Posted by
Dale Glaser on
Jun 21, 2006; 12:05am
URL: http://spssx-discussion.165.s1.nabble.com/SPSS-MVA-tp1069196p1069199.html
Deborah, I'll be interested to see responses from others, as I don't think there will be an ironclad truism, but here is my two-pence
(1) even though often we hear that ANOVA is "robust" to moderate violations of the assumptions, there are some papers that show this is not necessarily the case when sample size markedly varies across groups (e.g., if the larger variance is associated with the larger group tests of signficance tend to be conservative); so, to the extent that your design is relatively balanced, that will be of less concern that if it is not.
(2) Though there are myriad opinions about transformations (e.g., log, reciprocal, etc.), if the normality assumption is not tenable, attempt a transformation (one ideally that can be justified) and see if your general results/conclusion holds
(3) You can always resort to a nonparametric analogue (e.g., Kruskal-Wallis) and again check if the general results obtain
(4) and for the kicker: a p-value, regardless of the value, does not not necessarily mitigate violations of assumptions or relieve the researcher of concern about such. Null hypothesis significance testing has a long and storied history of misapplication and misinterpretation (see the edited text by Harlow et al. titled "what if there were no significance tests), and a 'smaller' p-value, though some would say this is prima facie evidence of "more" power, does not tell us anything more than the probability of the result given a true null hypothesis.....it can't tell us about replicability, and as many authors from Cohen on have emphasized, it doesn't yield information about magnitude (i.e, effect size). So, I would be very wary about abdicating concern about violations based on a p-value, as it should be subordinated to design and measurement issues.
Again, just my opinion!
Dale Glaser
Deborah Pearce <
[hidden email]> wrote:
Dear All,
I am an Ecologist/physiologist and thought I had sufficient understanding
of statistics for my purposes. However, I am being trouble by something
simple. When using something like ANOVA if for example assumptions of
normality or equality of variances are not met is this less important if
the output is highly significant e.g. P less than 0.00001 rather than P
only slightly less than 0.05? I am aware that this is a stats question
rather than an SPSS question so you may simply want to suggest another
listserv I could use. I am not finding the answer in textbooks.
Thanks in advance,
Deborah
Dale Glaser, Ph.D.
Principal--Glaser Consulting
Lecturer--SDSU/USD/CSUSM/AIU
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