# Estimation Methods for Item Factor Analysis: An Overview

arXiv: https://arxiv.org/abs/2004.07579 · HTML: https://mlsanity.com/paper/2004.07579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation](https://mlsanity.com/paper/2606.15643/) (2026)

## Abstract (arXiv)

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.