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Date:         Thu, 16 Jun 2011 14:16:30 -0400
Reply-To:     Rich Ulrich <>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Rich Ulrich <>
Subject:      Re: help me
Comments: To:
In-Reply-To:  <SNT106-W63A3F9F5257E1001E3A48CB46B0@phx.gbl>
Content-Type: multipart/alternative;

The usual proper use of a power analysis is to describe, even before data collection, what the chances are that your tests will be able to detect ("find significant") effect sizes that are (a) interesting, and (b) at least somewhat likely to be seen, given the range of Ns that is being considered.

In your instance, it *might* be reasonable that someone could be asking that you show, for your sample, how small of an effect could have been detected, say, with even 50% power. If some of your tests were not significant, it could be interesting to remark about how large of an effect could have been missed. (I also consider it possible that the person who asked for your power analysis is an idiot, who is mindlessly echoing, inappropriately, jargon that he has heard in the past. It possible to make some descriptive statement, after the analysis, referring to power, but I agree with the poster who says that giving confidence limits is often going to be more informative, to more readers.)

A sample size of 600 is "large" for what Cohen was interested in, for the usual studies in psychology and social sciences, and their usual effects. However, there are other topics where 600 is far too small. The study which showed that aspirin after a heart-attack is a good idea was the first "mega-study" using multiple sites, and it was designed from the power analysis which showed that 20,000 patients was a good number, if one wanted to show that a treatment that would cut in half the immediate fatalities was worthwhile.

What is an "effect size"? For a dichotomy like a dummy variable, predicting another dichotomy, the odds-ratio is a good measure of effect. However, the Odds Ratio is sensitive to the actual sizes of the proportions, so you often need to anchor the description in the actual proportions. That is, there is a 20-point difference between 40% and 60%, or between 10% and 30%; but the latter is a more severe difference than the former.

For your study, your purpose is some sort of illustration -- Obviously, you had *enough* power for some analyses, since you did have positive findings. I think I would perform the power analyses one variable at a time. That makes the presentation easier and clearer, and it is often the approach to start with, even the power analysis is done before the experiment.

-- Rich Ulrich

Date: Wed, 15 Jun 2011 21:00:31 +0000 From: Subject: Re: help me To: SPSSX-L@LISTSERV.UGA.EDU

Dear Gene and Evan,

Thank very much for your help me.

I got a model (GEE) with 5 independent variables (2 and 3 were categories were continuous covariates), these are all significant. Not consider covariates did not influence my model. As this analysis is an analysis of a secondary database, I have requested that I submit the statistical power. My English is not very good, I hope I could make myself understood. My understanding is that the sample is large gives a high statistical power, so I do not understand is referred to the effect size.

Thanks in advance.


[snip, previous]


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