ANOVA ANOVA Sum of Squares df Mean Square F p ω² Cerveza 15.4 2 7.69 5.50 0.008 0.106 Cara 21.4 1 21.40 15.30 < .001 0.168 Cerveza ✻ Cara 23.3 2 11.64 8.32 < .001 0.172 Residuals 57.3 41 1.40 [3] Assumption Checks Homogeneity of Variances (Levene's) F df1 df2 p 0.615 5 41 0.689 [3] Normality test (Shapiro-Wilk) statistic 0.986 Q-Q Plot p 0.834 General Linear Model Model Info Info Estimate Linear model fit by OLS Call Atractivo ~ 1 + Cerveza + Cara + Cerveza:Cara R-squared 0.516 Adj. R-squared 0.457 [4] Model Results ANOVA Omnibus tests SS df F p η²p Model 61.2 5 8.75 < .001 0.516 Cerveza 15.4 2 5.50 0.008 0.211 Cara 21.4 1 15.30 < .001 0.272 Cerveza ✻ Cara 23.3 2 8.32 < .001 0.289 57.3 41 118.6 46 Residuals Total Fixed Effects Parameter Estimates 95% Confidence Interval Names Effect Estimate SE Lower Upper β df t p (Intercept) (Intercept) 5.676 0.173 5.327 6.024 0.000 41 32.86 < .001 Cerveza1 Bajo alcohol - Placebo 0.723 0.426 -0.136 1.583 0.450 41 1.70 0.097 Cerveza2 Alto alcohol - Placebo 1.411 0.426 0.551 2.270 0.879 41 3.32 0.002 Cara1 Cara atractiva - Cara no atractiva 1.351 0.345 0.654 2.049 0.842 41 3.91 < .001 Cerveza1 ✻ Cara1 Bajo alcohol - Placebo ✻ Cara atractiva - Cara no atractiva -1.304 0.851 -3.022 0.415 -0.812 41 -1.53 0.133 Cerveza2 ✻ Cara1 Alto alcohol - Placebo ✻ Cara atractiva - Cara no atractiva -3.429 0.851 -5.147 -1.710 -2.136 41 -4.03 < .001 Simple Effects Simple effects of Cerveza : Omnibus Tests Moderator levels Cara F Cara no atractiva 14.033 Cara atractiva 0.225 Num df Den df p 2.00 41.0 < .001 2.00 41.0 0.800 Simple effects of Cerveza : Parameter estimates Moderator levels 95% Confidence Interval Cara contrast Estimate SE Cara no atractiva Bajo alcohol - Placebo 1.3750 0.591 0.181 Alto alcohol - Placebo 3.1250 0.591 1.931 Bajo alcohol - Placebo 0.0714 0.612 Alto alcohol - Placebo -0.3036 0.612 Cara atractiva ANOVA ANOVA Sum of Squares Flexplot Analysis Plot df Mean Square F p Lower Upper df t p 2.569 41.0 2.325 0.025 4.319 41.0 5.285 < .001 -1.165 1.307 41.0 0.117 0.908 -1.540 0.932 41.0 -0.496 0.623 References [1] The jamovi project (2020). jamovi. (Version 1.2) [Computer Software]. Retrieved from https://www.jamovi.org. [2] R Core Team (2019). R: A Language and envionment for statistical computing. (Version 3.6) [Computer software]. Retrieved from https://cran.r-project.org/. [3] Fox, J., & Weisberg, S. (2018). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.rproject.org/package=car. [4] Gallucci, M. (2019). GAMLj: General analyses for linear models. [jamovi module]. Retrieved from https://gamlj.github.io/.