Survey-Software Implicit Association Tests: A Methodological and Empirical Analysis
Bibliographic record
Abstract
The Implicit Association Test (IAT) is widely used in psychology. Unfortunately, the IAT cannot be run within online surveys, requiring researchers who conduct online surveys to rely on third-party tools. We introduce a novel method for constructing IATs using online survey software (Qualtrics); we then empirically assess its validity. Study 1 (student n = 239) found good psychometric properties, expected IAT effects, and expected correlations with explicit measures for survey-software IATs. Study 2 (MTurk n = 818) found predicted IAT effects across four survey-software IATs (d’s = 0.82 [Black-White IAT] to 2.13 [insect-flower IAT]). Study 3 (MTurk n = 270) compared survey-software IATs and IATs run via Inquisit, yielding nearly identical results and intercorrelations expected for identical IATs. Survey-software IATs appear reliable and valid, offer numerous advantages, and make IATs accessible for researchers who use survey software to conduct online research. We present all materials, links to tutorials, and an open-source tool that rapidly automates survey-software IAT construction and analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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 teacher head, 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".