Field-Identification IAT predicts students’ academic persistence over and above Theory of Planned Behavior constructs

Abstract

Ajzen and Dasgupta (2015) recently invited complementing Theory of Planned Behavior (TPB) measures with measures borrowed from implicit cognition research. In this study, we examined for the first time such combination, and we did so to predict academic persistence. Specifically, 169 first-year college students answered a TPB questionnaire and completed a field-identification Implicit Association Test (IAT). The IAT measure largely predicted academic persistence 6 months later over and above TPB constructs, including behavioral intention. We discuss interpretations of this finding and its relevance to educational research.

Forecasting a Fatal Decision: Direct Replication of the Predictive Validity of the Suicide–Implicit Association Test

“…The present findings challenge the common assumption that implicit associations, and implicit bias more generally, have poor predictive validity (Jost, 2018). On the contrary, these findings add to recent research, showing that indirect measures of implicit cognition predict real-world behavior better than explicit measures (Faure et al, 2018;Kurdi et al, 2018;Nock & Banaji, 2007;Roland et al, 2018;Serra et al, 2019). The present findings also have clear clinical implications.…”

“…From a theoretical viewpoint, Nock et al’s (2010) findings are important because they contribute to a growing body of work showing that the Implicit Association Test (IAT) has good predictive validity (Faure, Righetti, Seibel, & Hofmann, 2018; J. J. Glenn, Werntz, et al, 2017; Greenwald, Poehlman, Uhlmann, & Banaji, 2009; Kurdi et al, 2019; Nock & Banaji, 2007; Roland, Mierop, Frenay, & Corneille, 2018; Serra et al, 2019). The question of whether the IAT can predict complex behavior over and above existing measures has generated considerable debate and controversy in social psychology (for a recent review, see Jost, 2018), the discipline in which the test was initially proposed (Greenwald, Nosek, & Banaji, 2003).…”

Associative Attitude Learning: A Closer Look at Evidence and How It Relates to Attitude Models

“…The IAT, for instance, may help to predict future voting behavior among undecided voters (Arcuri, Castelli, Galdi, Zogmaister, & Amadori, 2008), and seems to better predict future suicide attempts in patients admitted in psychiatric emergency units than psychiatrists do on the basis of a clinical interview (Nock et al, 2010). In a recent study, a field-identification IAT measure was shown to better predict academic perseverance in undergraduate students than did a Theory of Planned Behavior Questionnaire (Roland, Mierop, Frenay, & Corneille, 2018). More generally, we believe that dual models of attitudes, such as Petty and Cacioppo’s (1986) elaboration likelihood model, Chaiken’s (1987) heuristic-systematic model, Fazio (1990)’s MODE model and Strack and Deutsch’s (2004) Reflective-Impulsive Model (i.e., RIM) have delivered very useful insights on how evaluations relate to behaviors.…”