Comparing more than two means of continuous variables I have a small sample of 11 people and their results on 5 intelligence test subscales. My hypotheses are that subscale's 1 mean will be significantly different than subscales' 3, 4 and 5 means and that subscale's 2 mean will be different than subscales' 3, 4 and 5 means. Levene's test says variances of subscales are equal (p>0.05).
What is the best way to test these hypotheses, which test and how to do it in SPSS? Is there a need for a e.g. Bonferroni correction?
 A: The easiest option will be a linear mixed model, if you are willing to use it.
Put all of your 5 independent variables in one column, create two new categorical variables, one for test id and one for individual id.
Here is how you would do it in R (don't know SPSS):
> dat=data.frame(
>   "score"=unlist(mtcars[,c("disp","hp","drat","wt","qsec")]),
>   "test"=factor(rep(c("disp","hp","drat","wt","qsec"),each=nrow(mtcars))),
>   "individual"=factor(rep(1:nrow(mtcars),5))
> )
>
> library(nlme)
> summary(lme(score~test,random=~1|individual,data=dat))

Fixed effects: score ~ test 
                 Value Std.Error  DF    t-value p-value
(Intercept)  230.72188  11.19880 124  20.602372       0
testdrat    -227.12531  14.47111 124 -15.695083       0
testhp       -84.03437  14.47111 124  -5.807043       0
testqsec    -212.87313  14.47111 124 -14.710212       0
testwt      -227.50462  14.47111 124 -15.721295       0

A: You could use ANOVA, which will tell whether there is a difference between any of the means.  If there isn't then you are done.  If there is, then you proceed to figure out which means are different. Read up an ANOVA, and post ANOVA.
