help on factor analysis/non-normality

2002-03-01 Thread Mobile Survey

What do i do if I need to run a factor analysis and have non-normal
distribution for some of the items (indicators)? Does Principal
component analysis require the normality assumption Can I use GLS to
extract the factors and get over the problem of non-normality Please
do give references if you are replying
Thanks


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Re: help on factor analysis/non-normality

2002-03-01 Thread Rich Ulrich

On 1 Mar 2002 04:51:42 -0800, [EMAIL PROTECTED] (Mobile Survey)
wrote:

 What do i do if I need to run a factor analysis and have non-normal
 distribution for some of the items (indicators)? Does Principal
 component analysis require the normality assumption. 

There is no problem of non-normality, except that it *implies*
that decomposition  *might*  not give simple structures.
Complications are more likely when covariances are high.

What did you read, that you are trying to respond to?

  Can I use GLS to
 extract the factors and get over the problem of non-normality. Please
 do give references if you are replying.
 Thanks.

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html


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Re: help on factor analysis/non-normality

2002-03-01 Thread Robert Ehrlich

to amplifiy a bit, the interpretability of regression tends to go down as
the assumptions of normality and homogeneous variance are markedly
different from reality.  You can still go through the calcualtions but the
interpretation of results gets tricky.  Factor analysis is a sort of
regression analysis and so suffers in the same way from break downs of
assumptions.

Rich Ulrich wrote:

 On 1 Mar 2002 04:51:42 -0800, [EMAIL PROTECTED] (Mobile Survey)
 wrote:

  What do i do if I need to run a factor analysis and have non-normal
  distribution for some of the items (indicators)? Does Principal
  component analysis require the normality assumption.

 There is no problem of non-normality, except that it *implies*
 that decomposition  *might*  not give simple structures.
 Complications are more likely when covariances are high.

 What did you read, that you are trying to respond to?

   Can I use GLS to
  extract the factors and get over the problem of non-normality. Please
  do give references if you are replying.
  Thanks.

 --
 Rich Ulrich, [EMAIL PROTECTED]
 http://www.pitt.edu/~wpilib/index.html



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