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
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