The aims of this tutorial are three-fold: (a) to clarify the meaning of variability measurement in personality and social psychology, (b) to demonstrate the relevance of and the need for time series analysis in investigations into the dynamics of psychological phenomena, and (c) to provide specific methods to analyze time series. This paper first presents a step-by-step description of univariate Auto-Regressive-Integrated-Moving-average (ARIMA) procedures, which are useful tools for building iterative models from empirical time series. We then develop two empirical examples in detail, based on the analysis of self-esteem and behavioral data. These examples allow us to present the two most often used models.
Marina Fortes is with the Laboratory “Motricity, Interactions, Performance,” University of Sport Sciences, Nantes France. E-mail: firstname.lastname@example.org. Grégory Ninot and Didier Delignières are with Laboratory “Sport, Performance, Health” at The University of Sport Sciences, Montpellier.