Date:         Fri, 10 Jul 2009 12:47:28 +0200
Reply-To:     "alollo@libero.it" <alollo@libero.it>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         "alollo@libero.it" <alollo@libero.it>
Content-Type: text/plain; charset=iso-8859-1

Hi,
I am PhD in Organizational Psychology. I study and work at the Faculty of Psychology in University of Bologna (Italy).
I don't speak very well english. I'll try to explain you my problem.
We assume multivariate normality on the data to carry out our analysis, however, I have seen that often (or always...) my variables have a non-normal distribution.
I controll the skenwess and the kurtosis index. I delete the univariate outliers from my data base. I transform (by logarithm, square root, ecc) the variables that have a value larger than or -1 (in skewess and kurtosis). I calculate the Mahalonobis Distance and delete the multivariate outliers and finally I calculate the Marcia Index... but my value of this index is often (or always) larger than the critical value (p (p*q)). Often I couldn't assume multivariate normality in my data.
I have two questions:
1. Exist other tecniques to change my data to obtain the multivariate normal distribution?
2. Exist other tecniques to work with data that have not a multivariate normal distibution?
I work with SPSS, and I hope to you help me, also to implement the answers in SPSS.

Thank you

Lorenzo

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