Instructions for Running a One-Way Repeated-Measures ANOVA Using SPSS

Example: Imagine that a researcher was interested in the effect of sleep deprivation on motor skills performance. Five participants were tested on a motor-skills task after 24 hours of sleep deprivation, tested again after 36 hours, and tested once more after 48 hours. The DV is the number of errors made on the motor skills task. The data for this example appear below. Note how these data are entered differently from the way data are entered for a one-way between-subjects ANOVA. In the repeated-measures case, each level of the IV is represented as a separate column.

Run a one-way repeated-measures ANOVA in SPSS by going under the “Analyze” menu to “General Linear Model” to “Repeated Measures…” You will get the following window:

As noted above, in your data window, there is not one single column in which your DV can be found, but rather, three columns (neutral, pleasant, aversive). Thus, you will need to tell SPSS what the name of your within-subjects variable (factor) is. Below, see how I have named this factor “hours” and noted that it had 3 levels:

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…and then “Define” to define my within-subjects variable:

Simply click on the name of the level of the IV in the left column, then click on the arrow, and it will bump into the right-hand box. Do this for all three levels of the IV (notice that I have done it only for the first one so far). Then click “OK”.

Next, click on “Options” (bottom-right hand corner of the screen) so you can ask SPSS for descriptive statistics (means and SDs for each condition):

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Next, I clicked on “Add”:

Click on the factor “hours” and then click the arrow to place it in the “Display Means for:” box. Then click on the box below labeled “Descriptive statistics,” as I have done. Then click “Continue.” You should now get bumped to the output window, where you can examine your findings (see other handout for how to interpret SPSS output for a repeated-measures ANOVA). SPSS prints a lot of output for the repeated-measures ANOVA, and you can ignore much of it. Note that you will be using the F value from the “Tests of Within-Subjects Effects” box (NOT the “Multivariate Tests” box). For the post-hoc tests of means, you can do one of two things: (1) calculate the Tukey HSD test by hand using the means given (SPSS does not have the option of a Tukey test for repeated-measures ANOVA), or (2) conduct a series of correlated groups t tests, one for each pairwise comparison you wish to make. You would then use a modified Bonferroni procedure (see pp. 395-6 of your textbook) to control Type I error rates.

In this example, you would conduct three correlated groups t tests (Analyze, Compare Means, Paired-Samples t test):

1. comparing 24 hrs to 36 hrs2. comparing 24 hrs to 48 hrs3. comparing 36 hrs to 48 hrs

When you get the output from these t tests, place the comparisons in order from the highest to lowest t value (in absolute value terms), and perform the modified Bonferroni procedure as follows:

Null hyp. tested Absolute value of t p value Critical alpha Null hyp. rejected?

m24 = m48

m36 = m48

m24 = m36

5.592.981.58

.005

.041

.189

.05/3 = .017

.05/2 = .025

.05/1 = .050

YesNoNo

If the observed p value is smaller than the critical alpha, then you can reject the null hypothesis. Thus, it appears that motor skills performance is significantly worse after 48 hours of sleep deprivation than after 24 hours of sleep deprivation, but that motor skills after 36 hours of sleep deprivation is not significantly different from motor skills after either 24 or 48 hours of sleep deprivation.

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