# Clustered standard errors vs. multilevel modeling?

I've skimmed through several books (Raudenbush & Bryk, Snijders & Bosker, Gelman & Hill, etc.) and several articles (Gelman, Jusko, Primo & Jacobsmeier, etc.), and I still haven't really wrapped my head around the major differences between using clustered standard errors verses multilevel modeling.

I understand the parts that have to with the research question at hand; there are certain types of answers you can only get from multilevel modeling. However, for example, for a two-level model where your coefficients of interest are only at the second level, what is the advantage of doing one method over the other? In this case, I'm not worried about making predictions or extracting individual coefficients for clusters.

The main difference I've been able to find is that clustered standard errors suffer when clusters have unequal sample sizes and that multilevel modeling is weak in that it assumes a specification of the random coefficient distribution (whereas using clustered standard errors is model-free).

And in the end, does all of this mean that for models that could ostensibly use either method, we should be getting similar results in terms of coefficients and standard errors?

Any responses or helpful resources would be greatly appreciated.

• user Stask has a nice answer to exactly this question. Jun 3, 2013 at 19:37
• Thanks. I did read that before, which actually made me more skeptical of the real benefits. However, I guess the real motivation behind my question is to see whether I'm at all validated in thinking that it's not overly useful if I'm only looking at level-two coefficients as being of interest. In addition, perhaps I missed it, but I don't think that post addressed whether these two methods should be producing similar results (when assumptions of both methods are met). Jun 4, 2013 at 5:48
• With "coefficients at the second level" you mean the level where the you the parameters of the first stage as dependent variables?
– sheß
Sep 2, 2015 at 23:33
• Yes, that is what I mean. Sep 18, 2015 at 18:19