Multilevel IRT Modeling in Practice with the Package mlirt +Code + Data

✍ Scribed by Fox J.-P.

Tongue: English
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals' outcomes differ substantially when taking the measurement error in the dependent variable of the model into account. The multilevel model can be extended to handle measurement error using an item response theory (IRT) model, leading to a multilevel IRT model. This extended multilevel model is in particular suitable for the analysis of educational response data where students are nested in schools and schools are nested within cities/countries.

✦ Subjects

Библиотека;Компьютерная литература;R

📜 SIMILAR VOLUMES

Estimating the Multilevel Rasch Model: W

📁 Estimating the Multilevel Rasch Model: With the lme4 Package + Code

✍ Doran H., Bates D., Bliese P., Dowling M. 📂 Library 🌐 English

Traditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals in clustered settings are uncorrelated and items within a test that share a grouping structure are also uncorrelated. These

Modeling of Responses and Response Times

📁 Modeling of Responses and Response Times with the Package cirt + Code

✍ Fox J.-P., Entink R.K., Linden W. 📂 Library 🌐 English

In computerized testing, the test takers' responses as well as their response times on the items are recorded. The relationship between response times and response accuracies is complex and varies over levels of observation. For example, it takes the form of a tradeoff between speed and accuracy at

Extended Rasch Modeling: The eRm Package

📁 Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R + Code

✍ Mair P., Hatzinger R. 📂 Library 🌐 English

Tem response theory models (IRT) are increasingly becoming established in social science research, particularly in the analysis of performance or attitudinal data in psychology, education, medicine, marketing and other fields where testing is relevant. We propose the R package eRm (extended Rasch mo

Marginal modeling of multilevel binary d

📁 Marginal modeling of multilevel binary data with time-varying covariates

✍ Miglioretti D.L. 📂 Library 📅 2004 🌐 English

Panel Data Econometrics in R: The plm Pa

📁 Panel Data Econometrics in R: The plm Package + Code

✍ Croissant Y., Millo G. 📂 Library 🌐 English

Panel data econometrics is obviously one of the main fields in the profession, but most of the models used are difficult to estimate with R. plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models

Modeling Longitudinal and Multilevel Dat

📁 Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples

✍ Todd D. Little, Kai U. Schnabel, Jurgen Baumert 📂 Library 📅 2000 🏛 Psychology Press 🌐 English

This book focuses on the practical issues and approaches to handling longitudinal and multilevel data. All data sets and the corresponding command files are available via the Web. The working examples are available in the four major SEM packages--LISREL, EQS, MX, and AMOS--and two Multi-level packag