Marginal Maximum Likelihood Estimation of Item Response Models in R + Code

✍ Scribed by Johnson M.S.

Tongue: English
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Item response theory (IRT) models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.

✦ Subjects

Библиотека;Компьютерная литература;R

📜 SIMILAR VOLUMES

Maximum likelihood estimation of oncogen

📁 Maximum likelihood estimation of oncogenetic tree models

✍ von Heydebreck A. 📂 Library 📅 2004 🌐 English

Maximum Likelihood Estimation of Misspec

📁 Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later

✍ T. Fomby & R. Carter Hill (Editors) 📂 Library 📅 2003 🏛 Emerald Group Publishing 🌐 English

This volume is the result of an Advances in Econometrics conference held in November of 2002 at Louisiana State University in recognition of Halbert White's pioneering work published in Econometrica in 1980 and 1982 on robust variance-covariance estimation and quasi-maximum likelihood estimation. It

Maximum Likelihood Estimation of Functio

📁 Maximum Likelihood Estimation of Functional Relationships

✍ Nico J. D. Nagelkerke (auth.) 📂 Library 📅 1992 🏛 Springer-Verlag New York 🌐 English

<p>The theory of functional relationships concerns itself with inference from models with a more complex error structure than those existing in regression models. We are familiar with the bivariate linear relationship having measurement errors in both variables and the fact that the standard regress

Maximum Likelihood Estimation of Misspec

📁 Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later, Volume 17

✍ T. Fomby, R. Carter Hill, Thomas B. Fomby 📂 Library 📅 2003 🏛 Emerald Group Publishing Limited 🌐 English

Eleven papers focus on maximum likelihood estimation in the presence of misspecified models, or quasi-maximum likelihood estimation, and recognize Halbert White's pioneering work on the topic beginning in 1982.

Maximum likelihood estimation of ordered

📁 Maximum likelihood estimation of ordered multinomial parameters

✍ Jewell N.P. 📂 Library 📅 2004 🌐 English

Maximum likelihood estimation in random

📁 Maximum likelihood estimation in random effects cure rate models with nonignorable missing covariates

✍ Herring R.H., Ibrahim J.G. 📂 Library 📅 2002 🌐 English