Tests of Normality

Kolmogorov-Smirnova

**a**Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

Q1 .154 794 .000 .911 794 .000

Q2 .238 794 .000 .880 794 .000

Q3 .249 794 .000 .882 794 .000

Q4 .168 794 .000 .911 794 .000

Q5 .165 794 .000 .914 794 .000

**a**. Lilliefors Significance Correction

The above sig values are not supporting the normality of data, as it is less than 0.05. (For all 50 questions - sig value is .000). BUT

www.psychwiki.com/images/7/79/Lab1DataScreening.doc - advises in page 9 that - “Test of Normality” box gives the K-S and S-W test results. If the test is NOT significant, then the data are normal, so any value above .05 indicates normality. If the test is significant (less than .05), then the data are non-normal. In this case, both tests indicate the data are non-normal. However, one limitation of the normality tests is that the larger the sample size, the more likely to get significant results. Thus, you may get significant results with only slight deviations from normality. In this case, our sample size is large (n=327) so the significance of the K-S and S-W tests may only indicate slight deviations from normality. You need to eyeball your data (using histograms) to determine for yourself if the data rise to the level of non-normal.

Now i am confused about my future course of action!

Can i go ahead with the same set of data for parametric tests?

Looking forward for help desperately!

Regards

Chidams