Date: Tue, 18 Jul 2006 15:11:42 -0300
Reply-To: Hector Maletta <firstname.lastname@example.org>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Hector Maletta <email@example.com>
Subject: Re: Standard error using GLM in SPSS/dealing with kurtosis
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ANOVA and regression are but special cases of GLM, both sharing the same
basic assumptions (ANOVA may add its own equal variance assumption).
Keep off transformations as long as they are not dictated by theory. For
instance, if your theory predicts a constant rate of growth in a variable,
you can transform it into its logarithm you can fit a linear regression
function to its logarithmic transformation, Or, if you think that something
is a function of the proportional increase in something else, rather than a
function of its absolute increase, you may use the logarithm of the
independent variable instead of its original raw value. But transformations
in order to make up for an unfulfilled assumption are equivalent to cheating
combined with wishful thinking, and equally pointless.
De: Cathy.Underhill@statcan.ca [mailto:Cathy.Underhill@statcan.ca]
Enviado el: Tuesday, July 18, 2006 12:06 PM
Asunto: RE: Standard error using GLM in SPSS/dealing with kurtosis
Thanks for your reply, Hector!
In actual fact, I am not using a regression procedure but analysis of
variance (I have a mixed model between-within subjects design) -- I am not
sure if the same assumptions of normality hold true for ANOVA as for
regression (don't have my stats text handy) -- but if so, that is good news,
as I would prefer to avoid transformation if at all possible!
From: Hector Maletta [mailto:firstname.lastname@example.org]
Sent: July 18, 2006 10:32 AM
To: Cathy.Underhill@statcan.ca; SPSSX-L@LISTSERV.UGA.EDU
Subject: RE: Standard error using GLM in SPSS/dealing with kurtosis
Regarding your first question I defer to others more knowledgeable than
myself. On your second question, I think you believe that the frequency
distribution of variables in your sample must be normal in order to apply
least squares procedures. This is not true, although a very common mistake.
Regression does not assume normal distribution in variables, either in the
population or the sample. What it assumes is normality of errors or
residuals, quite a different matter altogether.
Asymmetrical distributions may enlarge your standard error (without
invalidating the procedure, though) especially when outliers are present,
but more normal non-normal distributions (if you permit me a mild pun) may
hardly create any problem, least of all because of being a bit flatter than
Gauss's bell. Hector
De: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] En nombre de Cathy
Underhill Enviado el: Tuesday, July 18, 2006 10:59 AM
Asunto: Standard error using GLM in SPSS/dealing with kurtosis
I am new to this Listserv, but wondered if someone could help me with a
couple of issues:
* I am using GLM in SPSS for a mixed model (between-within) analysis
of variance and was told by someone that when using GLM the standard error
presented by SPSS is a "pooled" SE and is incorrect for my between-group
variables -- does anyone know is this is correct, and how I would obtain the
* Secondly, how serious is the impact of platykurtosis (one of my w-s
variables) on the final analysis (I have a relative small sample -- N = 144)
and does anyone have a recommendation on the best way to deal with this? I
have looked into transformations but from what I have read so far, there are
mixed opinions about this approach.
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