
Dear all,
I am using the lme function to anlyze the results of a drawing elicitation task, where the informants had to draw the trajectory of a moving car, based on stimulus sentences containing particular verbs and types of moving entity. It is a repeated measures design where every subject gives responses to the full set of sentences, which is the product of 7 different verbs and 2 didderent types of moving entity. Several things were coded about each trajectory drawing: the change-of-direction angle in degrees (a linear numeric variable), the relation of the main curve of the trajectory to a reference object in the drawing field (directed vs. distrivuted, coded as 0 vs. 1), the presence vs. absence of a path segment preceding the main curve (coded as 0 vs. 1), and the presence vs. absence of a path segment following the main curve (also coded as 0 vs. 1). My aim is to see how the verbs and the different types of moving entities in the stimulus sentences
 influence each of the four dependent variables. So far I have fitted a separate model for each of the dependent variables :

angle.lme = lme( angle ~ (verb + movingentity)^2, random = ~ 1|subject, data = drawelic)
pathtype.lme = lme( pathtype ~ (verb + movingentity)^2, random = ~ 1|subject, data = drawelic)
preceding.lme = lme( preceding ~ (verb + movingentity)^2, random = ~ 1|subject, data = drawelic)
following.lme = lme( following ~ (verb + movingentity)^2, random = ~ 1|subject, data = drawelic)

However, the preliminary examination of the data, and some of the results for the separate dependent variables make me think that all four dependent vatiables are correlated. What type of analysis should I use with my data, so that also this correlation is taken into account?

Best regards
Liliana Martinez
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