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Individual Differences in Implicit Bias Can Be Measured Reliably by Administering the Same Implicit Association Test
Thomas P Carpenter1, Alexandra Goedderz2, Calvin K Lai3
1Seattle Pacific University, WA, USA.
Personality & Social Psychology Bulletin
|June 25, 2022
Summary
The Implicit Association Test (IAT) shows low reliability for measuring traits. Aggregating multiple IATs significantly improves the assessment of individual differences in implicit bias.
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.
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