Re: Factor Analysis

[Ken Reed]

It's not really possible to explain this in lay person's terms. The
difference between principal factor analysis and common factor analysis is
roughly that PCA uses raw scores, whereas factor analysis uses scores
predicted from the other variables and does not include the residuals.
That's as close to lay terms as I can get.

I have never heard a simple explanation of maximum likelihood estimation,
but --  MLE compares the observed covariance matrix with a  covariance
matrix predicted by probability theory and uses that information to estimate
factor loadings etc that would 'fit' a normal (multivariate) distribution.

MLE factor analysis is commonly used in structural equation modelling, hence
Tracey Continelli's conflation of it with SEM. This is not correct though.

I'd love to hear simple explanation of MLE!



 From: [EMAIL PROTECTED] (Tracey Continelli)
 Organization: http://groups.google.com/
 Newsgroups: sci.stat.consult,sci.stat.edu,sci.stat.math
 Date: 15 Jun 2001 20:26:48 -0700
 Subject: Re: Factor Analysis
 
 Hi there,
 
 would someone please explain in lay person's terms the difference
 betwn.
 principal components, commom factors, and maximum likelihood
 estimation
 procedures for factor analyses?
 
 Should I expect my factors obtained through maximum likelihood
 estimation
 tobe highly correlated?  Why?  When should I use a Maximum likelihood
 estimation procedure, and when should I not use it?
 
 Thanks.
 
 Rita
 
 [EMAIL PROTECTED]
 
 
 Unlike the other methods, maximum likelihood allows you to estimate
 the entire structural model *simultaneously* [i.e., the effects of
 every independent variable upon every dependent variable in your
 model].  Most other methods only permit you to estimate the model in
 pieces, i.e., as a series of regressions whereby you regress every
 dependent variable upon every independent variable that has an arrow
 directly pointing to it.  Moreover, maximum likelihood actually
 provides a statistical test of significance, unlike many other methods
 which only provide generally accepted cut-off points but not an actual
 test of statistical significance.  There are very few cases in which I
 would use anything except a maximum likelihood approach, which you can
 use in either LISREL or if you use SPSS you can add on the module AMOS
 which will do this as well.
 
 
 Tracey



=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

PCA and factor analysis: when to use which

[Ken Reed]

What is the basis for deciding when to use principal components analysis and
when to use factor analysis. Could anyone describe a problem that
illustrates the difference?



=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

Guttman reference

[Ken Reed]

Guttman wrote a paper, in the 40s I think, called something like: "The four
principal components of a scale". It was re-printed later in an edited book.

Can anyone help with the full reference?




=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=

within group agreement for nominal/ordinal data

[Ken Reed]

I'm trying to test whether a variable measures a group-level property, and
so I'm looking for an analog to eta-squared, intra-class correlation etc for
nominal or ordinal data.

I have data comprising 2000 workplaces, within samples of individuals drawn
from each (n=20,000).

One variable has 4 categories (agree-neutral-disagree, don't know).

1. How can I estimate how much of the total variability derives from between
groups (workplaces) and within groups?

2. Is there a rule-of-thumb for what would be evidence of strong
within-group agreement?

3. Can I do this in SPSS?




=
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
  http://jse.stat.ncsu.edu/
=