Thanks everyone for the helpful ideas. It appears that this will be more difficult than I thought. I don't necessary have an inclination toward p-values, but many journals certainly do. I would be willing to try to calculate the confidence intervals around the estimates, but I haven't gotten any functions to work when applied to glmer & (quasi-)poisson distributions. Alternatively, it appears that I could look at using model comparison, Bayesian (BUGS), or other software (SAS/Stata). Model comparison seems a little inadequate for reporting on fixed effects. I'm not yet very familiar with Bayesian approaches, so I would prefer to avoid that direction -- at least for now. As for other software, I may very well end up using SAS for confirmatory analysis (and potentially reporting), but for exploratory analysis, I'd prefer to stick with R to continue to learn about the ever-advancing approach to analyzing statistics. I'm still a novice with R, but I recognize that its flexibility and user community will make it a strong statistics package now and into the future. Thanks again guys! -- View this message in context: http://n4.nabble.com/Multilevel-modeling-with-count-variables-tp1692632p1693476.html Sent from the R help mailing list archive at Nabble.com.
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