Dear Elena,
As is often the case in statistics (which makes it frustrating), there isn't a "right answer" to this question. Either method you describe seem like reasonable options. However, I would tend to lean towards using a Multi-level model, as that allows you to examine the effect of your covariate on both the intercept and slope separately.
However, to answer your first question, you absolutely can use a continuous predictor in a Repeated Measures MANCOVA and it would indeed get put into the covariates box in SPSS. I hope this helps!
Thanks for your help, Jeremy!.
i was afraid of this response :))) (multi-level modeling is not easy to understand really).
in any case if i would decide to go with the continuous predictor in RM MANCOVA, what do i do when i get significant interactions? how do i really explore that?
For example, i get a task x anxiety interaction (which is what i would expect anyway). how do i proceed from there?
what i thought to do was to break-up the main analysis and do it separately for each task, and check the effect of anxiety in each of these 3 new analyses. Would that be correct??
Hello there,
I had some questions regarding RMANCOVA.
First of all can i use it when i am actually interested in my continuous predictor (i don't want to just control for it) ?
In my study, i have a repeated measures design and i want to see whether people's performance on a task was influenced by the type of task (3 levels-within), the type of stimulus (2 levels-within) and trait anxiety measured at the beginning of the experiment (continuous predictor). i am of course interested to see if my continuous has a main effect on performance but also if it interacts with my 2 IVs.
Is it correct to add the continuous predictor as a covariate ?? or should i do multi-level modeling?
And if it is the correct analysis, what do i do when i have a significant interaction between my IV and the co-variate?? how do i examine that???
Thank you in advance for all the help,
Elena
(stats do make me cry)