How to factor-analyze your data right: do’s, don’ts, and how-to’s.

TLDR

Factor analysis distinguishes between exploratory and confirmatory approaches, rejects principal component analysis as a substitute, and highlights critical issues and recommended practices for appropriate use. This article offers a practical guideline for conducting factor analysis to estimate population‑level factor structures. The authors present a step‑by‑step walk‑through, detailing decisions and providing SPSS and LISREL syntax examples for preliminary procedures, EFA, and CFA.

Abstract

The current article provides a guideline for conducting factor analysis, a technique used to estimate the population-level factor structure underlying the given sample data. First, the distinction between exploratory and confirmatory factor analyses (EFA and CFA) is briefly discussed; along with this discussion, the notion of principal component analysis and why it does not provide a valid substitute of factor analysis is noted. Second, a step-by-step walk-through of conducting factor analysis is illustrated; through these walk-through instructions, various decisions that need to be made in factor analysis are discussed and recommendations provided. Specifically, suggestions for how to carry out preliminary procedures, EFA, and CFA are provided with SPSS and LISREL syntax examples. Finally, some critical issues concerning the appropriate (and not-so-appropriate) use of factor analysis are discussed along with the discussion of recommended practices.