As a continuation of a previous topic, I would like to address a similar experience.
Subjects completed an experiment in which their electrodermal activity was recorded. For each subject there are 72 observations, making the dataset ~40000 obs. long.
- As a depended variable is the electrodermal response amplitude of a subject for each observation.
- As fixed effects are binary variables (and their interaction), such as whether a subject had faced childhood maltreatment during the early year and whether they have experienced recent adversities.
- The variable "Subject" gets to be my random effect within the lmer function.
In R that would be translated into:
lmer(formula = response.Amplitudes ~ childhood.Adversity *
recent.Adversity + (1|Subject), data = data)
Using anova and not the mixed a effected models, that would be:
aov(lm(response.Amplitudes ~ childhood.Adversity *
recent.Adversity, data = data)
The outcome of the first (mixed effects) formula gives a significant main effect of one of the fixed effects while an anova gives a high significance of both main effects and the interaction. Which is a huge difference
Obviously, the most logical explanation is that in my random effect, that of Subject, there is a huge variance.
Now, my question is: If this is the case, how can I prove that the huge difference between the models lies on the variance in the random effect? Is there a way to prove this, other than just looking at the outcome?
** Since I am new to the mixed models and that statistical approach, I recognize that the way of describing the problem lacks information or may be misleading. Please, let me know if I can provide any further information to make it more accurate.