mirtjml - Joint Maximum Likelihood Estimation for High-Dimensional Item Factor Analysis

Provides constrained joint maximum likelihood estimation algorithms for item factor analysis (IFA) based on multidimensional item response theory models. So far, we provide functions for exploratory and confirmatory IFA based on the multidimensional two parameter logistic (M2PL) model for binary response data. Comparing with traditional estimation methods for IFA, the methods implemented in this package scale better to data with large numbers of respondents, items, and latent factors. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: 1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. <doi:10.1007/s11336-018-9646-5>; 2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. Journal of the American Statistical Association, <doi: 10.1080/01621459.2019.1635485>.

Last updated 4 years ago

ifaitem-factor-analysislarge-scale-assessmentparallel-computingpsychometrics

4.19 score 8 stars 1 packages 13 scripts 137 downloads

lvmcomp - Stochastic EM Algorithms for Latent Variable Models with a High-Dimensional Latent Space

Provides stochastic EM algorithms for latent variable models with a high-dimensional latent space. So far, we provide functions for confirmatory item factor analysis based on the multidimensional two parameter logistic (M2PL) model and the generalized multidimensional partial credit model. These functions scale well for problems with many latent traits (e.g., thirty or even more) and are virtually tuning-free. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale Full-information Item Factor Analysis. British Journal of Mathematical and Statistical Psychology. <doi:10.1111/bmsp.12153>.

Last updated 4 years ago

3.30 score 4 stars 2 scripts 136 downloads