Hello!! my problem is that i have normal and no normal data distributions in the subscales of a questionary!!
What i could do?, two different analisys? or can i normalice the data?
you can use both method two analyse the data.
Thank you all for your insight to the question.
These are very useful
Regarding some of the comments above, nonparametric tests are not distribution free. They assume that the distributions, whatever their shape, are similar in form and only differ in location (e.g., an ANOVA or t-test). Cases of heteroscedaticity mean that the populations being compared have different forms and so nonparametric tests are no better (and in some cases, more biased) than their parametric counterparts. Search for a paper by Zimmerman (1998) as a reference
Paul, I don't think this is correct the way you wrote it.
For instance, the Wilcoxon test does not require that the distributions of the compared populations are equal. This test is just often described (or "sold as") as test on a location shift, or a test on the "difference in medians". When Wilcoxon test is used to test this, then, and only then, it requires that the population distributions are equal except for their location. Otherwise it tests the stochastic inequality: the tested null hypothesis is P(X>Y)=0.5. The problem is that many users are not aware of the tested null and what it means.
I still want to wave a waring sign when people compare different tests, like saying that the t-test would be a "counterpart" or an "alternative" to the Wilcoxon test (or vice versa). This is very misleading because these two tests are about different (null)hypotheses. The Wilcoxon test does not test expected differences - again only with the exeption of the very special case that the distribution shapes are equal and symmetric! If a test on expected differences is desired, and the asusmptions for a t-test are considerably or obviousely violated, the Wilcoxon test is usually just not a viable alternative! It would test a different kind of hypothesis! But the much more interesting question is why the assumptions of the t-test are violated. It is possibly not very sensible to ask for an expected difference in the first place. It is often the case that not the value itself but some underlying parameter of a data.gerenating model is what should be infered (like, for instance, a proportion, a rate, a count-expectation ore something more complex). This would start a process of thinking about the data and open possibilities to get to a better understanding and new insights. The usual mechanic procedure of "oh, t-test won't work, so I will use Wilcoxon" is a rather dumb escape and misses/ignores the most important point: to understand your data.
From Kruskal and Wallis' original paper: "Only very general assumptions are made about the kind of distributions from which the observations come. The only assumptions underlying the use of ranks made in this paper are that the observations are all independent, that all those within a given sample come from a single population, and that the populations are of approximately the same form." (Kruskal & Wallis 1952:585).
Hello, my data doesn't follow normal distribution. They are obtained from behavioral experiment. I am using Poisson distribution. What should be my approach?
If your data are counts your options are:
Poisson modelzero-inflated Poissonnegative-binomial modehurdle models
@Jochen, Thank you for the info..I have count, duration and latency data.. The duration comes after counting and carefully recording the timing of that behavior. Can I use poisson for both duration and latency also?
Iqra National University
Blake M Warner
University of Pittsburgh
University of Valencia
Federico Y. Fontana
University of Verona
Shamrulazhar Shamzir Kamal
University of Copenhagen
William Jewell College
Lakehead University Thunder Bay Campus
North Dakota State University
University of Guelph
University of Nebraska at Kearney