I have variables a1 and a2 with 57 observations each, and variables b1 and b2 with 29 observations each.
I compare a1 with a2 by means of a paired-samples t-test, this gives p<0.001. Next, I compare b1 with b2 by means of a paired-samples t-test, this gives p>0.05 (actually p>0.10).
Mixed-effects models are wonderful for analyzing data, but it is always a hassle to find the best model, i.e. the model with the lowest AIC, especially when the number of predictor variables is large.
Presently when trying to find the right model, I perform the following steps:
I'm doing pairwise comparisons (Bonferroni) in GLM using SPSS. One (probably stupid) question: are the p values one-sided or two-sided?
If they are two-sided, how do I get one-sided p-values?
I'm doing an MANOVA with two dependent variables and one fixed factor (three levels). It is said that I need to test for multivariate normality.
What is the right way to test for multivariate normality in SPSS?
Do I need to test per level or all levels together?
When obtaining ANOVA results with SPSS we also get a R-squared and an adjusted R-squared. For a multi-way ANOVA the eta-squared is given per factor or interaction.
The measures are criticized by - among others - Andy Field:
"However, this measure of effect size is slightly...