### Power/Effective Sample Size

Posted:

**Wed Nov 29, 2017 1:36 pm**My name is Jillian and I am currently using three-level multinomial logistic regression in MlwiN to look at student substance use outcomes. I am trying to see what I am actually powered to look at (in terms of how many variables I can have in my model and how I have to operationalize my outcomes).

I want to be able to look at "regular users" differently from "occasional users" but my n for regular users gets rather small.

I have found that MLwiN produces effective sample sizes in column diagnostics. I am wondering if this takes into consideration the three-level structure of the data as it appears I am only selecting a single column without identifying my levels? I cannot find any documentation online that describes column diagnostics and this effective sample size calculation.

From my diagnostics, the Effective sample size for marijuana is 3387. I think this means that MLwiN is calculating the design effect as about 3 (10058/3387). Therefore, if we divide the n of regular users (~647) by 3 (design effect) we get 216 as the effective sample size for regular users.

Similarly for tobacco users, the effective sample size would be 148 (for n=400) and we would still be powered for our analyses (6 variables in the model).

I would truly appreciate if you would be able to let me know if I am interpreting this correctly, and if not, how I would go about calculating the effective sample size to determine power.

I want to be able to look at "regular users" differently from "occasional users" but my n for regular users gets rather small.

I have found that MLwiN produces effective sample sizes in column diagnostics. I am wondering if this takes into consideration the three-level structure of the data as it appears I am only selecting a single column without identifying my levels? I cannot find any documentation online that describes column diagnostics and this effective sample size calculation.

From my diagnostics, the Effective sample size for marijuana is 3387. I think this means that MLwiN is calculating the design effect as about 3 (10058/3387). Therefore, if we divide the n of regular users (~647) by 3 (design effect) we get 216 as the effective sample size for regular users.

Similarly for tobacco users, the effective sample size would be 148 (for n=400) and we would still be powered for our analyses (6 variables in the model).

I would truly appreciate if you would be able to let me know if I am interpreting this correctly, and if not, how I would go about calculating the effective sample size to determine power.