model1 <- lme(FOD_FODs ~ 0 + Condition_FODs*language_used_FODs_ind, random=~1|subject_FODs, na.action=na.omit, method = "ML", data=indvar_FODs,control = lmeControl(opt = "optim")) > p_value <- shapiro.test(resid(model1))$p.value > round(p_value, 3) [1] 0 > check_singularity(model1) [1] FALSE > ano <- anova(model1) > round(ano, 3) numDF denDF F-value p-value Condition_FODs 4 3262 0.402 0.807 language_used_FODs_ind 4 40 0.008 1.000 Condition_FODs:language_used_FODs_ind 12 3262 0.053 1.000 > library(predictmeans) > eta_squared(anova(model1), partial=TRUE) # Effect Size for ANOVA Parameter | Eta2 (partial) | 95% CI --------------------------------------------------------------------- Condition_FODs | 4.93e-04 | [0.00, 1.00] language_used_FODs_ind | 8.36e-04 | [0.00, 1.00] Condition_FODs:language_used_FODs_ind | 1.95e-04 | [0.00, 1.00] please report these results in APA style

The p-values of the ANOVA tests show that the effect of the main factors Condition_FODs and language_used_FODs_ind are not signifficant. The p-value of the interaction term is also not significant. This means that the model does not fit the data well. This is confirmed by the low partial eta squared values of the main factors and the interaction.