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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.

studying the role of ER in the association between reactive temperament and psychopathology/well-being, based on W4, W6 and W11 of the Nijmeegse Longitudinale Studie

During the last two decades a new class of indirect measurement procedures has emerged that has been used widely in psychological science. These procedures were developed in order to circumvent the limitations of self-reports and crack open the hidden world of ‘implicit’ cognition. Yet despite their popularity there seems to be no general framework that constrains (or guides) the way in which we think about the reliability of implicit measures. Instead there are far too many (subjective) researcher degrees of freedom when it comes to deciding how to assess, interpret, and even report reliability. In this paper we introduce such a framework and argue that it can be used by novel and seasoned researchers alike to estimate and interpret the reliability of their implicit measures. Our approach draws on Latent Variable Modeling and the idea of parceling in order to approximate reliability (in the sense of consistency, equivalence, stability) and test the assumptions that underlie those approximations. We close by discussing the implications of our framework for the conceptualization of reliability in particular and for implicit cognition research more generally.

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.

Background Individuals in remission from depression are at increased risk for developing future depressive episodes. Several cognitive risk- and resilience factors have been suggested to account for this vulnerability. In the current study we explored how risk- and protective factors such as cognitive control, adaptive and maladaptive emotion regulation, residual symptomatology, and resilience relate to one another in a remitted depressed (RMD) sample. Methods We examined the relationships between these constructs in a cross-sectional dataset of 69 RMD patients using network analyses in order to obtain a comprehensive, data-driven view on the interplay between these constructs. We subsequently present an association network, a concentration network, and a relative importance network. Results In all three networks resilience formed the central hub, connecting perceived cognitive control (i.e., working memory complaints), emotion regulation, and residual symptomatology. The contribution of the behavioral measure for cognitive control in the network was negligible. Moreover, the directed relative importance network indicates bidirectional influences between these constructs, with all indicators of centrality suggesting a key role of resilience in remission from depression. Limitations The presented findings are cross-sectional and networks are limited to a fixed set of key constructs in the literature pertaining cognitive vulnerability for depression. Conclusions These findings indicate the importance of resilience to successfully cope with stressors following remission from depression. Further in-depth studies will be essential to identify the specific underlying resilience mechanisms that may be key to successful remission from depression.