Spectral Analysis of Time-Series Data

1. Auflage 1998
ISBN: 978-1-57230-338-6
Verlag: Guilford Publications

Buch, Englisch, 225 Seiten, Format (B × H): 162 mm x 234 mm, Gewicht: 562 g

Reihe: Methodology in the Social Sciences

This 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.

Zielgruppe

Postgraduate, Professional, Professional Practice & Development, and Undergraduate

Autoren/Hrsg.

Warner, Rebecca M

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Gesellschaftstheorie
• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden

Inhaltsverzeichnis

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

Über Autor(innen)

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.