What is repeated measures?

You should choose a repeated-measures analysis when the experiment used paired or matched subjects. Prism can calculate repeated-measures two-way ANOVA with matching by either row or column, but not both. This is sometimes called a mixed model.

The table above shows example data testing the effects of three doses of drugs in control and treated animals. The decision to use repeated measures depends on the experimental design.

Repeated measures by row

Here is an experimental design that would require analysis using repeated measures by row:

The experiment was done with six animals, two for each dose. The control values were measured first in all six animals. Then you applied a treatment to all the animals and made the measurement again. In the table above, the value at row 1, column A, Y1 (23) came from the same animal as the value at row 1, column B, Y1 (28). The matching is by row.

Repeated measures by column

Here is an experimental design that would require analysis using repeated measures by column:

The experiment was done with four animals. First each animal was exposed to a treatment (or placebo). After measuring the baseline data (dose=zero), you inject the first dose and make the measurement again. Then inject the second dose and measure again. The values in the first Y1 column (23, 34, and 43) were repeated measurements from the same animal. The other three subcolumns came from three other animals. The matching was by column.

Repeated measures in both factors

Here is an experimental design where both factors are repeated measures:

The experiment was done with two animals. First you measured the baseline (control, zero dose). Then you injected dose 1 and made the next measurement, then dose 2 and measured again. Then you gave the animal the experimental treatment, waited an appropriate period of time, and made the three measurements again. Finally, you repeated the experiment with another animal (Y2). So a single animal provided data from both Y1 subcolumns (23, 34, 43 and 28, 41, 56).

Prism cannot perform two-way ANOVA with repeated measures in both directions, and so cannot analyze the data from this experiment.

"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.

Example without repeated measures

Finally, here is an example of an experiment done with replicates but no repeated measures:

The experiment was done with six animals. Each animal was given one of two treatments at one of three doses. The measurement was then made in duplicate. The value at row 1, column A, Y1 (23) came from the same animal as the value at row 1, column A, Y2 (24). Since the matching is within a treatment group, it is a replicate, not a repeated measure. Analyze these data with ordinary two-way ANOVA, not repeated-measures ANOVA.