Dear All,

I have sample data measuring intake of food at the individual level. The data is collected in a way that at the first stage there are divisions followed by districts, villages, households and individuals.

Since the data is hierarchical a multilevel (Stata's mixed command) can be a possible choice.

I have read posts on the “
by Clyde Schechter.

However I am still not clear about the following points

What factors would justify the choice of a multilevel model over clustered standard errors?

Given that the data has several levels isn’t is that clustering standard errors would not account for all the levels?

Can/ should clustering be included in the multilevel model?

I have sample data measuring intake of food at the individual level. The data is collected in a way that at the first stage there are divisions followed by districts, villages, households and individuals.

Since the data is hierarchical a multilevel (Stata's mixed command) can be a possible choice.

I have read posts on the “

**Choice between multilevel model and clustered standard errors**”, includingCode:

https://www.statalist.org/forums/forum/general-stata-discussion/general/1472336-mixed-effects-multilevel-model-vs-cluster-command

However I am still not clear about the following points

What factors would justify the choice of a multilevel model over clustered standard errors?

Given that the data has several levels isn’t is that clustering standard errors would not account for all the levels?

Can/ should clustering be included in the multilevel model?

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