1: Note that rotation does not alter the basic aspects of the analysis, such as the amount of variance extracted from the items. Indeed, although eigenvalues might change as factor loadings are adjusted by rotation, the overall percentage of variance accounted for will remain constant. [return]

2: Alternatively, you could imagine rotating each cluster of items toward the axis. It really works out to be functionally the same. [return]

3: Researchers also tend to misinterpret the meaning of “orthogonal” to mean that factor scores are also uncorrelated. Orthogonal factors can (and often do) produce factor scores that are correlated (Nunnally & Bernstein, 1994; Thompson, 2004). For more about factor scores, see Chapter 9. [return]

4: However, other authors have argued that there are few substantive differences between the two oblique rotations (Fabrigar, Wegener, MacCallum, & Strahan, 1999). [return]

5: However, some authors have argued that oblique rotations produce less replicable results as they might overfit the data to a greater extent. We do not think there is empirical evidence to support this argument, but overfitting the data is a concern to all EFA analyses, as we will discuss later in the book. [return]

6: In this section we draw heavily on Thompson (2004), which is always a good reference. [return]

7: Except for nerds like us trying to understand all this stuff. [return]

8: Please note, the PRIORS = SMC option is required to conduct a factor analysis as opposed to a PCA when the PRINIT (iterated PAF) method is used. Also, the variables included in the analysis are referred to as lists, such that the colon after the EngProb prefix includes all variables in the analysis that start with the specified prefix. [return]

9: Please note, the variables in the previous examples were put into their respective models according to the theoretical factor each variable was associated with; thus, the REORDER option would not have provided substantially different factor loading matrices from what was presented. [return]

10: This is calculated from the results in the table entitled “Variance Explained by Each Factor.” The explained variance is converted to a proportion by dividing it by the total variance (equal to the number of items in the model; in this case that is 30) and then multiplying by 100. [return]