The major advantage to maximum likelihood extraction is a test for the number of factors that account for most of th variance, which I believe is preferable to eigenvalues greater than one. Given the popularity of structural modeling and confirmatory factor analyses, I would suspect that ML is not that new. As for oblique factor solutions, I find that in most social science examples I have seen the existence of uncorrelated factors are the exception rather than the rule.
Dr. Paul R. Swank,
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center-Houston
From: Art Kendall [mailto:[hidden email]]
Sent: Saturday, February 19, 2011 5:57 PM
To: Swank, Paul R
Cc: [hidden email]
Subject: Re: What communalities and matrice to report ?
I seemed to recall that the OP had talked about working with summative scales, but was not sure.
I did not mean unconventional with the connotation that there was something wrong with it. New to an audience is not the same thing as "wrong".
If one reporting in the context of scale construction there is nothing wrong with using newer forms of factor analysis. However, I would usually ask for some explication to an audience why the newer way was used. How is it different? Did it make a substantive difference to use it? Different communities are used to seeing different approaches. When one does something novel-to-an-audience I believe it is helpful to tie what you are doing to what that audience is familiar with.
In scale construction based on the multi-trait multi-method approach it is conventional to allow an item to be unit weighted on only one scale (zero in the others). This is in interest of divergent validity. Oblique rotation is not consistent with that particular use. Items that have split loading are usually not used in the scoring key derived from the factor analysis. There are uses of factor analysis other than getting distinct interpretable measures of constructs.
The goals and context of an analysis make a lot of difference in communicating to an audience.
Perhaps my last question would have been clearer if I had asked if the OP found substantive differences in the scoring keys derived via ML vs PAF (aka PA2).
Without an intense interest, I kind of keep an eye out for actual instances where it makes a substantive difference what kind of extraction was used.
Offhand I cannot recall, for instance, an actual application in scale construction where it made much difference whether one use the principal components or the principal axes type of extraction. YMMV.
Art Kendall
On 2/19/2011 10:34 AM, Swank, Paul R wrote:
Are you suggesting that maximum likelihood and oblique solutions are unconventional?
Dr. Paul R. Swank,
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center-Houston
From: SPSSX(r) Discussion [[hidden email]] On Behalf Of Art Kendall
Sent: Saturday, February 19, 2011 6:31 AM
To: [hidden email]
Subject: Re: What communalities and matrice to report ?
Just curious. What is the goal of your factor analysis?
Are there specific reason to choose ML and Promax?
Do you find that the results differ in substantive conclusions from conventional approaches?
Art Kendall
Social Research Consultants
On 2/17/2011 4:03 PM, Mbaye Fall Diallo wrote:
Hi dear all,
1. I am using factor analysis (ML extraction). I would like to know what communality to report : initial or extraction ?
2. I used a Promax rotation. I would like to know what matrice to report : pattern matrix or structure matrix ?
Thanks in advance for your help. I would appreciate if you can provide some references.
Best,
Mbaye.
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