Items that are highly correlated will share a lot of variance.
Recall that the goal of factor analysis is to model the interrelationships between items with fewer (latent) variables. Due to relatively high correlations among items, this would be a good candidate for factor analysis. Correlation is significant at the 0.05 level (2-tailed).įrom this table we can see that most items have some correlation with each other ranging from \(r=-0.382\) for Items 3 and 7 to \(r=.514\) for Items 6 and 7. Correlation is significant at the 0.01 level (2-tailed). Let’s get the table of correlations in SPSS Analyze – Correlate – Bivariate: Correlations
Click on the preceding hyperlinks to download the SPSS version of both files. For simplicity, we will use the so-called “ SAQ-8” which consists of the first eight items in the SAQ. Let’s proceed with our hypothetical example of the survey which Andy Field terms the SPSS Anxiety Questionnaire. 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 variables called factors (smaller than the observed variables), that can explain the interrelationships among those variables.
Although the implementation is in SPSS, the ideas carry over to any software program. Part 1 focuses on exploratory factor analysis (EFA). This seminar is the first part of a two-part seminar that introduces central concepts in factor analysis.