My dependent variable is Accuracy (that takes 1 or 0 for right and wrong). My IVs are Emotion (Sad, Happy; coded as 1,2 respectively), Load (high or low coded as 1,2 respectively) and Group (Group A and Group B; coded as 1,2 respectively).
I'm running a logistic regression in R using the following formula:
model1 <- glm(Accuracy ~ Emotion + Group + Load, data = file_name, family = Binomial(link = "logit")
summary(model1) gives me a result that I can interpret. However, I notice that the Emotion and Group variables are followed by a number:
I'm new to logistic regression, but I'm not sure why its giving a specific result for an emotion. Why does it say
emotion2 instead of
emotion? Usually, in ANOVA, I get a simple effect of IVs and then I look inside to know the sub-factors affecting the DV. Why is this different?
Moreover, I'm interested in knowing the interaction effect. So, I executed another command:
model11 <- glm(Accuracy ~ Emotion * Task * Group, data = file_name, family = binomial(link = "logit"))
summary(model11) gives me something completely different. I had expected that the main effect will be the same for the two models (
model11), but this is not the case.
Can anyone explain why this is the case?