Re: st: Clustered Standard Errors vs HLM for Small Sample Project

[John Antonakis <John.Antonakis@unil.ch>]

Hi:

You should not use terms like "HLM" (which is a program in addition to 
an estimation method in some disciplines) without defining it (most here 
do not use this program but Stata obviously).

I guess I know what you are after, that is, whether you should estimate 
a random-effects (multilevel model), versus a pooled model using OLS 
with a cluster-robust estimate of the variance--. Before you do 
anything, and if you have level 1 (i.e., within cluster varying 
predictors), then you should be much more worried about omitted 
fixed-effects than just about robust standard errors--which are 
important too. See:

Halaby, C. N. 2004. Panel models in sociological research: Theory into 
practice. Annual Review of Sociology, 30: 507-544.

So, I would first check for omitted fixed-effects. If the Haumsan 
endogeneity test (can be tested with the user written command -xtoverid- 
from SSC) is significant, it means that he restrictions that your 
regressors don't correlate with the uj (i.e., the fixed-effect error 
term) is rejected. Then you either must model the fixed effects either 
with dummies or using the Mundlak procedure:

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. 2010. On making 
causal claims: A review and recommendations. The Leadership Quarterly, 
21(6): 1086-1120.

Next, as for the number of clusters ideally you'll have between 30-50 
for valid inference.

Hth.
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor:
The Leadership Quarterly
Organizational Research Methods
__________________________________________

On 18.11.2013 03:06, mkobren1@comcast.net wrote:
> I'm using STATA 10 and I'm trying to figure out whether to use clustered standard errors or HLM.I have 233 observations from agencies located in 10 different states.
>
> The minimum number of observations I have from a state is 3 and the maximum number of observations I have is 108 with an average
> of 23.3. I'm not interested in state level differences, I'm only interested in results from the agency level and I want to account for the fact that there may be some state level effects.
>
> The literature I've read so far doesn't seem to point me in any definite direction. The literature seems to say that HLM works best on larger datasets, but it also seems to say that you need at least 20 clusters for either method to be effective. Does anyone have a suggestion for which of these two methods I should use, or at least what I should consider in making my choice? Is there some other method I should use?
>
> Thank you in advance for your consideration.
>
> MK
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