Hello every , I have a sample of a study contain both of qualitative (categorical) and quantitative data. I want to know which model or estimation method I have to execute in the aim of measuring the effect of the independent variable on the dependents variables.
Thank you
I am guessing categorical X and quantitative Y (as you do not say - and it is crucial)
https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-dummy-coding/
https://stats.idre.ucla.edu/spss/faq/coding-systems-for-categorical-variables-in-regression-analysis/
and this one considers categorical and continuous Xs
https://stats.idre.ucla.edu/spss/library/spss-libraryhow-do-i-handle-interactions-of-continuous-andcategorical-variables/
Regarding the mechanical procedures you can use to do this, I think Kelvyn Jones has given you good guidance. Again mechanically, I think an ordinary least squares model will work fine. However, I want to provide a caution. This and any other procedure you use with these data will be a correlational in nature and you CANNOT establish causality with a correlational model. Only an experimental model can be used to test causality. Thus, it will not measure "effect," only relationships. I should add that a significant relationship is necessary for causality, but not sufficient.
Good luck,
Jim McLean
Abdelhammid Bourouaha
The question is ambiguous. How many dependent variables does your model consider? What is the nature of the dependent variable? (categorical, ordered categorical, metric scale). For example, if the dependent variable is only one and categorical with two categories, you can use a logistic regression. If the dependent variable is continuous in nature (metric scale), classical linear regression is recommended. In the case of having several dependent / independent variables you can use a path analysis with observed variables. Can you specify more your question?
Best Regards
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