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Simulation & Estimation
This provides immediate access to ALL print and digital modules in the portal by "registering" you for each and displaying all modules as a single collection as part of this pass.
All-access Pass (PRINT ONLY)
This provides access to a ZIP folder with all 45 previously published print modules.
Digital Module 06: Posterior Predictive Model Checking
In this digital ITEMS module, Dr. Allison Ames and Aaron Myers discuss the most common Bayesian approach to model-data fit evaluation, which is called Posterior Predictive Model Checking (PPMC), for simple linear regression and item response theory models. Keywords: Bayesian inference, simple linear regression, item response theory, IRT, model fit, posterior predictive model checking, PPMC, Bayes theorem, Yen’s Q3, item fit
Digital Module 11: Bayesian Psychometrics
In this digital ITEMS module, Dr. Roy Levy discusses how Bayesian inference is a mechanism for reasoning in probability-modeling framework, describes how this plays out in a normal distribution model and unidimensional item response theory (IRT) models, and illustrates these steps using the JAGS software and R. Keywords: Bayesian psychometrics, Bayes theorem, dichotomous data, item response theory (IRT), JAGS, Markov-chain Monte Carlo (MCMC) estimation, normal distribution, R, unidimensional models
Digital Module 13: Simulation Studies in IRT
In this digital ITEMS module, Dr. Brian Leventhal and Dr. Allison Ames provide an overview of Monte Carlo simulation studies (MCSS) in item response theory (IRT). MCSS are utilized for a variety of reasons, one of the most compelling being that they can be used when analytic solutions are impractical or nonexistent because they allow researchers to specify and manipulate an array of parameter values and experimental conditions (e.g., sample size, test length, and test characteristics). Key words: bias, bi-factor model, estimation, graded response model, item response theory, mean squared error, Monte Carlo, simulation, standard error, two-parameter logistic model
Module 27: Markov Chain Monte Carlo Methods for Item Response Theory Models
In this print module, Dr. Jee-Seon Kim and Dr. Daniel M. Bolt provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response (IRT) models and illustrate these ideas with a two-parameter logistic (2PL) model in the software program Winbugs. Keywords: Bayesian estimation, goodness-of-fit, item response theory, IRT, Markov chain Monte Carlo, MCMC, model comparison, two-parameter model, 2PL, Winbugs
Module 42: Simulation Studies in Psychometrics
In this print module, Dr. Richard A. Feinberg and Dr. Jonathan D. Rubright provide a comprehensive introduction to the topic of simulation studies in psychometrics using R that can be easily understood by measurement specialists at all levels of training and experience. Keywords: bias, experimental design, mean absolute difference, MAD, mean squared error, MSE, root mean squared error, RMSE, psychometrics, R, research design, simulation study, standard error
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Brian C. Leventhal (Editor, 2022-2024)
Phone: (540) 568-5004