For an ANOVA class assignment, we are doing post-hoc tests for interactions. For simple effect analysis on an interaction, I went through a really convoluted process to get my statistics and want to know if there is a more straightforward way. I have a 3x2 design with self-concept 1:3 (low, moderate, high) and gender 1:2 (male, female). For the assignment, I am checking to see the influence of interaction on some arbitrary score (the data is all made up for the sake of the assignment). The first step I took was to create my data frame and then do an omnibus F-test and check to see whether or not interaction is statistically significant. summary(aov(srl ~ gender * selfconcept, data=data)) The interaction is statistically significant. I plotted the interaction and decided to do a simple effect analysis. This is where I was stuck. I am a first year graduate student and very much an R novice. I am learning as I go through my statistics classes so even though I am optimistic, this is a rough to learn. I decided to create subsets of self-concept and test post-hoc through one a series of one-way ANOVAs. Low <- subset(data, selfconcept=="low") Moderate <- subset(data, selfconcept=="moderate") High <- subset(data, selfconcept=="high") And then? summary(aov(srl ~ gender, data=Low)) summary(aov(srl ~ gender, data=Moderate)) summary(aov(srl ~ gender, data=High)) This worked?kind of. It gave me the between groups mean square I needed but an inaccurate error (for my purposes anyhow). I got the omnibus error term, calculated the F ratio and used the F-table out of the book to determine significance. There is probably a much better and more efficient way of doing this, and I need the advice of somebody who is much cleverer with R than I. -- View this message in context: http://r.789695.n4.nabble.com/Simple-Effects-Analysis-for-ANOVA-tp3900635p3900635.html Sent from the R help mailing list archive at Nabble.com.