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Longitudinal data analysis focused on internal characteristics of a single time series has attracted increasing interest among psychologists. The systemic psychological perspective suggests, however, that many long-term phenomena are mutually interconnected, forming a dynamic system. Hence, only multivariate methods can handle such human dynamics appropriately. Unlike the majority of time series methodologies, the cointegration approach allows interdependencies of integrated (i.e., extremely unstable) processes to be modelled. This advantage results from the fact that cointegrated series are connected by stationary long-run equilibrium relationships. Vector error-correction models are frequently used representations of cointegrated systems. They capture both this equilibrium and compensation mechanisms in the case of short-term deviations due to developmental changes. Thus, the past disequilibrium serves as explanatory variable in the dynamic behaviour of current variables. Employing empirical data from cognitive psychology, psychosomatics, and marital interaction research, this paper describes how to apply cointegration methods to dynamic process systems and how to interpret the parameters under investigation from a psychological perspective.