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Quantifying idiosyncratic and shared contributions to judgment.

Joel E Martinez1,2, Friederike Funk3, Alexander Todorov4

  • 1Department of Psychology, Princeton University, Princeton, NJ, USA. joelem@princeton.edu.

Behavior Research Methods
|January 4, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces variance component analyses (VCAs) to quantify individual and shared contributions in human judgments. VCAs accurately capture psychometric properties and offer a rigorous method for analyzing agreement and disagreement in any judgment domain.

Keywords:
Interrater agreementJudgmentMeasurement errorMultilevel modelingVariance component analysis

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Area of Science:

  • Psychology
  • Behavioral Science
  • Psychometrics

Background:

  • Quantifying individual versus shared contributions to judgments is crucial in behavioral science.
  • Existing methods for estimating these contributions are not well-established.
  • Understanding judgment variance is key to fields like social psychology and decision science.

Purpose of the Study:

  • To introduce and validate variance component analyses (VCAs) as a method for estimating idiosyncratic and shared contributions to judgments.
  • To demonstrate the generalizability and precision of VCAs across different stimulus types and sample sizes.
  • To provide a rigorous framework for quantifying agreement and disagreement in behavioral data.

Main Methods:

  • Utilized variance component analyses (VCAs) on datasets with varying intrarater reliability and interrater agreement.
  • Employed simulations to assess VCA generalizability, and the impact of sample size and stimulus set size.
  • Analyzed the effect of repeated measures on VCA estimate stability and reliability.

Main Results:

  • VCAs accurately captured psychometric properties across stimuli like faces, objects, and complex patterns.
  • Simulations confirmed VCA generalizability to continuous ratings and identified optimal sample sizes (e.g., 60 raters, 30 stimuli).
  • VCA estimates stabilized with more than two repeated measures, mirroring increased reliability and agreement.

Conclusions:

  • Variance component analyses (VCAs) offer a robust method for dissecting judgment variance into individual and shared components.
  • The VCA framework, implementable with mixed models, is broadly applicable to any domain requiring quantification of agreement and disagreement.
  • This approach enhances the rigorous examination of human behavior by precisely estimating sources of variance in judgments.