· 2020
Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis
- 10,258citations
- 2020year
Short summary
This paper consolidates practical guidance for researchers on making optimal decisions within the complex, multi-step process of exploratory factor analysis (EFA). It focuses on four key areas: extraction methods, rotation techniques, determining the number of factors, and sample size considerations.
AI-generated from the title and abstract; the full text is not read.
Key points
- Provides consolidated guidance on best practices for Exploratory Factor Analysis (EFA).
- Covers critical decision points in EFA: extraction, rotation, factor number, and sample size.
- Aims to help researchers and practitioners make informed choices within popular EFA software.
- Focuses on improving the understanding and application of EFA.
AI-generated from the title and abstract; the full text is not read.
Abstract
Exploratory factor analysis (EFA) is a complex, multi-step process. The goal of this paper is to collect, in one article, information that will allow researchers and practitioners to understand the various choices available through popular software packages, and to make decisions about “best practices” in exploratory factor analysis. In particular, this paper provides practical information on making decisions regarding (a) extraction, (b) rotation, (c) the number of factors to interpret, and (d) sample size.
The authors' abstract, as published at the source., 2020 · DOI ↗
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Field: Social Psychology
Social PsychologyPsychology