An ANOVA with repeated measures is used to compare three or more group means where the participants are the same in each group. This usually occurs in two situations: (1) when participants are measured multiple times to see changes to an intervention; or (2) when participants are subjected to more than one condition/trial and the response to each of these conditions wants to be compared. For example, you could use a repeated measures ANOVA to understand whether there is a different in cigarette consumption amongst heavy smokers after a hypnotherapy programme (e.g., with 3 time points: cigarette consumption immediately before, 1 month after, and 6 months after the hypnotherapy programme). In this example, "cigarette consumption" is your dependent variable, whilst your independent variable is "time" (i.e., with 3 related groups, where each of the three time points is considered a "related group"). Alternately, you could use a repeated measures ANOVA to understand whether there was a difference in braking speed in a car based on three different coloured tints of windscreen (e.g., braking speed under 4 conditions: no tint, low tint, medium tint and dark tint). In this example, "braking speed" is your dependent variable, whilst your independent variable is "condition" (i.e., with 4 related groups, where each of the four conditions is considered a "related group"). Whilst the repeated measures ANOVA is used when you have just "one" independent variable, if you have "two" independent variables (e.g., you measured time and condition), you will need to use a two-way repeated measures ANOVA.
This "quick start" guide shows you how to carry out a repeated measures ANOVA using SPSS, as well as interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a repeated measures ANOVA to give you a valid result. We discuss these assumptions next.
When you choose to analyse your data using a repeated measures ANOVA, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a repeated measures ANOVA. You need to do this because it is only appropriate to use a repeated measures ANOVA if your data "passes" five assumptions that are required for a repeated measures ANOVA to give you a valid result. In practice, checking for these five assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task.
Before we introduce you to these five assumptions, do not be surprised if, when analysing your own data using SPSS, one or more of these assumptions is violated (i.e., is not met). This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out a repeated measures ANOVA when everything goes well! However, don’t worry. Even when your data fails certain assumptions, there is often a solution to overcome this. First, let’s take a look at these six assumptions:
You can check assumptions #3, #4 and #5 using SPSS. Before doing this, you should make sure that your data meets assumptions #1 and #2, although you don't need SPSS to do this. When moving on to assumptions #3, #4 and #5, we suggest testing them in this order because it represents an order where, if a violation to the assumption is not correctable, you will no longer be able to use a repeated measures ANOVA (although you may be able to run another statistical test on your data instead). Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running a repeated measures ANOVA might not be valid. This is why we dedicate a number of sections of our enhanced repeated measures ANOVA guide to help you get this right. You can find out about our enhanced content as a whole here, or more specifically, learn how we help with testing assumptions here.
In the section, Procedure, we illustrate the SPSS procedure to perform a repeated measures ANOVA assuming that no assumptions have been violated. First, we set out the example we use to explain the repeated measures ANOVA procedure in SPSS.
Heart disease is one of the largest causes of premature death and it is now known that chronic, low-level inflammation is a cause of heart disease. Exercise is known to have many benefits, including protection against heart disease. A researcher wants to know whether this protection against heart disease might be afforded by exercise reducing inflammation. The researcher was also curious as to whether this protection might be gained over a short period of time or whether it took longer. In order to investigate this idea, the researcher recruited 20 participants who underwent a 6-month exercise training program. In order to determine whether inflammation had been reduced, the researcher measured the inflammatory marker called CRP pre-training, 2 weeks into training and post-6-months-training
In our enhanced repeated measures ANOVA guide, we show you how to correctly enter data in SPSS to run a repeated measures ANOVA. You can learn about our enhanced data setup content here. Alternately, we have a generic, "quick start" guide to show you how to enter data into SPSS, available here.
The 13 steps below show you how to analyse your data using a repeated measures ANOVA in SPSS when the five assumptions in the previous section, Assumptions, have not been violated. At the end of these 13 steps, we show you how to interpret the results from this test. If you are looking for help to make sure your data meets assumptions #3, #4 and #5, which are required when using a repeated measures ANOVA, and can be tested using SPSS, you can learn more in our enhanced guides here.
Click Analyze > General Linear Model > Repeated Measures... on the top menu as shown below:
Published with written permission from SPSS Inc., an IBM Company.
You will be presented with the following screen:
Published with written permission from SPSS Inc., an IBM Company.
In the Within-Subject Factor Name: box, replace "factor1" with a name that is more meaningful name for your independent variable. In our example, we will call our within-subject factor name "time", as it represents the different times that we took CRP measurements from our participants (pre-training, pre + 2 weeks and post-training).
Enter into the Number of Levels: box the number of times the dependent variable has been measured. In this case, enter "3", representing pre-training, pre + 2 weeks and post-training.
Click the button.
Put an appropriate name for your dependent variable in the Measure Name: box. In this case, we have labelled our dependent variable CRP.
Click the button.
You will be presented with the diagram screen below:
Published with written permission from SPSS Inc., an IBM Company.
Click the button and you will be presented with the following screen:
Published with written permission from SPSS Inc., an IBM Company.
Transfer "Pre_Training", "Week2" and "Post_Training" into the Within-Subjects Variables (time): box by either drag-and-dropping or using the button. If you make a mistake you can use the and buttons to reorder your variables.
Published with written permission from SPSS Inc., an IBM Company.
Click the button. You will be presented with the following screen:
Published with written permission from SPSS Inc., an IBM Company.
Transfer the "time" factor from the Factors: box into the Horizontal Axis: box by either drag-and-drop or the button.
Click the button. You will be presented with the following screen:
Published with written permission from SPSS Inc., an IBM Company.
You will be presented with the following screen:
Published with written permission from SPSS Inc., an IBM Company.
Go to the next page for the SPSS output and an explanation of the output.