Principal Component Analysis and Factor Analysis in Accounting Research
综述了主成分分析和因子分析在会计研究中的应用，指出两者目标不同且结果可能差异显著，提供了编程指南和实用示例，帮助研究者正确使用这些方法。
ABSTRACT Principal component analysis (PCA) and factor analysis (FA) are both variable reduction techniques used to represent a set of observed variables in terms of a smaller number of variables. While both PCA and FA are similar along several dimensions (e.g., extraction of common components/factors), researchers often fail to recognize that these techniques are designed to achieve different goals and can produce significantly different results. We conduct a comprehensive review of the use of PCA and FA in accounting research. We offer simple guidelines on how to program PCA and FA in SAS/Stata and emphasize the importance of the implementation techniques as well as the disclosure choices made when utilizing these methodologies. Furthermore, we present a few intuitive, practical examples highlighting the unique differences between the techniques. Finally, we provide some recommendations, observations, notes, and citations for researchers considering using these procedures in future research. Data Availability: The data used in this paper are publicly available from the sources indicated in the text. JEL Classifications: C38; C88; M41.