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Time-series analysis in operant research.

R R Jones1, R S Vaught, M Weinrott

  • 1Oregon Research Institute.

Journal of Applied Behavior Analysis
|April 1, 1977
PubMed
Summary
This summary is machine-generated.

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This study introduces a time-series method to analyze behavior changes in operant studies. It statistically identifies level changes, trends, and trend shifts, supplementing visual analysis.

Area of Science:

  • Behavioral Science
  • Psychology
  • Experimental Analysis of Behavior

Background:

  • Traditional visual analysis of behavior change in operant studies can be subjective.
  • Existing methods may not fully capture complex temporal dynamics in behavioral data.
  • The need for robust statistical tools to supplement visual inspection is recognized.

Purpose of the Study:

  • To present a non-technical time-series method for analyzing individual-subject operant data.
  • To recommend this method as a supplement to visual analysis in experimental designs.
  • To identify statistically significant behavior changes, including level shifts, trends, and trend changes.

Main Methods:

  • A time-series analysis approach is detailed for operant study data.

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  • Emphasis is placed on detecting and utilizing serial dependency (autocorrelation) in behavioral scores.
  • The method is illustrated using published examples from the operant literature.

    • Main Results:

    • The time-series method can statistically identify changes in score levels between experimental phases.
    • It reliably detects significant upward or downward trends in behavioral scores.
    • The method is capable of identifying changes in trends across different experimental phases.

    Conclusions:

    • The proposed time-series method offers a statistically rigorous supplement to visual analysis in operant research.
    • It enhances the objective identification of behavior change, accounting for temporal dependencies.
    • This approach is valuable for analyzing data from reversal and multiple-baseline experimental designs.