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Area of Science:

  • Psychology
  • Social Psychology
  • Psychometrics

Background:

  • The Implicit Association Test (IAT) is widely used to measure implicit biases.
  • However, its test-retest reliability is insufficient for assessing stable, trait-level constructs.
  • This limitation hinders the accurate measurement of individual differences in implicit attitudes.

Purpose of the Study:

  • To evaluate the extent to which the IAT measures individual differences.
  • To test a method for improving the validity of the IAT as a trait measure by aggregating across multiple administrations.
  • To determine if aggregating IATs enhances the estimation of implicit bias.

Main Methods:

  • Conducted three studies with a total of 960 participants.
  • Participants completed multiple IATs within the same session or across different sessions.
  • Latent-variable models were employed to analyze the data and assess score variance.

Main Results:

  • Approximately half of the variance in single IAT scores is attributable to individual differences.
  • Aggregating scores from multiple IATs approximately doubled the variance explained by explicit measures.
  • A single IAT D-score was found to be inadequate for estimating trait-level bias.

Conclusions:

  • IAT scores contain substantial measurement error (noise).
  • A single IAT administration is insufficient for reliably estimating an individual's stable implicit bias.
  • Aggregating multiple IAT administrations can significantly improve the reliability and validity of measuring individual differences in implicit attitudes.