SAS macros for longitudinal IRT models
- Published
- Accepted
- Subject Areas
- Data Science, Graphics, Programming Languages
- Keywords
- polytomous IRT model, Birnbaum model, 1PL model, 2PL model, Generalized Partial Credit model, Rasch model, item parameter drift, longitudinal IRT model, SAS, response dependence
- Copyright
- © 2018 Olsbjerg et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. SAS macros for longitudinal IRT models. PeerJ Preprints 6:e26740v1 https://doi.org/10.7287/peerj.preprints.26740v1
Abstract
IRT models are often applied when observed items are used to measure a unidimensional latent variable. Originally used in educational research, IRT models are now widely used when focus is on physical functioning or psychological well-being. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a collection of SAS macros that can be used for fitting data to, simulating from, and visualizing longitudinal IRT models. The macros encompass dichotomous as well as polytomous item response formats and are sufficiently flexible to accommodate changes in item parameters across time points and local dependence between responses at different time points.
Author Comment
This is a submission to PeerJ Computer Science for review.