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Designing, understanding and modelling two-phase experiments with human subjects.

Christopher James Brien1,2

  • 1UniSA STEM, UniSA STEM, University of South Australia, Adelaide, South Australia.

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Summary

This study introduces a factor-allocation paradigm for designing experiments and analyzing data, particularly for training effects on pain ratings in therapy students. It refines analysis by recommending models with heterogeneous residual variances.

Keywords:
ANOVAAnalysis of varianceblock-treatment interactiondesign anatomyhuman experimentsintertier interactionlaboratory phaselinear mixed modelsmultiple randomizationstwo-phase experiments

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Area of Science:

  • Statistics
  • Experimental Design
  • Biostatistics

Background:

  • A prior study examined training effects on pain ratings among occupational and physical therapy students.
  • Analysis of this experiment highlighted the need for advanced statistical strategies.

Purpose of the Study:

  • To illustrate a multi-step factor-allocation paradigm for experimental design and data analysis.
  • To demonstrate the application of this paradigm in understanding confounding and formulating prior allocation models.
  • To re-examine a previous pain-rating experiment and propose refined analytical models.

Main Methods:

  • Utilized a multi-step factor-allocation paradigm for experimental design.
  • Employed an analysis-of-variance-style table to understand design confounding.
  • Formulated linear mixed models (prior allocation models) for data analysis.
  • Re-analyzed a two-phase experiment on training and pain ratings.

Main Results:

  • The factor-allocation paradigm effectively aids in designing experiments and understanding confounding.
  • Prior allocation models serve as robust starting points for data analysis.
  • Re-analysis of the pain-rating experiment suggests a model with heterogeneous residual variances is optimal.
  • The paradigm facilitated the creation of an alternative experimental design.

Conclusions:

  • The factor-allocation paradigm offers a comprehensive approach to experimental design and analysis.
  • Refined statistical models, including those accounting for heterogeneous variances, improve the interpretation of experimental data.
  • This methodology provides a framework for developing more robust experimental designs and analyses in future studies.