ABSTRACT
Longitudinal data occur most frequently in the behavioral sciences in the clinical trials frequently undertaken by psychologists, psychiatrists and others to assess the effectiveness or otherwise of different treatments. The analysis of repeated measures data and for longitudinal data, models are needed that can both assess the effects of explanatory variables on the multiple measures of the response variable and account for the likely correlations between these multiple measures. Graphical displays of data are almost always useful for exposing patterns in the data, particularly when these are unexpected; this might be of great help in suggesting which class of models might be most sensibly applied in the later more formal analysis. Graphic for longitudinal data that is often helpful in making informed decisions about models that might be appropriate for the data is the scatterplot matrix. The graphical methods can provide insights into both potentially interesting patterns of response over time and the structure of any treatment differences.
