Spectral Analysis of Time-Series Data

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A01=Rebecca M. Warner
analysis
artefact detection techniques
Author_Rebecca M. Warner
behavioural time series
Category=JHBA
Category=JMB
cyclic behavioural pattern analysis
data
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
in
methodology
physiological signal cycles
psychological data analysis
psychology
research
research design methods
sciences
series
social
sociology
spectral
SPSS data processing
statistics
the
time

Product details

  • ISBN 9781572303386
  • Weight: 520g
  • Dimensions: 152 x 229mm
  • Publication Date: 19 Jan 1999
  • Publisher: Guilford Publications
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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.

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