIRT modeling procedures is dependent on the quality of parameter estimates provided by the estimation software and techniques used. This study investigated model parameter recovery of two popular MIRT packages, BMIRT and flexMIRT, under some common measurement conditions. These packages were specifically selected to investigate the…

This study compares the performance of two estimation algorithms of new usage, the Metropolis-Hastings Robins-Monro (MHRM) and the Hamiltonian MCMC (HMC), with two consolidated algorithms in the psychometric literature, the marginal likelihood via EM algorithm (MML-EM) and the Markov chain Monte Carlo (MCMC), in the estimation of multidimensional…

Mixed-effects models (MEMs) and latent growth models (LGMs) are often considered interchangeable save the discipline-specific nomenclature. Software implementations of these models, however, are not interchangeable, particularly with small sample sizes. Restricted maximum likelihood estimation that mitigates small sample bias in MEMs has not been…

Item response theory (IRT) has gained an increasing popularity in large-scale educational and psychological testing situations because of its theoretical advantages over classical test theory. Unidimensional graded response models (GRMs) are useful when polytomous response items are designed to measure a unified latent trait. They are limited in…

Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and…

This study examines the use of cross-classified random effects models (CCrem) and cross-classified multiple membership random effects models (CCMMrem) to model rater bias and estimate teacher effectiveness. Effect estimates are compared using CTT versus item response theory (IRT) scaling methods and three models (i.e., conventional multilevel…

The current study used simulated data to investigate the properties of a newly proposed method (Yao's rater model) for modeling rater severity and its distribution under different conditions. Our study examined the effects of rater severity, distributions of rater severity, the difference between item response theory (IRT) models with rater effect…

This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE)…

The process-component approach has become quite popular for examining many psychological concepts. A typical example is the model with internal restrictions on item difficulty (MIRID) described by Butter (1994) and Butter, De Boeck, and Verhelst (1998). This study proposes a hierarchical generalized random-situation random-weight MIRID. The…

Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…

Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…

Response time has been regarded as an important source for investigating the relationship between human performance and response speed. It is important to examine the relationship between response time and item characteristics, especially in the perspective of the relationship between response time and various factors that affect examinee's…

The Programme for International Student Assessment comparative study of reading performance among 15-year-olds is reanalyzed using statistical procedures that allow the full complexity of the data structures to be explored. The article extends existing multilevel factor analysis and structural equation models and shows how this can extract richer…

A hierarchical latent regression model is suggested to estimate nested and nonnested relationships in complex samples such as found in the National Assessment of Educational Progress (NAEP). The proposed model aims at improving both parameters and variance estimates via a two-level hierarchical linear model. This model falls naturally within the…

Large-scale educational assessments such as the National Assessment of Educational Progress (NAEP) sample examinees to whom an exam will be administered. In most situations the sampling design is not a simple random sample and must be accounted for in the estimating model. After reviewing the current operational estimation procedure for NAEP, this…