Consider the following setup: two mouse strains ("KO" and "WT") have been compared in three independent experiments ("E1", "E2" and "E3"). In each experiment, there were two groups corresponding to the two mouse strains compared, and each group consisted of four different mice. There were all in all 8 mice per experiment, and three experiments (total of 4 x 2 x 3 = 24 mice).
The question is, of course, whether the strains differ.
In R, I would do this as follows, given the variables
a <- data.frame( response= c(100, 110, 120, 130, 200, 210, 220, 230, 105, 115, 125, 135, 205, 215, 225, 235, 105, 115, 125, 135, 215, 225, 235, 245), experiment= rep( c( "E1", "E2", "E3" ), each= 8 ), strain= rep( rep( c( "WT", "KO" ), each= 4 ), 3 ) ) myaov <- aov( response ~ strain + Error( experiment ), data= a ) summary( myaov )
However, I have been asked this question by persons who work on a regular basis with GraphPad Prism. It does feature "repeated measures ANOVA" which is (in the help file) said to be equivalent to random block design, but I find the explanations somewhat confusing:
"Repeated measures" vs. "randomized block" experiments
The term repeated measures is appropriate when you made repeated measurements from each subject.
Some experiments involve matching but not repeated measurements. The term randomized-block describes these kinds of experiments. For example, imagine that the three rows were three different cell lines. All the Y1 data came from one experiment, and all the Y2 data came from another experiment performed a month later. The value at row 1, column A, Y1 (23) and the value at row 1, column B, Y1 (28) came from the same experiment (same cell passage, same reagents). The matching is by row.
Randomized block data are analyzed identically to repeated-measures data. Prism always uses the term repeated measures, so you should choose repeated measures analyses when your experiment follows a randomized block design.
My question: how can I analyse these data using GraphPad Prism? Or, alternatively: does the quoted description fit my case?