u/mohdd22

Hello,
I have a dataset I got from my likert scale questionnaire (16 questions for IV and 14Q for DV) n is 66, and I need to study the relationship of the variables, and I thought linear regression is the best for this type of situation since its common and used in most previous dissertations, I did normality on residuals and got sig above 0.05, but the teachers in the uni keep telling me to do it on variables instead which makes my normality test fail at values under 0.05, what do I do? how do I convince them and if there is a better way to study the relationship without normality tests im down for it, the Q-Q plot is ok all the dots are close the line but the teachers still refuse to accept it without the normality test on variables.

Hey everyone,

I am working on my master's thesis and running into an issue with assumption testing before Pearson correlation and simple linear regression.

My sample is n=67, and I ran the Shapiro-Wilk test on my two composite variables (independent and dependent). Both came back significant:

Variable 1: W = 0.916, p < .001

Variable 2: W = 0.961, p = .034

So technically normality is violated according to the test. The Q-Q plots look fairly clean with only minor deviation at the lower tail.

My questions are:

Is it acceptable to proceed with parametric tests given that Shapiro-Wilk is significant but the Q-Q plots look reasonable?

Is the Central Limit Theorem argument valid here given n=67?

Should I be switching to non-parametric alternatives instead?

This is a survey-based study using Likert scales in a management/organisational context if that matters. Any advice appreciated.