ABSTRACT
This chapter describes the main problems that growth curve analysis is meant to address. It discusses a particular kind of data, called time course data or longitudinal data, which involve systematic relationships between observations at different time points. Time course data are the result of making repeated observations or measurements at multiple time points. Two key properties distinguish time course data from other kinds of data. The first is that groups of observations all come from one source, which is called nested data. The second key property of longitudinal data is that the repeated measurements are related by a continuous variable. For continuous predictors, one can do that kind of simple comparison, but it is also possible to assess the shape of the change – whether the relationship between letter recognition accuracy and letter size follows a straight line, or accuracy improves rapidly for smaller sizes and then reaches a plateau, or follows a U-shape.
