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This series aims to make statistics and research design more accessible to investigators and students. Each volume offers guidance on how design and theoretical issues affect implementation and interpretation; common errors to avoid; and how to interpret the output from computer program packages.
Provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. Topics covered include research design, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series.
List of contents
Contents
1. Research Questions for Time-Series and Spectral Analysis Studies
2. Issues in Time-Series Research Design, Data Collection, and Data Entry: Getting Started
3. Preliminary Examination of Time-Series Data
4. Harmonic Analysis
5. Periodogram Analysis
6. Spectral Analysis
7. Summary of Issues for Univariate Time-Series Data
8. Assessing Relationships between Two Time Series
9. Cross-Spectral Analysis
10. Applications of Bivariate Time-Series and Cross-Spectral Analyses
11. Pitfalls for the Unwary: Examples of Common Sources of Artifact
12. Theoretical Issues
Appendix A. Raw Time-Series Data
Appendix B. Critical Values for the Fisher Test of Significance for Periodogram Analysis
About the author
Rebecca M. Warner, PhD, is Professor of Psychology at the University of New Hampshire. Her research interests include communication style, cardiovascular reactivity and modulation of physiological rhythms in social interactions, and coordination of talk patterns in conversation.
Summary
This text provides a thorough introduction to methods for detecting and describing cyclic patterns by clarifying key concepts and covering topics such as research design issues, preliminary data screening and identification and description of cycles.
