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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