Factor Analysis in SPSS (Principal Components Analysis) - Part 3 of 6

In this video, we look at how to run an exploratory factor analysis (principal components analysis) in SPSS (Part 3 of 6). Subscribe today! Video Transcript: we&also pull up our Scree plot here. These two tables here, the Total Variance Explained and Scree Plot, both deal with what&known as our factor extraction methods. If you recall when we went through SPSS, the options, we left the eigenvalue greater than one rule option selected as the default, but we also selected that a Scree plot be output in our analysis. And these are two of the most commonly used procedures for deciding how many components or factors to retain; how many do you want to keep in our solution. Here for our Total Variance Explained table, notice first of all that we have 5 components in our rows here. And you may be wondering, well wait a second, I thought factor analysis, the whole purpose of it, was to reduce our number of variables into a smaller number of components? And if you are thinking that, you&correct, that is our purpose here. But, as just a matter of definition, it&always the case that the number of variables we input in our analysis, will always be equal to the number of components shown here. So we have five variables input in our analysis, therefore we have 5 rows or 5 components shown here. Now here in our Initial Eigenvalues table, notice that we have these various eigenvalues. So the first one is 3.136 and everything after that is less than 1. Now if you recall our first rule was...