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Sum of S . 1.000 1000 2100 14.236 AM 1 . 4728 Gender Marginal Mean Lover Cl Upper Cl Female 4.553 4.201 4.824 Male 5.000 4.826 5.305 Marginal Means-Condition Condition Marginal Mean Lower Cl Upper Cl Abstract Art 4.781 0.107 4.540 5.022 Nudes 4.837 0.120 4.567 5.107 one-way ANOVAs comparing the experimental conditions for males and females separately. Fa & Condition Gender Simple Man Efects-Con Level of Gender Sum of Squares Fende Male 11.067 20555 Revituar 116 THE JASP DATA LIBRARY Marginal Means-Gender SE 0.121 0.105 · T 1300 Mean Sour 11.067 20.555 4.121 F 0.491 14.707 400 D 0.004 <.001 Figure 135: JASP output for the Erotic Pictures & Love data (Balzarini et al. 2017): ANOVA. Figure 136: JASP output for the Erotic Pictures & Love data (Balzarini et al. 2017): Marginal means Figure 137: Screenshot of the JASP input panel for the anal- ysis of Erotic Pictures & Love data set (Balzarini et al 2017) using ANOVA: Simple main effects. Figure 138: JASP output for the Erotic Pictures & Love data (Balzarini et al. 2017): Simple main effects.
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2:18 5G Lab 5 - Erotic Pictures and Love.docx The dataset is located on Canvas. Please answer the following questions: 1. Based on the above vignette which analysis are you going to run? Why did you select this analysis? (Hint: Think about the two assumptions) 2. Based on the prediction of the researcher, is this a one- or two-tailed test? 3. Prior to conducting a hypothesis test, set up the hypotheses (i.e., H. and H₂) 4. Select a criterion (i.e., a = .05) 5. Evaluate the first three assumptions (i.e., study design and the measurement). If there are any violations, then make suggestions of what to do to remedy the violations. If not, then move on. 6. Evaluate the final three assumptions (i.e., characteristics of the data). If there are any violations, then make suggestions of what to do to remedy the violations. If not, then move on. 7. Make the appropriate calculations using JASP. 8. Evaluate the output and decide if there are main effects or interactions. 9. Write a conclusion based on your findings. 10. Create graph with error bars. Background The two-way ANOVA is used to determine whether there is an interaction effect between two independent variables (IVS) on a continuous dependent variable (DV; i.e., if a two-way interaction effect exists). It is quite common for the IVS to be called "factors" or M Calendar To Do Notifications Inbox Dashboard