论文标题
项目因素分析的估计方法：概述
Estimation Methods for Item Factor Analysis: An Overview
论文作者
论文摘要
项目因素分析（IFA）是指分析多元分类数据的因子模型和统计推理程序。 IFA技术通常在社会和行为科学中用于分析项目级响应数据。这样的模型通过少数潜在因素来概括并解释一组分类变量之间的依赖性结构。在本章中，我们回顾了IFA建模技术，通常使用IFA模型。然后，我们讨论了IFA模型及其计算的估计方法，重点是样本量，项目数量和因子数量都大。对IFA的现有统计软件进行了调查。本章以IFA方法的实际应用和对未来方向的讨论的实际应用结束。
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.
