Recurrence-Based Analyses: (Quantitative Applications in the Social Sciences)

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.

Table of Contents:
Series Editor Introduction Acknowledgments About the Authors Acronyms and Notation Chapter 1: What is Recurrence Analysis? The Recurrence Plot Deriving Recurrence Measures Advantages and Limitations of Recurrence Analysis Chapter 2: The Basics of Recurrence Analysis—Univariate RQA Parameter Estimation The Delay Parameter t The Embedding Parameter m The Radius Parameter e Further Parameters Summarizing RQA Outputs Chapter 3: The Bi-Variate Case: Cross-Recurrence Quantification Analysis Introduction to CRQA Standardization Alignment The Cross-Recurrence Plot (CRP) Using CRQA With Continuous Data: Stock Market Fluctuations Using CRQA With Categorical Data Chapter 4: The Diagonal-Wise Cross-Recurrence Profile (DCRP) Diagonal-Wise Cross Recurrence Profiles (DCRP) Building a Baseline by Means of Shuffling Chapter 5: Windowed Recurrence Analysis Introduction to Univariate Windowed Recurrence Analysis Windowed Cross-Recurrence Analysis Using Windowed Recurrence Analysis for Continuous Monitoring Chapter 6: Multivariate Analysis: Multidimensional Recurrence Quantification Analysis (MdRQA) Introduction to MdRQA Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) An Example Using Multidimensional RQA on Political Polling Data Chapter 7: Sample Analysis and Practicalities Calculating General Parameters Time Series Length Computing Confidence Bounds Via Boot-Strapping Parameter Exploration Surrogate Analysis Dealing With Multiple Recurrence-Measures Chapter 8: Conclusion Further Applications Finding Software A Final Note References Index

About the Author :
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.

Review :
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. The authors provide a clear and concise introduction that makes an advanced topic accessible to a broad audience. 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. 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.