Decision-Based Learning - Reporting a non parametric Friedman test in APA

1. Reporting a non-parametric Friedman
Test in APA

2. • Note – that the reporting format shown in this
learning module is for APA. For other formats
consult specific format guides. It is also
recommended to consult the latest APA manual to
compare what is described in this learning module
with the most updated formats for APA

4. • In the data set below, we are comparing the number
pizza slices eaten in one sitting by football players
before, during, and after their season.

5. • In the data set below, we are comparing the number
pizza slices eaten in one sitting by football players
before, during, and after their season.
Before During After
12 45 1
13 7 4
12 8 5
11 7 4
12 8 3
13 9 2
14 7 4
12 6 5
15 5 4
11 6 3

6. • In the data set below, we are comparing the number
pizza slices eaten in one sitting by football players
before, during, and after their season.
Before During After
12 45 1
13 7 4
12 8 5
11 7 4
12 8 3
13 9 2
14 7 4
12 6 5
15 5 4
11 6 3
• Here is the output for a Friedman Test

7. • In the data set below, we are comparing the number
pizza slices eaten in one sitting by football players
before, during, and after their season.
Before During After
12 45 1
13 7 4
12 8 5
11 7 4
12 8 3
13 9 2
14 7 4
12 6 5
15 5 4
11 6 3
• Here is the output for a Friedman Test
Ranks
Mean Rank
Before the Season 2.90
During the Season 2.10
After the Season 1.00

8. • In the data set below, we are comparing the number
pizza slices eaten in one sitting by football players
before, during, and after their season.
Before During After
12 45 1
13 7 4
12 8 5
11 7 4
12 8 3
13 9 2
14 7 4
12 6 5
15 5 4
11 6 3
• Here is the output for a Friedman Test
Ranks
Mean Rank
Before the Season 2.90
During the Season 2.10
After the Season 1.00
Test Statistics
N 10
Chi-Square 18.200
df 2
Asymp. Sig 0.000

9. • Here is the template for reporting a Friedman Test in
APA

10. • Here is the template for reporting a Friedman Test in
APA
• “ A non-parametric Friedman test of differences
among repeated measures was conducted and
rendered a Chi-square value of X.XX which was
significant (p<.01).”

12. • Here is how the report would read with our “Pizza-
Eating” example:

13. • Here is how the report would read with our “Pizza-
Eating” example:
• “ A non-parametric Friedman test of differences
among repeated measures was conducted and
rendered a Chi-square value of 18.20 which was
significant (p<.01).”

14. • Here is how the report would read with our “Pizza-
Eating” example:
• “ A non-parametric Friedman test of differences
among repeated measures was conducted and
rendered a Chi-square value of 18.20 which was
significant (p<.01).”
Ranks
Mean Rank
Before the Season 2.90
During the Season 2.10
After the Season 1.00
Test Statistics
N 10
Chi-Square 18.200
df 2
Asymp. Sig 0.000

15. • Here is how the report would read with our “Pizza-
Eating” example:
• “ A non-parametric Friedman test of differences
among repeated measures was conducted and
rendered a Chi-square value of 18.20 which was
significant (p<.01).”
Ranks
Mean Rank
Before the Season 2.90
During the Season 2.10
After the Season 1.00
Test Statistics
N 10
Chi-Square 18.200
df 2
Asymp. Sig 0.000

16. • Here is how the report would read with our “Pizza-
Eating” example:
• “ A non-parametric Friedman test of differences
among repeated measures was conducted and
rendered a Chi-square value of 18.20 which was
significant (p<.01).”
Ranks
Mean Rank
Before the Season 2.90
During the Season 2.10
After the Season 1.00
Test Statistics
N 10
Chi-Square 18.200
df 2
Asymp. Sig 0.000