# Recognizing Components of an APA-Style Statistical Report

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The American Psychological Association (APA) has specific requirements about how statistical results are reported. These generally do not vary much—you usually have an indication of what test was used, the degrees of freedom associated with the test, the actual value of the test statistic, the p-value, and an appropriate measure of effect size. In this blog, I will walk you through recognizing components of an APA-style statistical report so that you will feel more confident reading, interpreting, and writing statistical reports.

Here is an example of a statistical result using an F-test:

F(2,34) = 2.51, p = .003, η2 = .04 ### Discover How We Assist to Edit Your Dissertation Chapters

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The first part indicates the test used—in this case, the F-test. Other common tests include the chi-square (2) and t-test. Letters of the English alphabet that represent a statistical value (such as t, F, and p) should be italicized; however, Greek letters representing statistical values (such as χ) are generally not italicized. The degrees of freedom associated with the test should be in parentheses following the statistical letter or symbol. Then, after an equals sign, the actual value of the test statistic is reported to two decimal places. Make sure that you have a space on either side of the equals sign. After a comma comes the p-value (notice the italics); p-values are reported in the “.000” form, so no leading zeroes and three places after the decimal. The eta squared (η2) is an effect size often reported for an ANOVA F-test. Measures of effect sizes such as R2 and d are common for regressions and t-tests respectively. Generally, the effect size is listed after the p-value, so if you do not immediately recognize it, it might be an unfamiliar effect size.

Here is an example of what results might look like for a t-test:

t(6)= 0.54, p = .547, d = .05

And here is an example for a chi-square test:

2(18) = 4.25, p = .458

These results should not be listed alone, but always explained. For example, you might say “Variables X and Z were strongly negatively correlated, r = -.60,” or “the two groups were significantly different, t(4) = -4.21, p = .041. Participants in the Group A scored significantly higher (M = 1.23, SD = 0.81) than Group B (M = 0.52, SD = 0.10).”