I have problems in undertanding how to correctly run a mixed linear model.
I have done an experiment to compare three groups (1 between-subject factor); for each subject, 2 sensory stimuli are given (factor1), each stimulus is given in 3 conditions (factor2), for each condition 3 different spatial positions are used on a screen (factor3). Subjects have to answer which condition they are presented.
Thus, I have 3 way crossed factorial design (2x3x3).
Each subject gives N responses for each combination of factors. Then average accuracy rates (ACC) and average reactions times are registered for each combination (RT).
Since I have missing data in some conditions I want to use linear mixed model.
For the moment I am interested in understanding how accuracy rates change according to my main effects as well as interations between my within-subject factors and between-subject factor by accounting for age, gender and RT.
I am new to mixed models.
I have inserted (in spss) my main effects as well as interactions between grouping factor and my within subject factors treating them as fixed factors. I treated covariates as fixed factors as well.
As far as I have understood I can account for differences among subjects by including a random intercept to the model.
I would like to know whether my model is correct in this way or if I have to further model covariance structure of residuals (using for instance a /repeated statement in SPSS format).
Any comment will be appreciated!