You might use multiple regression in this situation, treating the dependent variable as continuous. An alternative is to use logistic or probit regression where you treat the dependent variable as categorical. Norms might vary across disciplines, and even within disciplines people have varying opinions about which is best to use.

Thank you Mr. Paul

The most appropriate regression is multinational logit regression. The factors can be coded (0,1,2,3,4).

The underlining data generating process (DGP) for this is type of data is similar to that of count data. This type of data is not continuous data. Traditional OLS regression is designed for continuous data. So, I will suggest you use Poisson Regression. Poisson regression is best designed to handle count data. To the best of my knowledge, most applied economics research papers use Poisson regression for count data.

Dear Paul, Nmadu & Ogundari,

So here is the situation that has occured. I have tried "Ordinal Regression" Using the IV as covariates in SPSS and the DV as stated above. The problem occurs as many empty cells are reported (85%) because IVs are in ratio scale.

While performing Linear multiple regression as suggested by Mr. Paul the problem of Homoscedasticity occurs. Although, multicollinearity is absent, regression standard residual are linear and normal. A big problem that could be seen is that although Correlation Analysis among all the DVs and IVs shows positive relationship. The Regression Analysis results in a negative constant unstandardized coefficient, which is matter of concern. What might have caused the CONSTANT unstandardized coefficient to be negative.