Reporting a one way repeated measures anova

1. Reporting a One-Way Repeated Measures ANOVA

2. Reporting the Study using APA • 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

5. Reporting the Study using APA • You can report that you conducted a One-Way Repeated Measures ANOVA by using the template below.

6. Reporting the Study using APA • You can report that you conducted a One-Way Repeated Measures ANOVA by using the template below. • “A one-way repeated measures ANOVA was conducted to compare the effect of (IV)______________ on (DV)_______________ in _________________, __________________, and __________________ conditions.”

7. Reporting the Study using APA • You can report that you conducted a One-Way Repeated Measures ANOVA by using the template below. • “A one-way repeated measures ANOVA was conducted to compare the effect of (IV)______________ on (DV)_______________ in _________________, __________________, and __________________ conditions.” • “A one-way repeated measures ANOVA was conducted to compare the effect of (IV) time of eating on (DV) pizza slices consumed, before, during and after the season.”

8. Reporting Results using APA

9. Reporting Results using APA • Just fill in the blanks by using the SPSS output

10. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant (not a significant) effect of the IV ___________, Wilks’ Lambda = ____, F (____,____) = _____, p = _____.

11. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant (not a significant) effect of the IV ___________, Wilks’ Lambda = ____, F (____,____) = _____, p = _____. Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

12. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (____,____) = _____, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

13. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (____,____) = _____, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

14. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2,____) = _____, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

15. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2,____) = _____, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

16. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2, 6) = _____, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

17. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2, 6) = _____, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

18. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2, 6) = 128, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

19. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2, 6) = 128, p = _____.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

20. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2, 6) = 128, p = .000.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic

21. Reporting Results using APA • Just fill in the blanks by using the SPSS output • “There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2, 6) = 128, p = .000.” Multivariate Testsa Effect Value F Hypothesis df Error df Sig. Time_eating Pillai's Trace .977 128.030b 2.000 6.000 .000 Wilks' Lambda .023 128.030b 2.000 6.000 .000 Hotelling's Trace 42.677 128.030b 2.000 6.000 .000 Roy's Largest Root 42.677 128.030b 2.000 6.000 .000 a. Design: Intercept Within Subjects Design: Time_eating b. Exact statistic • Once the blanks are full…you have your report:

22. Reporting Results using APA There was a significant effect of time of season on eating pizza, Wilks’ Lambda = .023, F (2, 6) = 128, p = .000.

23. Reporting Results using APA • Note- if there is a significant difference (which there was in this case) you would also report the pair-wise t results which look like this:

24. Reporting Results using APA • Note- if there is a significant difference (which there was in this case) you would also report the pair-wise t results which look like this: • Three paired samples t-tests were used to make post hoc comparisons between conditions.

25. Reporting Results using APA • Note- if there is a significant difference (which there was in this case) you would also report the pair-wise t results which look like this: • Three paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and during (M= 6.3, SD=.71) the season; t(7)= 6.62, p = .000. A third paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and after (M =1.4, SD=.518) the season; t(7)= 6.18, p = .000.

26. Reporting Results using APA • Note- if there is a significant difference (which there was in this case) you would also report the pair-wise t results which look like this: • Three paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and during (M= 6.3, SD=.71) the season; t(7)= 6.62, p = .000. A second paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten during (M= 6.3, SD=.71) and after (M =1.4, SD=.52) the season; t(7)= 13.91, p = .000. A third paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and after (M =1.4, SD=.518) the season; t(7)= 6.18, p = .000.

27. Reporting Results using APA • Note- if there is a significant difference (which there was in this case) you would also report the pair-wise t results which look like this: • Three paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and during (M= 6.3, SD=.71) the season; t(7)= 6.62, p = .000. A second paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten during (M= 6.3, SD=.71) and after (M =1.4, SD=.52) the season; t(7)= 13.91, p = .000. A third paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and after (M =1.4, SD=.518) the season; t(7)= 6.18, p = .000.

28. Reporting Results using APA • Three paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and during (M= 6.3, SD=.71) the season; t(7)= 6.62, p = .000. A second paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten during (M= 6.3, SD=.71) and after (M =1.4, SD=.52) the season; t(7)= 13.91, p = .000. A third paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and after (M =1.4, SD=.518) the season; t(7)= 6.18, p = .000. Descriptive Statistics Mean Std. Deviation N Before 3.00 .756 8 During 6.25 .707 8 After 1.38 .518 8

35. Reporting Results using APA • Three paired samples t-tests were used to make post hoc comparisons between conditions. A first paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and during (M= 6.3, SD=.71) the season; t(7)= -6.62, p = .000. A second paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten during (M= 6.3, SD=.71) and after (M =1.4, SD=.52) the season; t(7)= 13.91, p = .000. A third paired samples t-test indicated that there was a significant difference between the number of pizza slices eaten before (M=3.0, SD=.76) and after (M =1.4, SD=.518) the season; t(7)= 6.18, p = .000. Paired Samples Test Paired Differences Std. Error Mean 95% Confidence Interval of the Difference Mean Std. Deviation Lower Upper t df Sig. (2-tailed) Pair 1 Before - During -3.250 1.389 .491 -4.411 -2.089 -6.619 7 .000 Pair 2 During - After 4.875 .991 .350 4.046 5.704 13.913 7 .000 Pair 3 Before - After 1.625 .744 .263 1.003 2.247 6.177 7 .000

Reporting a one way repeated measures anova

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Reporting a one way repeated measures anova