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

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.

Cultural pressure and biased responding in free will attitudes

“…To test whether participants had positive/negative attitudes towards free will/determinism (Hypothesis 1, see table 1), we assessed implicit and explicit FWAs. In order to assess implicit FWAs, we used the IAT A score (also referred to as Ruscio's A or the probability of superiority [34]), which is of higher psychometric quality than the commonly reported IAT D score [31,35]. In either case, IAT scores reflect the degree to which a participant is quicker to respond when 'free will' shares a response mapping with positive words and 'determinism' with negative words relative to the opposite mapping.…”