6 Comparing Means: Repeated Measures One-Way ANOVA

Jenna Lehmann

This post will be about finding a difference in means when it comes to repeated measures in research designs with a factor with more than 2 levels. Just like with the Repeated Measures t-test, we’ll be lining our levels up in columns. For this example, we’ll pretend that we’ve collected data on self-reported depression. Participants were asked to rate on a scale from 1-9 how severe they felt their depression is. They were then given medication to take which is known to reduce depressive symptoms. Participants were asked again after 6 months how high they rated their depression. They were asked one last time at the end of 12 months.

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I went ahead and named the levels in the Variable view.

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To run the actual test, simply go up to Analyze, scroll over General Linear Model, and click Repeated Measures.

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A pop-up will appear. In the first box, create the name of your factor. In this case, I’ve named it time, because we’re doing comparisons across time. In the second box, I typed in 3 because we have 3 levels and then I pressed Add. In the third box, I named our dependent variable and clicked Add. Next, we need to Define our factors.

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Another pop up will appear. Move the levels over into the top, right box. I prefer doing this in chronological order from top to bottom. Then, click Options.

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I would recommend getting means for everything, so move OVERALL and time over to the box on the right. I also recommend clicking the Descriptive Statistics and Estimate of Effect Size boxes. Finally, click the Compare Means checkbox; it’s located under the big, white box on the right. Click all the Continues and OK’s that follow.

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We can see from the means that the average for Start is greater than at 6 months is greater than at 12 months. This is important to know, but this does not prove significance.

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Go to the Tests of Within-Subjects Effects box and find “time” on the left, scroll over to Greenhouse-Geisser, and then scroll all the way to F and significance. We have a huge F score of 68.5 and a significance which is less than 0.05 and so we can say that somewhere there is a significant difference in the groups. If you need to report the effect size, you can find it under Partial Eta Squared.

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Screenshot of SPSS document

This last box shows us the post-hoc under Pairwise Comparison. As you can see, all the comparisons are significantly different with a significance less than 0.05. This means that we can say that there was a significant difference in times since treatment began with participants expressing the most depression before the treatment started, less depression 6 months after the treatment started, and the least depression after 12 months of treatment.

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This chapter was originally posted to the Math Support Center blog at the University of Baltimore on July 16, 2019.

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