Posted by
Poes, Matthew Joseph on
Feb 17, 2012; 8:42pm
URL: http://spssx-discussion.165.s1.nabble.com/Sample-size-in-GLMM-tp5493887p5494577.html
I would need to know more about your study design to know if the following advice is appropriate, but the short answer is, Yes that is roughly right. The longer answer is, it depends, you might need more, you might be able to get away with less. I'd suggest a power analysis using the Optimal Design software. It's built around RCT education studies primarily, but can be adapted to a wide range of experimental trial studies which may have similar conditions. The newest edition allows for analyzing your design in the context of the addition of multilevel cv's, and what impact this has on power.
I'm currently the primarily analyst of a study with a little under 40 treatment/control schools, and with the CV's we are using, we are estimating the MDE to be around .15 to .2. When you reduce that number in half, you still have power such that the MDE is around .25 or so, more than adequate in any typical education study. There is also evidence from other studies that the MDE's generated in these power analysis are way too high, and that we may in fact have the power to detect effects as small as .1 or less with the 40 schools, and thus .15 to .2 with half as many schools.
-Matt
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of George Siardos
Sent: Friday, February 17, 2012 1:07 PM
To:
[hidden email]
Subject: Sample size in GLMM
Dear list members,
I would greatly appreciate if you could inform me about the limitations set for sample sizes in performing Generalized Linear Mixed Models and specifically a multinomial logistic mixed model. Could you suggest me any rule of thumb for MLM to use?
Some years ago GLMM experts suggested that for MLM regression models the higher level sample size be at least 20, preferable 50. Is this valid for binary and/or multinomial logistic regression?
I really appreciate your kind reply.
Geoge C.S.
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