Analysis techniques of chaotic patterns: a review of empirical studies in Psychology
Abstract
For a variety of reasons longitudinal research remains uncommon in Psychology. Traditional longitudinal analysis techniques, based on linearity assumption, offer limited ways to study different types of changes over time.
This article describes nonlinearity analysis techniques, with emphasis in those whom allow the study of chaotic patterns in time series (Lyapunov exponents, recurrence plots, surrogate data, Kolmogorov‟ entropy, Hurst‟ exponent, and correlation dimension).
Our main objective is to check the use of these techniques in Psychology with a research review of PsycINFO articles database. Results show the wide use of these techniques in some topics and the prevalence of articles published on impact factor‟ journals. A considerable amount of articles have been cited. All of these reveal the growing presence of these techniques in Psychology.
Finally, we think that techniques based on chaos theory offer a complementary perspective to study time series patterns‟. We expect a grown in their application to psychological research.
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