Go to top of page Motivating Example: The SAQ (SPSS Anxiety Questionnaire) Do all these items actually measure what we call “SPSS Anxiety”? Let’s say you conduct a survey and collect responses about people’s anxiety about using SPSS. Suppose you are conducting a survey and you want to know whether the items in the survey have similar patterns of responses, do these items “hang together” to create a construct? The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying or latent variables called factors (smaller than the number of observed variables), that can explain the interrelationships among those variables.

Principal axis factoring (2-factor PAF).

Running a PCA with 2 components in SPSS.

Running a PCA with 8 components in SPSS.

Partitioning the variance in factor analysis.

SPSS Syntax: SPSS Syntax File for EFA and PCA Seminar.

Powerpoint Slides: Slides for EFA and PCA in SPSS.

The seminar will focus on how to run a PCA and EFA in SPSS and thoroughly interpret output, using the hypothetical SPSS Anxiety Questionnaire as a motivating example. For the EFA portion, we will discuss factor extraction, estimation methods, factor rotation, and generating factor scores for subsequent analyses. For the PCA portion of the seminar, we will introduce topics such as eigenvalues and eigenvectors, communalities, sum of squared loadings, total variance explained, and choosing the number of components to extract.

We will begin with variance partitioning and explain how it determines the use of a PCA or EFA model. This seminar will give a practical overview of both principal components analysis (PCA) and exploratory factor analysis (EFA) using SPSS.

Pca less rows than columns