- Thread starter rmil
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If the departures from normality in your 3 non-normal variables are quite serious, I'd suggest using non-parametric analyses for all of them - based on the argument that the non-parametric analyses are still valid for the normally distributed variables (albeit with some data reduction involved), while the parametric analyes would likely NOT be valid for the non-normally distributed variables. It'd certainly be less convoluted than doing parametric tests for the relationships between some pairs of variables and non-parametric tests for other pairwise comparisons.

Just a sidenote: on the off chance your 6 variables represent measurements of some kind of the same subjects, you would need to use repeated measures analyses rather than Kruskal-Wallis etc.

Hope that helps!

How can I check the skewness and kurtosis values?

SPSS doesn't give that in the output. It gives only significance level.

Just a sidenote: on the off chance your 6 variables represent measurements of some kind of the same subjects, you would need to use repeated measures analyses rather than Kruskal-Wallis etc.

Thank you for answering

How can I check the skewness and kurtosis values?

SPSS doesn't give that in the output. It gives only significance level.

How can I check the skewness and kurtosis values?

SPSS doesn't give that in the output. It gives only significance level.

Yes, all those 6 variables are measurments of the same subjects. So, you suggest that ANOVA would be better even though some variables aren't normally distributed?

Skewness values (for every single variable) are out of the range.

But, there's only one kurtosis value that's out of the range. It seems quite contradictory. How' s that possible?

P.S.

I've calculated ranges using instructions from this web site --> http://www.une.edu.au/WebStat/unit_materials/c4_descriptive_statistics/determine_skew_kurt.html

A better way to check whether skewness and kurtosis indicate significantly skewed distributions is to take the skewness and kurtosis values, and divide them by their respective standard error. This creates a z-value which must be compared to a cut-offs of -1.96 to 1.96 (based on normal distribution for p <. 05). If the z-values are within the range -1.96 to 1.96, then the variable is normal, if outside this range it is skewed. If you want to test for severe skewness, then use the cut-offs between -2.5 to 2.5. The only problem with this method that very large sample sizes have smaller standard errors, making it more likely to have significant z-values indicating skewness. A more useful guideline is to use a range for skewness and kurtosis of -1 to +1, within this range is normal. See this online book from Google:

http://books.google.com/books?id=Qb...mrjaoO&sa=X&oi=book_result&ct=result&resnum=1

Skewness values (for every single variable) are out of the range.

But, there's only one kurtosis value that's out of the range. It seems quite contradictory. How' s that possible?

P.S.

I've calculated ranges using instructions from this web site --> http://www.une.edu.au/WebStat/unit_materials/c4_descriptive_statistics/determine_skew_kurt.html

Z-scores for skewness are: 6.28; 3.32; 3.61; -2.53; -2.09; 3.93

Z-scores for kurtosis are: 4.85; -0.43; 0.51; 1.45; 0.57; 1.51

I guess that this means that distributions are definitely non-normal.

By the way, sample size is 123.

Thank you, guys, for helping me!