2020/04/16 by Yunxiao Chen, Chen, Yunxiao, Siliang Zhang +1 · 1 citation
Agricultural and Biological Sciences · Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Statistical Modeling Techniques #Artificial intelligence #Categorical variable #Computation (stat.CO) #Computer science #Data mining #Engineering #Estimation #FOS: Computer and information sciences #Factor (programming language) #Factor analysis #Inference #Latent variable #Machine learning #Mathematics #Methodology (stat.ME) #Multivariate statistics #Psychometric Methodologies and Testing #Sample (material) #Sensory Analysis and Statistical Methods #Set (abstract data type) #Statistical inference #Statistical model #Statistics #stat.CO #stat.ME
paper · pdf · doi:10.48550/arxiv.2004.07579
published in arXiv (Cornell University) (Cornell University) · 23 pages
arxiv created 2020/04/16 · openalex publication_date 2020/04/16 · arxiv updated 2020/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Item factor analysis (IFA) refers to the factor models and statistical inference procedures for analyzing multivariate categorical data. IFA techniques are commonly used in social and behavioral sciences for analyzing item-level response data. Such models summarize and interpret the dependence structure among a set of categorical variables by a small number of latent factors. In this chapter, we review the IFA modeling technique and commonly used IFA models. Then we discuss estimation methods for IFA models and their computation, with a focus on the situation where the sample size, the number of items, and the number of factors are all large. Existing statistical softwares for IFA are surveyed. This chapter is concluded with suggestions for practical applications of IFA methods and discussions of future directions.