# Likert-normality and multiple regression assumptions?

I have a questions measured through Likert scale (1-5)

Independent variables: I believe xx is a problem strongly disagree(1) disag neutral agree s-agree(5)

I believe yy is a problem strongly disagree(1) disag neutral agree s-agree(5)

Dependent variable xyz (1,2,3) ---not a binary

I am planning to run some tests.

I found there are lots of debate on parametric vs non parametric on it.

Some test needs normality. What about normality test in this case? Do I need to carry out normality test on Likert scale.Or is it logical?

but lets suppose that in question 1, most of the respondents agree that xx is a problem(4 or 5), so how would data be a normal distributed? obviously it will be skewed(right).

So does normality make sense in it? (unless the result is kind of neutral). I saw lots of debates going on but for an academic research what can i do? (Likert scale score vs category score or continuous and so on)

Since my dependent variable is not binary, I am planning use multiple regression(cannot use logistic regression, but log regression is much lenient on assumptions), but problem is for some reasons my all questions replies are not meeting the criteria for multiple regression (especially normality of data, and standard residual).

so what kind of do? Is it needed to carry out all assumption test to run multiple regression on likert scale question?

What may be my options(to predict or test the hypothesis)?

thank you

• 1. Likert items are plainly not normal stats.stackexchange.com/search?q=normality+likert -- there would be no point in testing something you already know for sure... however 2. IVs are not assumed to be normal in regression. 3. Your DV is not normal either (though it's not the raw/marginal DV that is assumed to be normal), not even close. You don't state what the 1,2,3 values represent -- is that categorical? Ordered categorical? A count? Likely-to-be-suitable models for each of those cases are discussed in posts on site already. Please clarify the nature of your DV. Oct 17 '17 at 22:54
• thanks for your comments. DV is categorical, 0=not present,1=low presence,2=high presence. I also found that there is something called orginal regression, what may be good in my case. I am trying to take IV1,IV2 and trying to test effect on DV(hypothesis). Oct 18 '17 at 2:05
• Please edit that additional (critical) information into your question (and also change "screwed" to "skewed" in the question). That would be ordinal regression rather than orginal, and yes, that might be a good choice for a DV with ordered categories, depending on what you're trying to find out. What is the actual question you're trying to answer? (in plain words, avoid any hint of statistical jargon please) Oct 18 '17 at 2:14
• my hypothesis is: A factor(IV-multivariate) is positively related to intention to use something(DV). Oct 18 '17 at 3:08
• HOW many IVs or factors you have ? What are your question items? Oct 18 '17 at 5:26