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The PI: An alternative scoring algorithm for the IRAP using a probabilistic semiparametric effect measure

Author

Listed:
  • De Schryver, Maarten
  • Hussey, Ian

    (Ghent University)

  • De Neve, Jan
  • Cartwright, Aoife
  • Barnes-Holmes, Dermot

Abstract

The Implicit Relational Assessment Procedure (IRAP) has been used to assess the probability of arbitrarily applicable relational responding or as an indirect measure of implicit attitudes. To date, IRAP effects have commonly been quantified using the DIRAP scoring algorithm, which was derived from Greenwald, Nosek and Banaji’s (2003) D effect size measure. In the article, we highlight the difference between an effect size measure and a scoring algorithm, discuss the drawbacks associated with D, and propose an alternative: a probabilistic, semiparametric measure referred to as the Probabilistic Index (Thas, De Neve, Clement, & Ottoy, 2012). Using a relatively large IRAP dataset, we demonstrate how the PI is more robust to the influence of outliers and skew (which are typical of reaction time data), and improves internal consistency. Finally, we conclude that PI models, in addition to producing point estimate scores, can also provide confidence intervals, significance tests, and afford the possibility to include covariates, all of which may aid single subject design studies.

Suggested Citation

  • De Schryver, Maarten & Hussey, Ian & De Neve, Jan & Cartwright, Aoife & Barnes-Holmes, Dermot, 2017. "The PI: An alternative scoring algorithm for the IRAP using a probabilistic semiparametric effect measure," OSF Preprints xaqn6, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:xaqn6
    DOI: 10.31219/osf.io/xaqn6
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    References listed on IDEAS

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    1. Peter Bentler, 2009. "Alpha, Dimension-Free, and Model-Based Internal Consistency Reliability," Psychometrika, Springer;The Psychometric Society, vol. 74(1), pages 137-143, March.
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