Repeated measures ANOVA within and between factors I have conducted repeated measures ANOVA, with 2 factors: One within factor (time, pre-post intervention) and one between factor (treatment, two groups). I have measured different dependent variables. I find in all the cases an effect of the within factor, an effect of the between factor, and only in some cases an interaction between both. I want to know how to interpret these results. 


*

*What does the interaction mean?  

*Is the significance of the factor treatment  (between) sufficient to be able to say that one group is superior to other one, that one treatment is better? 


EDIT: The measures I took in three moments are: time spent using technologies, frecuency of use, abuse.. The significant interaction is for within factor (moment) X between factor (group), but it's only significant for the time spent using technologies. 
What means the sugnificance of between factor? Why it's important the intetaction?
Thanks

The measures I took in three moments are: time spent using technologies, frecuency of use, abuse.. The significant interaction is for within factor (moment) X between factor (group), but it's only significant for the time spent using technologies. 
What means the sugnificance of between factor? Why it's important the intetaction?
Thanks
 A: I assume time of use refers to an addiction (such as use of a drug).  Also when you say dependent variables i think youprobably mean the predictors (usually called independent variables).  Time of use interacting with treatment group could mean that the effect of the intervention can be stronger/weaker depending on how recently the subject was using the drug for example.
A: What does the interaction mean: It means the effect of treatment differed as a function of time. This is almost certainly the outcome you desire. 
Is the significance of the factor treatment (between) sufficient to be to say that one group is superior to other one, that one treatment is better: Almost certainly not. As mentioned in the comments this might suggest that your groups were actually different at pre - intervention. 
A: The between-within group interaction can be interpreted as such: does the difference between moments (within) depend on the treatment group that they are in (between)? 
The math behind this is very simple but important. Let's say you only had moments 1 and 2. We would find the interaction by subtracting moment 2 from moment 1, and use that value as a dependent variable in the model. So your dependent variable is now the difference between moment 2 and moment 1, and your independent variable is still the treatment group. Now if you run this model and interpret it normally, the interpretation will be very similar to above; just interpreted in terms of how the effect of the within-subjects variable is dependent on the between-subjects variable.
So if you have a significant interaction for time spent using technologies when comparing moments 1 and 2, that means that the difference between these moments for time spent using technologies depends on the between-subjects variable (treatment) that they receive. 
To answer your second question, your between-subjects results and interaction results are complementary but tell you rather different things. These are not 2 between-interactions like interpreted elsewhere on this page. The between-subjects test can tell you if treatment 1 is more effective than treatment 2. So, on average across moments, this would tell you if treatment x is more effective than treatment y in a statistically-significant manner. The interaction would then tell you if the difference between these moments depends on the treatment they receive. 
The interaction is not necessarily important in determining whether the treatment is effective or not, but if you're interested how the treatment affects how participants' time spent using technologies changes between moments, it is ideal.
PS: I know that this question is extremely old, but this is the first and most relevant result from search engines and I haven't seen a fully-correct answer yet.
