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Jul 24, 2015 at 14:41 comment added L_T Thank you for the link and for your answer. Actually I would ask you to have a look to this related question about how reporting the results in the different possible cases of using different models: stats.stackexchange.com/questions/162804/…
Jul 23, 2015 at 12:30 comment added conjugateprior By more information I meant more information about independent variation in things that are naturally correlated. Experimental manipulation is the usual way, but that's probably not possible here. So just interpret the marginal effects.
Jul 23, 2015 at 12:29 comment added conjugateprior Is stats.stackexchange.com/questions/31027/… helpful?
Jul 21, 2015 at 13:34 comment added L_T I think I understood the marginality principle and now my functions are: lme_centroid <- lme(Mode ~ Weight*Height, data = Gravel_ds, random = ~1 | Subject) followed by anova.lme(lme_centroid,type = "marginal"). However, while I get significance for this model for some factors, I do not get significance if I add a third factor in the formula. I do not know how to proceed. You were mentioning that additional information is required in order to disentangle the effects. Can you be more specific please?
Jul 21, 2015 at 0:18 comment added L_T Thanks a lot, your answer is really informative! However, it is not clear to me how should I proceed in my analysis then. I guess have enough data since I have 19 subjects and 6 repetitions for the investigated stimulus for a total of 114 measurements. I want to know whether participants’ choices of the dependent variable depend on their weight, height, shoe size and the combination of those effects. Would the formula of model 2 be correct? Which results should I take into account then? Those of model 2? What is your recommendation?
Jul 20, 2015 at 21:20 history edited conjugateprior CC BY-SA 3.0
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Jul 20, 2015 at 15:14 history edited conjugateprior CC BY-SA 3.0
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Jul 20, 2015 at 15:01 history answered conjugateprior CC BY-SA 3.0