Dear fellow Stata users,

I have some questions about the use of multi-level models.

I am working on a project concerning city effects upon individual-level health. I am working with a sample of individuals located within a multitude of United States cities (metropolitan statistical areas (MSAs)).

Using Stata’s cluster analysis command and many city-level variables, I have amalgamated these United States cities into 4 unique MSAclusters. My central independent variable is the variable based on these four MSAclusters, coded as three dummy categories and a reference category. My model will include a number of covariates at the individual level. My model will include no covariates at the city level.

I am planning on using Stata’s ‘mixed’ command. My current plan is to include a random coefficient based on respondents’ individual cities (not the four MSAclusters variable), as well as to include clustering of standard errors based on respondents’ individual cities (once again, not the four MSAclusters variable).

Would you advise I follow this plan given my central independent variable within the ‘fixed’ portion of my model is the four-category MSAclusters variable?

More generally, I am wondering if my research goal and my central independent variable (the four MSAclusters variable) allow for the use of single-level modelling instead of multi-level modelling. If my dependent variable is a set of ordinal categories of health, does this necessitate that I employ multi-level modelling? Can I instead use single-level modelling with an ordinal dependent variable? If my dependent variable is a continuous measure of health, would this allow for the use of single-level modelling instead of multi-level modelling?

I would very much appreciate any advice you can give me. Thank you in advance.

Yours sincerely,

Jason Settels