A Step-by-Step Guide to Exploratory Factor Analysis with SPSS

What it’s about

Exploratory factor analysis is over a century old and ubiquitous across the behavioral, medical, and social sciences, yet surveys repeatedly show it is routinely misapplied because researchers receive little formal training and lean on poor software defaults. Marley Watkins answers this gap with a concise, accessible, applied manual that walks the reader through every decision step of an EFA—choosing variables and participants, screening data, judging whether EFA is appropriate, selecting the model, extraction method, number of factors, rotation, interpretation, and reporting—each illustrated with annotated SPSS screenshots, syntax, downloadable datasets, and scholarly citations. With minimal mathematics and a calm, jargon-light tone, the book equips students and seasoned researchers alike to produce defensible, replicable factor-analytic results and to respond confidently to editorial reviews.

The through-line

Who it’s for

An applied researcher or graduate student who wants to conduct a credible, publishable exploratory factor analysis in SPSS.

The problem

They must make many technical EFA decisions in SPSS with little training and unsound software defaults. They feel uncertain, intimidated by the math, and worried their analysis is wrong or indefensible.

The plan

1. Follow the ten-step EFA decision checklist in order.
2. Screen data and verify EFA is appropriate before analyzing.
3. Choose the common factor model with a justified extraction method.
4. Use multiple criteria (parallel analysis, MAP, scree, theory) to decide factor number.
5. Apply oblique rotation, interpret competing models, and report every decision transparently.

The payoff

The reader produces defensible, replicable EFA results. · They can justify every analytic choice to reviewers with citations. · They confidently interpret, name, and report factors and understand when to use EFA versus CFA.

See our guide

• The Four-S Discipline — One System, Four Capabilities →
• Study People And Organizations With Scientific Rigor →
• Choose And Apply The Right Analytical Method →
• Build A People Analytics Function →
• Choose The Right Lens — Multivariate Statistical Analysis: PCA, Factor, Cluster, Discriminant, MANOVA And Friends →
• Do Research That Holds Up →
• Design Surveys People Answer Honestly →

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Additional reading

• Exploratory Factor Analysis · Fabrigar, L. R., & Wegener, D. T. (2012)

  Cited frequently as a key methodological text, providing a more in-depth theoretical and mathematical treatment of the topics covered in this applied guide.

• Factor Analysis (2nd ed.) · Gorsuch, R. L. (1983)

  Presented as a seminal, classic text on factor analysis that provides the foundational rationale for many of the book's best-practice recommendations.

• Using Multivariate Statistics (7th ed.) · Tabachnick, B. G., & Fidell, L. S. (2019)

  Cited throughout for specific, practical guidelines on data screening, statistical assumptions, and numerical thresholds for interpretation.

• Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications · Thompson, B. (2004)

  Used as a key reference for clear explanations of complex concepts like factor rotation, higher-order models, and the interpretation of different types of factor loadings.

Featured in these guides

• Build A People Analytics Function →Cited source
• Choose The Right Lens — Multivariate Statistical Analysis: PCA, Factor, Cluster, Discriminant, MANOVA And Friends →Cited source
• Study People And Organizations With Scientific Rigor →Cited source
• Do Research That Holds Up →Cited source
• Choose And Apply The Right Analytical Method →Cited source
• Design Surveys People Answer Honestly →Cited source