README.md

mpirt

Overview

mpirt contains a collection of functions and wrappers for estimating monotonic polynomial item response models using rpf and OpenMx.

Installation

# From GitHub:
# install.packages("devtools")
devtools::install_github("falkcarl/mpirt")

Some relevant references

Falk, C.F. (2020). The monotonic polynomial graded response model: Implementation and a comparative study. Applied Psychological Measurement, 44, 465-481. https://doi.org/10.1177/0146621620909897

Falk, C.F. (2019). Model selection for monotonic polynomial item response models. In In M. Wiberg, S. Culpepper, R. Janssen, J. Gonzalez, and D. Molenaar (Eds.), Quantitative Psychology: The 83rd Annual Meeting of the Psychometric Society, New York, NY, 2018 (pp. 75{85). Cham, Switzerland: Springer Nature.

Falk, C.F., & Cai, L. (2016). Maximum marginal likelihood estimation of a monotonic polynomial generalized partial credit model with applications to multiple group analysis. Psychometrika, 81, 434-460. http://dx.doi.org/10.1007/s11336-014-9428-7

Feuerstahler, L. M. (2016). Exploring alternate latent trait metrics with the filtered monotonic polynomial IRTmodel (Unpublished doctoral dissertation). Department of Psychology, University of Minnesota.

Liang, L., & Browne, M. W. (2015). A quasi-parametric method for fitting flexible item response functions. Journal of Educational and Behavioral Statistics, 40, 5-34. https://doi.org/10.3102/1076998614556816

Pritikin, J. N., Hunter, M. D., & Boker, S. M. (2015). Modular open-source software for Item Factor Analysis. Educational and Psychological Measurement, 75(3), 458–474. https://doi.org/10.1177/0013164414554615

Pritikin, J.N., & Falk, C.F. (in press). OpenMx: A modular research environment for item response theory methods development. Applied Psychological Measurement. https://doi.org/10.1177/0146621620929431

Pritikin, J. N., & Schmidt, K. M. (2016). Model builder for Item Factor Analysis with OpenMx. R Journal, 8(1), 182–203.



falkcarl/mpirt documentation built on July 11, 2024, 12:09 a.m.