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. ; 2. Chen, Y., Li, X., & Zhang, S. (2017). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. arXiv preprint .


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1.2 by Siliang Zhang, a month ago

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Authors: Siliang Zhang [aut, cre] , Yunxiao Chen [aut] , Xiaoou Li [aut]

Documentation:   PDF Manual  

Task views: Psychometric Models and Methods

GPL-3 license

Imports Rcpp, stats, GPArotation

Linking to Rcpp, RcppArmadillo

See at CRAN