You had a stats introduction before (maybe some time ago) and know how to run a linear model in R, e.g. “lm(y~a+b)”. But you got a little bit rusty on interpreting the model output?
You ask yourself: What do these t-test and F-tests tell you? What is a post-hoc test? Why is there a difference between covariates and factors? How is an interaction treated?
Don’t worry, we will bring you up to date without having to dedicate a whole week. Based on an empirical dataset, we will recap reading model output and perform model testing for LM, ANOVA, ANCOVA, LMM (and some brief outlook on GLM).
Participants learn how to understand model output of R-functions lm(), aov(), and lmer(). They will be prepared for testing hypotheses based on their own datasets.