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Related Experiment Videos

Statistical issues in the study of temporal data: daily experiences.

S G West1, J T Hepworth

  • 1Department of Psychology, Arizona State University, Tempe 85287-1104.

Journal of Personality
|September 1, 1991
PubMed
Summary

This study addresses statistical challenges in temporal data analysis, focusing on nonindependence and causality. It introduces concomitant time-series analysis and discusses methods for handling trends, cycles, and pooling data for robust scientific insights.

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Area of Science:

  • Statistics
  • Psychology
  • Data Science

Background:

  • Temporal data presents unique statistical challenges, including nonindependence of observations and complex data structures.
  • Accurate analysis of temporal data is crucial for understanding phenomena over time and establishing causality.

Purpose of the Study:

  • To review and address key statistical issues encountered in temporal data analysis, particularly for daily experience data.
  • To present methods for analyzing temporal data, from single-subject studies to pooled cross-sectional and time-series data.

Main Methods:

  • Concomitant time-series analysis is illustrated for examining relationships between multiple time series with sufficient observations.
  • Methods for detecting and correcting trend, cycles, and serial dependency in temporal data are discussed.

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  • Structural equation modeling is considered for analyzing longitudinal data with many subjects and few time points.

    • Main Results:

    • Concomitant time-series analysis provides a general method for relationship examination with >=50 observations.
    • Techniques for addressing issues like trend, cycles, and serial dependency improve temporal data reliability.
    • Different statistical models are suitable for varying data structures (e.g., few observations/many subjects vs. many observations/few subjects).

    Conclusions:

    • The optimal statistical approach for temporal data depends on substantive theory, research questions, data properties, and model assumptions.
    • Careful consideration of statistical issues like nonindependence and causality is vital for valid temporal data interpretation.
    • Advanced methods like concomitant time-series analysis and structural equation modeling offer solutions for complex temporal data structures.