Overview

This book introduces techniques developed in physics and physiology for characterizing and analyzing patterns in time series data to a broad audience of social scientists. In contrast to time-series regression and related techniques, recurrence quantification analysis (RQA) has its background in chaos and nonlinear dynamical systems—theory arguably very relevant to social processes. The goal of Recurrence-Based Analyses is to introduce readers to these techniques that can characterize a system’s complexity, stability and instability, and conditions under which it transitions from one state to another. The authors illustrate concepts and techniques with relevant social science examples at different temporal scales: biweekly polling data on federal elections in Germany; daily values of three stock market indices; daily cases of SarsCov-19 in four countries during the pandemic; and second-by-second vocalizations of mothers and infants interacting recorded by motion cameras. This introduction to RQA serves as a useful supplement to undergraduate and graduate courses in computational social science, and also by researchers who seek new tools to address social scientific questions in new ways.

Full Product Details

9781071872338

Table of Contents

Reviews

This book is a solid introduction to recurrence-based analysis. It is very accessible. The authors explain the basic elements in clear language that most methodologically-oriented students will find understandable. The book is also surprisingly comprehensive for such an introductory treatment. -- Courtney Brown The authors provide a clear and concise introduction that makes an advanced topic accessible to a broad audience. -- Clayton Webb This book provides an intuitive introduction to a topic with much potential for social science applications, but one whose daunting mathematical demands have made it inaccessible to most students and scholars. -- David McDowall This book presents an advanced quantitative technique to social science researchers in a clear and precise manner, offering a perfect balance of technical depth and accessibility. -- Duan Zhang

Author Information

Sebastian Wallot obtained his diploma in psychology from the University of Trier (Germany) and his PhD in experimental psychology from the University of Cincinnati, OH (USA). After postdoctoral positions at the University of Aarhus (Denmark) and the Max Planck Institute for Empirical Aesthetics in Frankfurt at the Main (Germany), he is currently working as Professor for research methods in psychology at Leuphana University of Lüneburg (Germany). His research if focused on joint action and reading from a dynamic systems perspective. Moreover, he is developing new analysis tools for time series – particularly in the area of recurrence and fractal analysis. Giuseppe Leonardi obtained his MA degree in psychology from the University of Padua (Italy) and his PhD in experimental psychology at the University of Trieste (Italy). As a visiting student he was at the Center for Complex Systems at Florida Atlantic University (USA). His interests gradually focused on a dynamical approach in behavioral interactions and the methodological challenges this new approach requires. He especially concentrated on RQA and its applications to human language and cooperative behavior. From 2017 he has been at the University of Economics and Human Sciences in Warsaw (Poland), where he serves as dean of the School of Human Sciences since 2019.

Tab Content 6

Countries Available