Visual-Graphical Methods for Exploring Psychological Longitudinal Data ∗

Longitudinal data typically have the characteristics of changes over time, nonlinear growth patterns, between-subjects variability, and the within errors exhibiting heteroscedasticity and dependence. The data exploration is more complicated than that of cross-sectional data. The purpose of this paper is to organize/integrate various visual-graphical techniques to explore longitudinal data. From the application of the proposed methods, investigators can answer the research questions including characterizing or describing the growth patterns at both group and individual level, identifying the time points where important changes and unusual subjects occur, selecting suitable statistical models, and suggesting possible within-error variance.