Share
Recurrence-Based Analyses
- Sebastian Wallot - Leuphana University Lüneburg, Germany
- Giuseppe Leonardi - University of Economics and Human Sciences, Poland
Volume:
197
Courses:
Intermediate/Advanced Statistics | Quantitative Methods | Research Methods & Statistics in Sociology | Research Methods in Political Science | Statistics - General Interest | Statistics in Political Science | Statistics in Psychology | Structural Equation Modeling, Hierarchical Linear Modeling, & Multilevel Modeling
Intermediate/Advanced Statistics | Quantitative Methods | Research Methods & Statistics in Sociology | Research Methods in Political Science | Statistics - General Interest | Statistics in Political Science | Statistics in Psychology | Structural Equation Modeling, Hierarchical Linear Modeling, & Multilevel Modeling
December 2024 | 136 pages | SAGE Publications, Inc
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.
Series Editor Introduction
Acknowledgments
About the Authors
Acronyms and Notation
Chapter 1: What is Recurrence Analysis?
Chapter 2: The Basics of Recurrence Analysis—Univariate RQA
Chapter 3: The Bi-Variate Case: Cross-Recurrence Quantification Analysis
Chapter 4: The Diagonal-Wise Cross-Recurrence Profile (DCRP)
Chapter 5: Windowed Recurrence Analysis
Chapter 6: Multivariate Analysis: Multidimensional Recurrence Quantification Analysis (MdRQA)
Chapter 7: Sample Analysis and Practicalities
Chapter 8: Conclusion
References
Index
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.
Emory University
The authors provide a clear and concise introduction that makes an advanced topic accessible to a broad audience.
University of Kansas
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.
University at Albany-SUNY
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.
University of Denver
Sage College Publishing
