Description
Book SynopsisThis book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.
Trade ReviewThis is an excellent book for behavioral and social scientists seeking a quick but thorough introduction to spectral analysis. Rigorous in presenting basic equations, it also features practical examples that facilitate the learning process. The author's clear exposition and use of commonly accessible software to illustrate analyses will help readers make the leap from reading this book to actually analyzing their own time-series data. --Randy J. Larsen, PhD, Department of Psychology, University of Michigan
A wonderful book, filled with clear language and interesting examples. Warner helps us understand how many of the enduring features of life are repetitive ones that cannot be described in terms of means and static relationships. A common-sense guide to cyclical patterns in time-series data, the volume is both practical and intellectually stimulating. --James M. Dabbs, PhD, Department of Psychology, Georgia State University
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Table of ContentsContents
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
