# Tag Info

2

I don't have any good references for you, but yes, you can do this. The question will be what the results mean and that would depend on the specifics of the tests. If they have good alphas as single tests, that means the averaged correlations among items aren't too bad. That's an aspect of reliability. But what about validity? The fact that the items ...

1

Don't call the unweighted item sums "factor scores". Even if the items were continuous and you were using a weighted sum, the scores would not be the same as, or even necessarily good estimates of, the theoretical factors in a factor analysis. Call them "scale scores" (or something similar). Such scales are the norm, rather than the exception, and treating ...

0

Principal components analysis (PCA; which is related to factor analysis, and sometimes found under the same menus in stats packages) is used to analyze repertory grids - but it tends to be one person's grid at a time. If the user generated the constructs, I don't think you can combine grids because the variables don't make sense over time. Hierarchical ...

0

Exploratory factor analysis ($EFA$) is appropriate (psychometrically and otherwise) for examining the extent to which one may explain correlations among multiple items by inferring the common influence of (an) unmeasured (i.e., latent) factor(s). If this is not your specific intent, consider alternative analyses, e.g.: General linear modeling (e.g., ...

1

As gung said, recoding reverse-scored variables will only reverse the sign of their factor loadings, so the decision is only important because you will have to keep track of (and specify in anything you write about it) which variables are reverse-scored, or whether you recoded them. An unrelated concern arises with factor analysis of Likert scale ratings. ...

0

I think that your professor is counting the interactions as separate to your 5 variables. This means that instead of having k = 5, with the inclusion of the interactions between 4 of your variables, k = 11. I hope that helps!

2

Your question seems to be not about "two types" of PCA. The second "type" is a continuation of the explanation of the "first": the concept of loadings and how to get them, is introduced this time. Loadings are more important in factor analysis than in PCA because they are the source of interpretation of the latents (in PCA, you not often interpret the ...

Top 50 recent answers are included