FINGERPRINTING TIME-SERIES - DYNAMIC PATTERNS IN SELF-REPORT AND PERFORMANCE-MEASURES UNCOVERED BY A GRAPHICAL NONLINEAR METHOD

Two simple non-linear techniques are shown to be useful for understand ing the dynamics of affect, symptoms, social interaction experience an d cognitive performance. The techniques are justified by arguments der ived from chaos theory, and demonstrated using data from an intensive time sampling study in which 30 subjects completed a set of self-ratin gs and a memory task on a hand-held computer every two hours during wa king hours for 14 days. The data were pooled and two types of Poincare plot were constructed for each variable. The first was a plot of each value against its predecessor, and the second was a plot of each chan ge in value from one interval to the next against the previous change. These plots are particularly suitable for uncovering asymmetric state -dependent changes in control between time points. The plots showed a number of distinctive 'fingerprints' for the different variables. Alto gether, the results suggest that the plots are a novel and useful meth od for understanding psychological variables in terms of their dynamic control.