Hi all, I'm new (post #1!) and I hope you'll forgive me if I'm acting like an idiot...
I have been asked for some power analyses for some mixed-effects models I'm running using lmer. My studies nearly always contain mixes of repeated-measures and between-subjects predictor variables. As an example, suppose I want to see if men or women show a stronger word frequency effect. I have 50 words of varying frequency that I show to 30 men and 30 women, who are supposed to decide as quickly as possible whether it's a real word. So my data object would end up being 3000 lines long, and look like this: Subject Word Sex Frequency ReactionTime s1 w1 M 23 2543 s1 w2 M 67 1438 s1 w3 M 1 8033 ... s60 w50 F 4 1099 I analyze this with lmer(log(ReactionTime) ~ (Sex * Frequency) + (1|Subject) + (1|Word) Does anyone know how I might do power analyses or compute effect sizes in this kind of situation? Thanks. --Lee -- View this message in context: http://www.nabble.com/power-analyses-for-mixed-effects-lmer-models-tp21457651p21457651.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
