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Hold what you would consider to be equal increments of the (say) arbitrarily rescore all values so that equal-increments do Of what the other numbers represent - such that you cannot What comes to my mind as a potential cause of "non-normal"Įxpectations is that you have a bunch of scores at zero What you want to figure is what transformation would make theĪverage changes across lags be homogeneous for the low scorers Really minor distortions, but, then, those would not be much concern. It is useful to look at the lagged scatterplots to figure what the Is this a reference to a conventional transformation?ĭata collected across time are often correlated well enough that I cannot achieve this assumption." Huh? You are jumping some step. I don't know what you mean when you say, "even with the correction, Possible? Is one reasonable? - What are you measuring? The bestĬlue for what transformation is reasonable is the knowledge of where The syntax is potentially ambiguous, but I think you say that youĮxpected "not normally distributed". You might show? Does an analysis of ranks show it? Is this just hypothetical froth, or do you have something that Do you have any effects apparent when you plot your data? Make assumptions that you might need to justify for an audience. Of the ranks (logit or probit) might rescue that, but that step does If the residualsĭo not have outliers, the testing is probably okay. Under-rate it, or both (in varying circumstances). I am going to say some more about normality and non-normality.īeginners often either over-rate the importance of normality, or I like Bruce's comments he hits a number of good points. Rank-based procedure may introduce more problems than it solves,ĭepending on how similar the population shapes are. Wilcoxon-Mann-Whitney test under scrutiny in the title). The journal Statistics in Medicine, IIRC-something about the Google for articles by Morten Fagerland (in Tests are very sensitive to heterogeneity of variances and smallĭifferences in skewness. When they are used to test hypotheses about location, rank-based Normality? If you perform the mixed design ANOVA you would like to doĪnd save the residuals, how are those residuals distributed?ģ. Normality within groups, not of the DV overall). The normality assumption for ANOVA is normality of the errors (i.e., They are models that can be useful under certainĢ. As George Box reminded us, normal distributions and straight lines do > Unfortunately my data are not normally distributed (as expected)and even with the correction I cannot achieve this assumption.ġ. > I have different DP variables but I want to analyse these one by one. > In each condition 10 subjects (total 20) between subject (condition) with 2 levels