Best Practices for Your Exploratory Factor Analysis: A Factor Tutorial

ABSTRACT Context: exploratory factor analysis (EFA) is one of the statistical methods most widely used in administration; however, its current practice coexists with rules of thumb and heuristics given half a century ago. Objective: the purpose of this article is to present the best practices and recent recommendations for a typical EFA in administration through a practical solution accessible to researchers. Methods: in this sense, in addition to discussing current practices versus recommended practices, a tutorial with real data on Factor is illustrated. The Factor software is still little known in the administration area, but is freeware, easy-to-use (point and click), and powerful. The step-by-step tutorial illustrated in the article, in addition to the discussions raised and an additional example, is also available in the format of tutorial videos. Conclusion: through the proposed didactic methodology (article-tutorial + video-tutorial), we encourage researchers/methodologists who have mastered a particular technique to do the same. Specifically about EFA, we hope that the presentation of the Factor software, as a first solution, can transcend the current outdated rules of thumb and heuristics, by making best practices accessible to administration researchers.

摘要
研究背景：探索性因子分析（exploratory factor analysis, EFA）是管理学领域应用最为广泛的统计方法之一，但当前的实践应用仍沿用半个世纪前提出的经验法则与启发式规则。
研究目的：本文旨在通过面向研究者的实用解决方案，系统阐述管理学领域典型探索性因子分析的最佳实践与最新权威建议。
研究方法：本文在对比当前实践与推荐实践的基础上，结合真实数据演示了基于Factor软件的实操教程。Factor软件目前在管理学领域鲜为人知，但该软件为免费开源工具，操作简便（支持点击式交互）且功能强劲。本文所附分步教程、配套讨论及额外案例，均同步提供了视频教程版本。
研究结论：通过本文结合文字教程与视频教程的一体化教学方法论，我们呼吁已掌握特定技术的研究者与方法学者效仿此类实践。针对探索性因子分析而言，我们希望本文对Factor软件的介绍作为首选解决方案，能够打破当前过时的经验法则与启发式规则的桎梏，让管理学研究者能够便捷获取并应用探索性因子分析的最佳实践方法。