I have three treatment group and an inventory that consists of three subscale. There are 16 students in each groups (totally 48 students). I implemented three different techniques for treatment groups. I collected data before and after the implementation process. Normality and equal variance assumptions were not violated for parametric tests, so I can not use parametric test (ANOVA or MANOVA). On the other hand, I can not use Kruskal Wallis H test (non-parametric eqivelent of ANOVA) due to three subscales of inventory. In this case, which statistical techniques should I use? Do you know any non parametric equivelent of MANOVA in SPSS or another software. Or, Do I have to use a multivariate analysis because of the subscales of inventory.
Dear Mehmet!
First of all you wrote "Normality and equal variance assumptions were not violated for parametric tests" - this means you CAN use ANOVA.
I assume you wanted to write they were violated. In this case, you actually CAN run ANOVA, becasue SPSS carry out other tests too. In the output the top row always states "Sphericity assumed" , the next row is Greenhous- Geisser - you want to read this line, and write down the results form here (same applies to every variable)
IF you happen to have nominal variables, you will have to carry out a Log-linear analysis, but that's a whole new storry.
Best regards,
Andrew
Dear Mehmet!
First of all you wrote "Normality and equal variance assumptions were not violated for parametric tests" - this means you CAN use ANOVA.
I assume you wanted to write they were violated. In this case, you actually CAN run ANOVA, becasue SPSS carry out other tests too. In the output the top row always states "Sphericity assumed" , the next row is Greenhous- Geisser - you want to read this line, and write down the results form here (same applies to every variable)
IF you happen to have nominal variables, you will have to carry out a Log-linear analysis, but that's a whole new storry.
Best regards,
Andrew
Since you have before-after data, you likely want an alternative to ANCOVA, not ANOVA (see discussion of Lord's paradox, in particular Wainer 1991 Psych Bull and http://ftp.cs.ucla.edu/pub/stat_ser/r436.pdf). There are several alternatives for ANCOVA. One is allowing a flexible curve (like a spline) for the relationship between the pre and post score. Also, Rand Wilcox discusses a lot of "distribution-free" tests in his books. His ANCOVA based on trimmed means might be what you are looking for, and this is implemented in the ancova function in the R package WRS2 (and there is a bootstrap function with this which may also be useful).
SO, If the data is not normally distributed, and we want to do MANOVA, does the transformation to normal quantiles affects the results?
or any other alternative to MAONA?
I have 2 independent variabls (Site and Season) and may dependent variables(Salinity, SPM, POM, NH4 etc)
Which non parametric test should I use?
Here is a tiny article using a non-parametric approach rather than MANOVA:
- https://odajournal.com/2016/11/11/novometric-analysis-vs-manova-mmpi-codetype-gender-setting-and-the-macandrew-alcoholism-scale/
Here is a short review article that discusses the non-parametric method
- https://odajournal.com/2017/04/18/what-is-optimal-data-analysis/
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