ABSTRACT
This chapter provides an overview of how longitudinal data can be analyzed using mixed effects models. It discusses how mixed effects models benefits L2 researchers, before providing a practical example of how longitudinal mixed effects data analysis can be conducted. The main focus of this chapter employs the lme4 package, which provides an up-to-date implementation of linear mixed effects models. The random effects parameters is needed to model all known sources of random variance amongst the different participants. The summary shows that it includes random intercepts for "student" and "class" and provides information about the variance associated with each. The syntax 1+c_time includes a random intercept, a random slope for c_time and a correlation between the two. This allows for the possibility that there may be a correlation between the random intercepts and random slopes. Random intercepts for subjects and classes were included, as were random slopes for time varying by both students and classes, using maximal random effects structure.
