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<body class='hmmessage'><div dir='ltr'>Hi,<div><br></div><div>Maybe someone can help me with a question.</div><div>I collected data of 16 subjects in a repeated measures design with 2 within subject factors (time (3 levels) and arm (2 levels)). I measured several dependent variables.</div><div>Before applying a RM ANOVA to my data i first inspected each dependent variable (with its levels) regarding the assumption of normality. Some variables look positively skewed and some look negatively skewed. These deviations of normality can be corrected with a reflection first and then log transformation in the case of a negative skew distribution and the positive skew distributions can be corrected by a log transform alone. Some people say that the RM ANOVA is robust for mild violations of normality but I can not find any papers confirming this.</div><div>Now my question, should I apply these transformations to my data or not? And secondly, if I apply a transformation to one variable (for example the negatively skewed variables) should I also use this transform for the other variables or can I differentiate and decide the appropriate transform for each dependent variable?</div><div><br></div><div>Many thanks,</div><div><br></div><div>Best,</div><div>Emanuel</div> </div></body>
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