Estimation Methods for Item Factor Analysis: An Overview

TL;DR

This paper provides a comprehensive overview of item factor analysis (IFA), discussing models, estimation methods, computational strategies, software tools, and future research directions for analyzing multivariate categorical data.

Contribution

It offers a detailed review of IFA modeling techniques, estimation procedures, and software, highlighting challenges and practical applications in large-sample scenarios.

Findings

Survey of existing IFA models and estimation methods

Discussion of computational strategies for large data

Recommendations for practical application of IFA

Abstract

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.