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Correcting bias in extreme groups design using a missing data approach.

Lihan Chen1, Rachel T Fouladi2

  • 1Department of Psychology, University of British Columbia.

Psychological Methods
|July 18, 2022
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Summary
This summary is machine-generated.

Extreme groups design (EGD) improves study power on a budget but biases results. Modern missing data techniques, like full information maximum likelihood (FIML), can correct these biases in EGD data.

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Area of Science:

  • Psychological research methodology
  • Statistical analysis in behavioral science

Background:

  • Extreme groups design (EGD) enhances statistical power within budget constraints.
  • EGD involves recruiting participants with the lowest and highest scores on a screening variable.
  • Standardized estimates derived from EGD are known to be biased.

Purpose of the Study:

  • To demonstrate that the bias in EGD stems from its inherent missing at random mechanism.
  • To show that modern missing data techniques can correct EGD biases.
  • To provide a practical guide for computing correlations in EGD data using R.

Main Methods:

  • Utilizing a missing at random (MAR) framework to understand EGD bias.
  • Applying full information maximum likelihood (FIML) estimation to correct for bias.
  • Developing a tutorial for implementing FIML in R for EGD correlation analysis.

Main Results:

  • The missing at random mechanism in EGD inherently introduces bias into standardized estimates.
  • Full information maximum likelihood (FIML) effectively corrects for the bias introduced by EGD.
  • The R tutorial provides a reproducible method for accurate correlation computation in EGD.

Conclusions:

  • EGD bias is a correctable consequence of its sampling strategy.
  • FIML is a robust method for addressing missing data issues in EGD.
  • Researchers can improve the accuracy of findings from EGD studies using FIML.