I’m struggling to figure out the right model specification for eye-tracking data.
The design is a repeated measures with between-subject factors ‘visual-world’ eye-tracking study: Two types of participants (L1_L2 Speakers), 32 subjects in the L1 group and 24 subjects in the L2 group, each of the subjects is tested to the two conditions (tense: past vs future). Each subject exposed to 10 past and 10 future tense sentences while viewing scenes with many objects, but the data includes only the fixations from two target objects.
Independent variables (IV) are Participants (L1 vs. L1 Speakers) and sentence tense (Past vs. Future tense). Dependent variable (DV) is fixation durations: a participant looks to a particular object at a given time, which gives a continuous variable (time ('t' in model)) on a categorical variable (gaze location).
I'm interested in whether L2 speakers differ from the L1 group in processing tense sentences. My first question: I am not sure if I can conduct the empirical logit analysis when in one group I have fewer participants than in another group.
The following model has been used for the L1 group within-subject design (which is similar to the model on this page: talklab.psy.gla.ac.uk/tvw/elogit-wt.html):
1000ms <- lmer(elog ~ tense.ct*t + (1|ParticipantName), data=bySubj1000, weights=1/wts) %
The second question is I'm unclear about the between-subject with this model. It seems there are, in principle, different ways to test for an effect of type (L1 vs. L2) in this design, and I'm not sure how different they are, or which one makes more sense. One approach is just to treat type (L1_L2) as a standard fixed effect. E.g. something like:
a1000ms <- lmer(elog ~ tense.ct*L1_L2.ct*t + (1|ParticipantName), data=bySubj1000, weights=1/wts)
b1000ms <- lmer(elog ~ tense.ct*L1_L2.ct*t + (1+L1_L2.ct*t|ParticipantName), data=bySubj1000, weights=1/wts)
c1000ms <- lmer(elog ~ tense.ct*L1_L2.ct*t + (1+tense.ct*L1_L2.ct*t|ParticipantName), data=bySubj1000, weights=1/wts)