### Within-Subjects Factors Table

The **Within-Subjects Factors** table reminds us of the groups of our independent variable (called a "within-subject factor" in SPSS Statistics) and labels the time points 1, 2 and 3. We will need these labels later on when analysing our results in the **Pairwise Comparisons** table. Take care not to get confused with the "**Dependent Variable**" column in this table because it seems to suggest that the different time points are our dependent variable. This is not true – the column label is referring to fact that the dependent variable "CRP" is measured at each of these time points.

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### Descriptive Statistics Table

The **Descriptive Statistics** table simply provides important descriptive statistics for this analysis, as shown below:

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### Tests of Within-Subjects Effects Table

The **Tests of Within-Subjects Effects** table tells us if there was an overall significant difference between the means at the different time points.

Published with written permission from SPSS Statistics, IBM Corporation.

From this table we are able to discover the *F* value for the "**time**" factor, its associated significance level and effect size ("**Partial Eta Squared**"). As our data violated the assumption of sphericity, we look at the values in the "**Greenhouse-Geisser**" row (as indicated in red in the screenshot). We can report that when using an ANOVA with repeated measures with a Greenhouse-Geisser correction, the mean scores for CRP concentration were statistically significantly different (*F*(1.171, 22.257) = 21.032, *p* < 0.0005).

### Pairwise Comparisons Table

The results presented in the previous table informed us that we have an overall significant difference in means, but we do not know where those differences occurred. This table presents the results of the Bonferroni post hoc test, which allows us to discover which specific means differed. Remember, if your overall ANOVA result was not significant, you should not examine the **Pairwise Comparisons** table.

Published with written permission from SPSS Statistics, IBM Corporation.

Looking at the table above, we need to remember the labels associated with the time points in our experiment from the **Within-Subject Factors** table. This table gives us the significance level for differences between the individual time points. We can see that there was a significant difference in CRP concentration between post-training and pre-training (*p* = 0.0005), and between post-training and after 2 weeks of training (*p* = 0.001), but no significant differences between pre-training and after 2 weeks of training (*p* = 0.149). From the "**Mean Difference (I-J)**" column we can see that CRP concentration was significantly reduced at this time point.

It is also possible to run comparisons between specific time points that you decided were of interest before you looked at your results. For example, you might have expressed an interest in knowing the difference in CRP concentration between the pre- and post-intervention time points. This type of comparison is often called a planned contrast or a planned simple contrast. However, you do not have to confine yourself to the comparison between two time points only. You might have had an interest in understanding the difference in CRP between the pre-intervention time point and the average of the mid- and post-intervention time points. This is called a complex contrast. All these types of planned contrast are available in SPSS Statistics. In our enhanced guide we show you how to run contrasts in SPSS Statistics using syntax and how to interpret and report the results. In addition, we also show how to "trick" SPSS Statistics into applying a Bonferroni adjustment for multiple comparisons which it would otherwise not do.

### Profile Plot

This plot is the last element to this analysis. We are only including it so that you can see some of the limitations of doing so in its current format. SPSS Statistics has means of altering graphs' axes. This is important because these profile plots always tend to exaggerate the differences between means by choosing a y-axis range of values that is too narrow. In this case, it is known that most people have CRP concentrations ranging from 0 to 3, so the profile plot that you should produce should take this into consideration. However, this plot can be useful in gaining an easy understanding of the tabular results.

Published with written permission from SPSS Statistics, IBM Corporation.

###### SPSS Statistics

## Reporting the Output

A repeated measures ANOVA with a Greenhouse-Geisser correction determined that mean CRP concentration differed statistically significantly between time points (*F*(1.171, 22.257) = 21.032, *P* < 0.0005). Post hoc tests using the Bonferroni correction revealed that exercise training elicited a slight reduction in CRP concentration from pre-training to 2-weeks of training (3.09 ± 0.98 mg/L vs 2.97 ± 0.89 mg/L, respectively), which was not statistically significant (*p* = .149). However, post-training CRP had been reduced to 2.24 ± 0.50 mg/L, which was statistically significantly different to pre-training (*p* < .0005) and 2-weeks training (*p* = .001) concentrations. Therefore, we can conclude that a long-term exercise training program (6 months) elicits a statistically significant reduction in CRP concentration, but not after only 2 weeks of training.

In our enhanced repeated measures ANOVA guide, we show you how to write up the results from your assumptions tests, repeated measures ANOVA and post hoc results if you need to report this in a dissertation/thesis, assignment or research report. We do this using the Harvard and APA styles. You can learn more about our enhanced content on our Features: **Overview** page.