High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm

By Li Cai. 2010

187 citations recorded by the source corpus.

A Metropolis–Hastings Robbins–Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The accuracy of the proposed algorithm is demonstrated with simulations. As an illustration, the proposed algorithm is applied to explore the factor structure underlying a new quality of life scale for children. It is shown that when the dimensionality is high, MH-RM has advantages over existing methods such as numerical quadrature based EM algorithm. Extensions of the algorithm to other modeling frameworks are discussed.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/084914f2b3e13edaf05791011065e6b055a159d4

Citations in the corpus

• Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “These algorithms avoid the numerical integration by Monte Carlo simulation (Cai, 2010a,b; Diebolt and Ip, 1996; Ip, 2002b; Meng and Schilling, 1996; Zhang et al., 2020; Zhang and Chen, 2020).”