mormonsandscience

Roy,
Explaining poisson regression in Gpower is next on my list. I will try to get to it soon. You may try http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/user-guide-by-distribution/z/poisson_regression

The Gpower authors recently posted a tutorial on poisson regression power analysis.

Greetings Nicolas,

I am glad you’ve found the G*Power guide useful. I hope I am able to answer your question.
Unfortunately G*Power can be somewhat confusing when it comes to MANOVA.

One problem is that some people call a design MANOVA when referring to univariate repeated measures designs. In this case, the M in MANOVA refers to “multiple” as is multiple/repeated measures. While others, such as myself and G*Power included, call a design MANOVA when referring to “multivariate” designs with more than one outcome variable (DV).

Another problem is that under G*Power’s drop down list for F tests it says that it can run calculations for “MANOVA: Repeated measures”. No doubt people have interpreted this to mean that the program can run power and sample size calculations for repeated measures, multivariate analysis of variance (RMMANOVA), sometimes called doubly multivariate ANOVA. RMMANOVA involves measuring multiple outcome variables (DVs) on the same group of people multiple times (e.g., time1, time2, time3). If this is what you’re trying to accomplish then I am sorry to say that G*Power cannot do this. I even contacted one of G*Power’s creators and he confirm that G*Power cannot handle RMMANOVA, which is unfortunate because it is a common research design.

So what kind of analysis does G*Power’s “MANOVA: Repeated measures” refer to? It is the multivariate approach to analyzing univariate (one DV) repeated measures data. This can be seen more clearly when you mouse click on Tests along the top, then go to means, and then go to “Repeated measures: between factors, MANOVA-approach” in the drop down list. The MANOVA approach is commonly used in univariate repeated measures when the Sphericity assumption does not hold in univariate analyses. The multivariate approach to univariate repeated measures (RMANOVA) treats the outcomes as being comprised of multiple DVs obtained on a single group of people.

If you are running a true repeated measures multivariate ANOVA (RMMANOVA) then I recommend generating random samples of data (using expected means and standard deviations) for each group of the within subjects factor (e.g., time1, time2, time3) and then run these through SPSS. Ask SPSS to give you the power calculation each time you run the RMMANOVA. Do it multiple times (with a new set of generated means and SDs) and average the power calculations that SPSS gives you. If you need help on generating random data for specific expected means and SDs, I can give you a site that does this nicely. You only need to copy and paste the randomly generated data into SPSS.

If you are looking at powering a univariate repeated measures design then I recommend using G*Power’s “ANOVA: repeated measures” function. Instructions on how to use this function are included on my website. You should only use the multivariate equivalent if you expected that the sphericity assumption will not be met, which is unlikely in most cases. For this reason I think that adding the MANOVA equivalent test for univariate repeated measures in G*Power was superfluous at best, and confusing at worst.

Please let me know if you have any more questions.

Nicolas,

I've included some replies to your questions.

So, what i am trying to do is to see if two groups of subjects differ regarding a variable measured 5 sequential times in each subject. In this case I assume the correct test is a univariate repeated ANOVA testing for between-subjects effect (setting group as the between-subjects factor).

- I agree. So to calculate power for the between subjects effect in a univariate, repeated measures ANOVA you use the G*Power “ANOVA: Repeated Measures, between factors” analysis. On the other hand, if you wanted to know power for testing the within subjects factor (5 sequential times), then you would use the “ANOVA: Repeated Measures, within factors” analysis.

What is the mean in each group that should be entered in G*Power (ANOVA, repeated measures, between factors) for effect size calculation? The mean of the means for the 5 measurements in each group or the mean of all data pooled for each group?

- The means being asked for are those for each group across all 5 measurements (the mean of all data pooled for each group). Calculate the means for all data points for group 1 and group 2 while ignoring the times when those data were collected, then enter these 2 means into the appropriate cells.

I am confused because the variable was measured 5 times in each subject. And what if the SDs (for the overall means)differ between the 2 groups? I should use the mean of the two SDs in the G*Power?

- Yes, use the mean of the two estimated SDs.

Instead of this, could I use the partial eta squared for the pilot data in the SPSS derived from repeated-ANOVA for the effect of group? It seems much more comfortable to me!

- Yes. Under the “Select procedure” tab, chose “effect size from variance” and enter the SPSS partial eta squared in the appropriate box. This is the direct approach. Eta squared conventions are small=.01, medium=.06, large=.14. I would compare the results from using eta squared and using the means. They should be similar. If you have preliminary data in SPSS, then ask SPSS to give you an estimate of power in your output file. This should also be similar to the G*Power calculations given that they use the same data.

I know this is all too much, but I cant stand also to ask: If the power calculation is done with normally distributed pilot data, but the final data turn out not to be(and therefore analysed with a non-parametric test), is there a problem with the validity of power calculation?

- No. Power and sample size calculations are simply a best guess analyses of your chances of correctly rejecting the null hypothesis if your data turn out the way you expected them.

And finally, what is done when the pilot data for power calculation are non-parametric or someone wishes to calculate observed power for not-normally distributed data? I havent found any answer to this case anywhere(I dont know if i am so unlucky, but my final data are always eager for non-parametric tests!!)

- If your preliminary data are not normal, you may consider doing a natural log transformation to bring them within normal parameters, that way you could stick with ANOVA. If you did this you could just enter the power calculation parameters into G*Power for the log transformed data. On the other hand, you could analyze your data using the nonparametric equivalent of the RMANOVA, called the Friedman test, although, as you point out, it is difficult finding a power calculation tool for this test. Anyway, here is what I would do if the preliminary data were not normal. I would power everything for a RMANOVA and add extra participants to the study if the expected power is not at least 90% or higher to compensate for the possibility of having to resort to a less powerful nonparametric test in the event that the final data turn out to violate the normalcy assumption.

I hope this helps.