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Donalson (1968) observed that the results of ANOVA are robust even if there is no normality of the data.
So you shd go ahead applying t-test/ANOVA without worrying about the Normality of data.
Plz read this page:
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Dear Neeraj Sir and Mr. Sagar,
Thank you for this discussion. Actually, I also faced similar kind of confusion some months ago when I read a paper in which it was mentioned that Dependent variable was non-normal and still the researcher went ahead with Regression, t-test and ANOVA. You can check the Paper here.
Neeraj sir, kindly share your guidance on the following three points as per your convenience:
1. Though ANOVA is robust to non-normality (Donaldson-1968, as mentioned in Andy Field, 2020) but there is one more condition …that ANOVA can be robust to the violations of normality and homogeneity of variance, if “Sample sizes are equal across all conditions.”Â
Sir, should we give importance to this aspect also?…I am asking particularly with reference to this Paper in which samples sizes across all groups were not equal still they went ahead with ANOVA?
2. Secondly, Is it necessary that in Linear Regression, Dependent variable (cumulative score of likert statements) should be normally-distributed?
I am aware that assumptions of regression require- homoscedasticity (residuals should have same variance) and normally-distributed errors (residuals in the model should be normally-distributed) but I am not sure that whether regression requires normally-distributed DV also or not?
3. Lastly, in general, if t-test and ANOVA are robust enough to violate the assumption of normality then what the - Basic difference remains between Parametric and Non-Parametric tests?
Kindly guide sir.
Thankyou and Regards,
Dr. Sukhmani.
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