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Date:         Tue, 18 Jul 2006 15:11:42 -0300
Reply-To:     Hector Maletta <>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Hector Maletta <>
Subject:      Re: Standard error using GLM in SPSS/dealing with kurtosis
Comments: To:
In-Reply-To:  <>
Content-Type: text/plain; charset="iso-8859-1"

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.


-----Mensaje original----- De: [] Enviado el: Tuesday, July 18, 2006 12:06 PM Para: 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!


-----Original Message----- From: Hector Maletta [] Sent: July 18, 2006 10:32 AM To:; SPSSX-L@LISTSERV.UGA.EDU Subject: RE: Standard error using GLM in SPSS/dealing with kurtosis

Cathy, 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

-----Mensaje original----- De: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] En nombre de Cathy Underhill Enviado el: Tuesday, July 18, 2006 10:59 AM Para: SPSSX-L@LISTSERV.UGA.EDU 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 correct values?

* 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.



Cathy Underhill 613-951-6023 | facsimile / télécopieur 613-951-4179 Statistics Canada | 170 Tunney's Pasture Driveway Ottawa ON K1A 0T6 Statistique Canada | 170, promenade du Pré Tunney Ottawa

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