ABSTRACT

Exploratory Factor Analysis (EFA) is a widely used statistical technique for reducing data dimensionality and representing latent constructs via observed variables. Different software offer toolsets for performing this analysis. While Python’s statistical computing ecosystem is less developed than that of R, it is growing in popularity as a platform for data analysis and now offers several packages that perform EFA. This article reviews EFA modules in the statsmodels, FactorAnalyzer, and scikit-learn Python packages. These packages are discussed with regard to official documentation, features, and performance on an applied example.

摘要

探索性因子分析 (EFA) 是一种广泛使用的统计技术，用于减少数据维度并通过观察变量表示潜在构造。不同的软件提供了用于执行此分析的工具集。虽然 Python 的统计计算生态系统不如 R 发达，但它作为数据分析平台越来越受欢迎，现在提供了几个执行 EFA 的包。本文回顾了statsmodels、FactorAnalyzer和scikit-learn Python 包中的 EFA 模块。这些包的讨论涉及官方文档、功能和应用示例的性能。