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Analyze data from the Implicit Association Test
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README.rst

pyiat

pyiat is a Python package to analyze data from the Implicit Association Test (IAT) consistent with the standard IAT scoring algorithm (Greenwald et al., 2003) and Brief IAT scoring algorithm (Nosek et al., 2014). pyiat requires that data are in a pandas DataFrame.

Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197–216. https://doi.org/10.1037/0022-3514.85.2.197

Nosek, B. A., Bar-Anan, Y., Sriram, N., Axt, J., & Greenwald, A. G. (2014). Understanding and Using the Brief Implicit Association Test: Recommended Scoring Procedures. PLOS ONE, 9(12), e110938. https://doi.org/10.1371/journal.pone.0110938

See the Documentation for more details on installation and usage.

Features

pyiat currently supports:

  • Analysis of the IAT and Brief IAT (BIAT)
  • IAT can be analyzed with weighted or unweighted algorithm
  • BIAT can include 1, 2, or 3 blocks
  • Obtain D scores for each stimulus (e.g. word)
  • Output includes overall error percentage as well as error percentages by block (if using a weighted score) and by condition
  • Same output (overall, by block, by condition) for trials considered too fast or too slow
  • Can set reaction time for trials considered too fast or too slow
  • Can set cutoffs for error/too fast/too slow flags indicating that a participant is excluded for poor performance
  • Can print output to Excel
  • Can return the total number and percentage of trials removed because they were too fast or too slow

see the Documentation for more details

Examples

For details of the input, output and more examples of pyiat see the Jupyter notebook located on Github. There is also simulated data on Github as well.

How to get help or provide feedback

For help or feedback, please enter an issue on Github

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