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Spectral and cross-spectral analysis-A tutorial for psychologists and social scientists.

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Social scientists can now model cyclical patterns in time-series data using spectral and cross-spectral analysis. These methods reveal periodic components and synchrony in dyadic data, overcoming limitations of traditional analytical techniques.

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Area of Science:

  • Social Sciences
  • Quantitative Psychology
  • Behavioral Data Analysis

Background:

  • Intensive longitudinal methods are increasingly used to study dynamic social phenomena.
  • Many social processes exhibit cyclical fluctuations (e.g., mood, sleep).
  • Existing analytical methods often fail to capture these temporal patterns.

Purpose of the Study:

  • Introduce spectral and cross-spectral analysis for social science research.
  • Demonstrate their application to time-series data, including dyadic and group-level data.
  • Provide guidance on estimating significance and effect size for these analyses.

Main Methods:

  • Spectral analysis to decompose time series into periodic components.
  • Cross-spectral analysis to identify synchrony and phase offsets in dyadic data.
  • Nonparametric bootstrapping for significance testing and effect size estimation.

Main Results:

  • Spectral analysis effectively identifies periodicities in univariate and bivariate time-series data.
  • Cross-spectral analysis reveals synchrony and time offsets in dyadic time series.
  • Circular statistics and polar plots aid in visualizing phase relationships.

Conclusions:

  • Spectral and cross-spectral analyses offer powerful tools for studying cyclical social phenomena.
  • These frequency-domain methods complement traditional time-domain approaches.
  • Accessible application via provided Jupyter Notebook facilitates adoption in social science research.