Re-assessing the incremental predictive validity of Implicit Association Tests
Bibliographic record
Abstract
Indirect measures of attitudes or stereotypes, such as the Implicit Association Test (IAT), assess associations that are relatively automatic, unintentional, or uncontrollable. A primary argument for the IAT’s use is that it can predict relevant outcomes beyond parallel direct measures, such as self-report (a claim referred to as demonstrating incremental predictive validity). Prior work on this issue relied primarily on least squares linear regression analyses, which are unable to correct for measurement (un)reliability and may then seriously inflate false positive rates in claims of incremental predictive validity. Properly accounting for the impact of measurement reliability requires using Structural Equation Modeling (SEM). In a pre-registered analysis, we investigated 10 IATs and 250 outcomes variables ( N > 14,000), and found that 69.6% of outcomes were reliably correlated with the IAT. Among outcomes that were associated with both the IAT and self-report, the IAT showed incremental predictive validity in 58.6% of cases using least squares linear regression analysis and 59.2% of cases when using SEM, with the two analytic approaches reaching the same conclusion 91.4% of the time. Though the two analysis strategies largely converged, discrepancies were large enough to suggest a non trivial percentage of conclusions drawn from least squares linear regression will be erroneous. As only SEM properly accounts for measurement reliability, it should be adopted in future analyses. To facilitate that goal, we provide tools for researchers to complete SEM analyses on tests concerning the incremental predictive validity of the IAT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.133 | 0.547 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".