And so you'll see a summary of what your selection is.

And we'll click on Continue, and we're going to just click through

the first couple of charts, has to do with the display of information.

If you're using XLSTAT, you'll get this popup window,

asking you to add it as a trusted source.

And again, XLSTAT is one tool that's out there,

there's a free package called Real Statistics that's a nice package.

The limitation there with factor analysis,

it doesn't allow you to save those factor scores.

Those of you who are teaching yourselves statistical languages such as R.

Factor analysis is built into R,

it's built into environments such as Matlab, Jump, SAS.

So you can conduct this really using whatever

software you're most comfortable with, right.

And that's all there is to conducting the analysis, so

let's just take a look at the output.

We have a summary of the range of

each of the survey items mean and standard deviation.

Notice, we get this lovely correlation matrix, and

then we can try to eyeball it.

We can try to look for patterns here ourselves, but that's going to get

difficult, especially since it doesn't all fit on one screen, right?

We're going to move down, in terms of looking at the output.

We do see what the eigenvalues are, and

notice that this analysis has been run out to 18 factors.

And you'll see that the eigenvalues continue to decline, that's by design.

The first factor is going to have the largest eigenvalue,

the second factor will have the second largest, and so forth.

And that's directly related to the variation that's going to be explained,

and that continues to decline with smaller eingenvalues.

So that's the variation being explained by each incremental factor.

And then the row below that giving is going to give us

the commutative amount of variation that's explained.

And what we're looking at here, notice that when we get up to 9 factors,

we're capturing almost 72% of the variation in the original survey.

So we've gone from about 30 questions down to about a third of those questions.

And we still have more than 70% of the information contained in the survey.

We could keep on adding more and more factors to capture more and

more information.

But notice that we see very little gained in terms of the amount of

information being explained as we add more factors.

That's mimicked in the screen plot that we see.

Notice that early on the red line giving us that accumulative variation that we're

capturing, does a pretty good job, and then it plateaus.

And so that plateau, or if we were to invert this, it would look like an elbow.

That's what we're looking for as a means of deciding, when do we want to stop?

So it looks like in this case, we're going to stop after the 9 factors.

And that's what's been done automatically for us.