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Related Experiment Video

Updated: Mar 16, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Modeling stimulus variation in three common implicit attitude tasks.

Katie Wolsiefer1, Jacob Westfall2, Charles M Judd3

  • 1University of Colorado, Boulder, CO, USA. katherine.wolsiefer@colorado.edu.

Behavior Research Methods
|August 14, 2016
PubMed
Summary
This summary is machine-generated.

Ignoring stimulus variation in implicit attitude research inflates statistical results by about 60%. This study provides models to correctly analyze implicit attitude measures like the Implicit Association Test, affect misattribution procedure, and evaluative priming task.

Keywords:
Implicit attitudesMixed modelsStimulus sampling

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Area of Science:

  • Social Psychology
  • Psycholinguistics
  • Quantitative Psychology

Background:

  • Implicit attitude research often overlooks stimulus sampling variation.
  • Existing statistical recommendations from psycholinguistics are not widely applied in social psychology.
  • Complications exist in applying crossed random-effect models to common implicit attitude tasks.

Purpose of the Study:

  • To investigate the consequences of ignoring stimulus variation in implicit attitude measurement.
  • To develop and apply statistically appropriate models for implicit attitude tasks.
  • To quantify the inflation of test statistics when stimulus variation is disregarded.

Main Methods:

  • Developed statistically sound crossed random-effect models for the Implicit Association Test, affect misattribution procedure, and evaluative priming task.
  • Applied these models to large datasets (N ≈ 3,206) assessing implicit attitudes.
  • Provided R, SAS, and SPSS syntax for implementing the recommended models.

Main Results:

  • Traditional analyses of implicit attitude data show substantially inflated test statistics (approx. 60% inflation) compared to models incorporating stimulus variation.
  • The study quantified the relative contributions of stimulus variation across different implicit attitude tasks.
  • Demonstrated the consequential nature of random stimulus variation in implicit attitude measurement.

Conclusions:

  • Ignoring stimulus variation leads to inflated Type I error rates in implicit attitude research.
  • The proposed crossed random-effect models offer a statistically appropriate and feasible approach for analyzing implicit attitude data.
  • Accurate statistical treatment of stimulus variation is crucial for reliable implicit attitude measurement.