1. Review of Results From data analysis to presentation

2. APA manual (2001): “The results section summarizes the data collected and the statistical treatment of them. First, briefly state the main results of findings. Discussing the implications of the results is not appropriate here. Mention all relevant results, including those that run counter to the hypothesis. Do not include individual raw scores or raw data…” (p. 20).

3. t-test (two-groups)

4. Results (two groups) A t-test for independent groups (female versus male) was conducted for the dependent measure escape latency. The results revealed a significant effect [t (38) = 2.24, p <.05]. As shown in Figure 1, Female subjects escaped significantly faster than male subjects. _____________________ Insert Figure 1 about here _____________________

5. Results (two groups)

6. A t-test for independent groups (female versus male) was conducted for the dependent measure escape latency. The results revealed a significant effect [t (38) = 2.24, p <.05]. As shown in Figure 1, Female subjects escaped significantly faster than male subjects. Figure 1. Absolute heading error (+/- SEM) on the probe test following latent learning test trials in room 500B. The female group had a significantly more accurate heading to the goal location than the male group.

7. One-way ANOVA (multiple groups)

8. Tukey post-hoc test

9. Results: Omnibus F

10. Results: Tukey Post-hoc

11. Results (multiple groups) A one-way analysis of variance (ANOVA) was conducted using the 4 levels of the independent variable (control, 1 mg/kg, 4 mg/kg and 8 mg/kg) and the dependent variable escape latency. The results revealed a significant effect of the drug [F (3,36) = 6.96, p <.005]. As shown in Figure 1, The control group escaped faster than the 3 drug groups. Tukey post-hoc analyses revealed that the Control group differed significantly from all other other groups (ps <.05). _____________________ Insert Figure 1 about here _____________________

12. Factorial design More than one IV (called factors) More than one IV (called factors) Advantages: Advantages: One experiment instead of two One experiment instead of two Fewer subjects may be needed Fewer subjects may be needed Can study interactions in addition to main effects Can study interactions in addition to main effects

13. Simple Factorial Design Two factors Two factors Two treatments (a.k.a., conditions or levels) for each factor Two treatments (a.k.a., conditions or levels) for each factor Example (water maze): Sex x Cue condition (2 x 2) Hypothesis: Females will perform better than males when navigating in the presence of distal cues but not when navigating in the presence of proximal cues.

14. Simple Factorial Design FemaleDistalFemaleProximal MaleDistalMaleProximal Sex F M Cue condition DistalProximal

15. Simple Factorial Design How many results? Two main effects (Sex and cue condition) Two main effects (Sex and cue condition) Did males perform better than females? Did males perform better than females? Is distal better than proximal? Is distal better than proximal? One interaction (Sex x cue condition) One interaction (Sex x cue condition) Does the effect of sex differ at different levels of the cue condition? Does the effect of sex differ at different levels of the cue condition?

16. Columns from sham data set Fem/Dis(A)Fem/Prox(B) M/Dis(C)M/Prox(D) Sex F M Cue condition DistalProximal

17. This is not the data set you have

18. Cell and Marginal Means 16.825.3 25.724.8 Sex F M Cue Condition DistalProximal 21.05 25.25 21.2525.05 Main effect of cue Main effect of sex Marginal Means

19. Coding IVs in SPSS Fem/Dis(1,1)Fem/Prox(1,2) M/Dis(2,1)M/Prox(2,2) Sex F (1) M (2) Cue condition Distal (1) Proximal (2)

20. SPSS spreadsheet

21. Analyze: GLM  Univariate

22. Variables

23. Results

24. Results

25. Cell and Marginal Means 16.825.3 25.724.8 Sex F M Cue Condition DistalProximal 21.05 25.25 21.2525.05 Main effect of cue Main effect of sex Marginal Means

26. Results Main effect of Sex, F (1,36) = 6.65, p <.05 Main effect of Sex, F (1,36) = 6.65, p <.05 Main effect of Cue, F (1,36) = 5.70, p <.05 Main effect of Cue, F (1,36) = 5.70, p <.05 Sex x Cue Interaction, F (1,36) = 8.53, p <.01 Sex x Cue Interaction, F (1,36) = 8.53, p <.01

27. Main Effects Marginal Means Main effect of Sex Marginal Means Main effect of Cue

28. Sex x Cue interaction

29. Simple Factorial Design Results: Main effects Sex – not sig Sex – not sig Cue – sig Cue – sigInteraction Sex x Cue - sig Sex x Cue - sig Hypothesis: Females will perform better than males when navigating in the presence of distal cues but not when navigating in the presence of proximal cues.

30. Results A 2 x 2 (Sex x Cue) factorial analysis of variance (ANOVA) was conducted for the dependent measure, escape latency. The results showed a significant main effect of Sex [F (1,36) = 6.65, p <.05], a significant main effect of Cue [F (1,36) = 5.70, p <.05] and a significant Sex x Cue interaction [F (1,36) = 8.53, p <.01]. As shown in Figure 1, Female subjects escaped significantly faster than male subjects in the distal cue condition. However, male and female subjects did not differ significantly in the proximal cue condition. _____________________ Insert Figure 1 about here _____________________

31. APA manual (2001): “After presenting the results, you are in a position to evaluate and interpret their implications, especially with respect to your original hypotheses. You are free to examine, interpret, and qualify the results as well as to draw inferences from them.” (p. 26)

32. APA manual (2001): “Simmilarities and differences between your results and the work of others should clarify and confirm your conclusions. Do not, however, simply reformulate and repeat points already made; each new statement should contribute to your position and to the reader’s understanding of the problem. You may remark on certain shortcomings of the study, but do not dwell on every flaw. Negative results should be accepted as such without an undue attempt to explain them away.” (p. 26)