Faking revisited: Exerting strategic control over performance on the Implicit Relational Assessment Procedure

Hughes, Sean; Hussey, Ian; Corrigan, Bethany; Jolie, Katie; Murphy, Carol; Barnes‐Holmes, Dermot

doi:10.1002/ejsp.2207

Cited by 17 publications

(15 citation statements)

References 42 publications

(57 reference statements)

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“…However, due to its block-based nature, the IRAP is limited to assessing implicit beliefs toward a single set of beliefs at a time (i.e., for a given pair of blocks with their associated reference beliefs). In addition, IRAP scores have been shown to be susceptible to faking attempts (Hughes et al, 2016) and often exhibit moderate reliability only (e.g., Remue et al, 2013, 2014; see also Gawronski and De Houwer, 2014). Finally, the IRAP is also held back by substantial dropout rates in participants (more than 20% dropout is reported among university students in Remue et al, 2013; for a discussion, see De Houwer et al, 2015) – an issue that is thought to be attributable to the trial-by-trial response key reassignment.…”

Section: Issue 3: Focus On Associations Versus Beliefsmentioning

confidence: 99%

Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions

et al. 2019

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Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.

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Section: Issue 3: Focus On Associations Versus Beliefsmentioning

confidence: 99%

Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions

et al. 2019

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“…There are two important points to highlight here. First, it appears difficult to fake responding on the IRAP without a concrete strategy and/or detailed instructions which participants are unlikely to spontaneously devise (Drake et al 2016;Hughes et al 2016;McKenna et al 2007). Also, if the Time 2 results represented more effortful, conscious responding, it is interesting to note that there was still an explicit Male-STEM/Female-Arts bias among the groups.…”

Section: Discussionmentioning

confidence: 99%

Examining the effectiveness of brief interventions to strengthen a positive implicit relation between women and STEM across two timepoints

2020

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The current study assessed the impact of three brief interventions aimed at influencing implicit gender bias in Science, Technology, Engineering and Math (STEM) fields. The reduction of this bias is a key consideration as it remains a major barrier to gender equality in STEM. The interventions (psychoeducation, exposure to positive counter-stereotypical exemplars and perspective-taking) were compared to a control group at two timepoints. Gender-STEM bias was assessed at the implicit level (via the Implicit Relational Assessment Procedure; IRAP) and the explicit level (via rating scales). Two hundred and ten adults (58.1% women) completed one of the four study conditions. Results indicated that implicit gender-STEM bias is malleable, at least in the short term. At Time 1, intervention groups showed higher levels of implicit pro-Women-STEM bias and lower levels of pro-Men-STEM bias than the Control group. Psychoeducation appeared most effective, followed by exposure to positive counter-stereotypical exemplars. Results from Time 2 presented a more complex picture of implicit bias change, as the control group exhibited an increased pro-Women-STEM bias that was unexpected. The implications of these findings are discussed in relation to refining the interventions, the malleability of gender-STEM bias and the measurement of implicit bias across contexts.

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“…The should-IRAP would be identical to the are-IRAP, with the exception that the statement at the top of the screen would instead be "Men should be" or "Women should be". In terms of its implicitness, the IRAP has been argued to capture responding under fast and unintentional conditions (Barnes-Holmes et al, 2010), although some evidence disputes its unintentional nature (Drake et al, 2016;Hughes et al, 2016).…”

Section: Overview Of Relational Implicit Measuresmentioning

confidence: 99%

Implicit measures of beliefs

Cummins¹

2020

Preprint

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The assessment of the thoughts and evaluations of human beings is a central feature of modern psychological science. Further to this, many researchers are specifically interested in the automatic thoughts and evaluations of individuals. To assess automatic thoughts and evaluations, researchers typically use a range of measurement procedures whose outcomes are broadly referred to as implicit measures (De Houwer, 2006). Historically, researchers have believed that only associations between stimuli operate at the automatic level. As a consequence, the procedures of implicit measures have tended to measure only associations between stimuli (i.e., without specifying the relations between stimuli). However, growing evidence now suggests that beliefs about how stimuli are related (i.e., implicit beliefs) can also be measured at the automatic level (for a recent review, see De Houwer et al., 2020). This has led to an issue for the measurement of automatic thoughts and evaluations: since most procedures of implicit measures have been developed from an associative perspective, these procedures are unable to accommodate relational information into their stimuli. As a result of the limitations of implicit measures developed from an associative perspective, researchers have begun developing relational implicit measures (i.e., implicit measures of beliefs) which can accommodate relational information. To date, four relational implicit measures have been developed: the Implicit Relational Assessment Procedure (IRAP; Power et al., 2009), the Relational Responding Task (RRT; De Houwer et al., 2015), the Propositional Evaluation Paradigm (PEP; Müller & Rothermund, 2019), and the Autobiographical Implicit Association Test (aIAT; Sartori et al., 2008). Although the above four measures represent initial progress towards developing valid relational implicit measures, there is much work yet to be done: both in terms of developing new relational implicit measures, and in terms of validating existing measures (relative to whether they are valid measures of their construct of interest, whether they capture relational information, and whether they reflect automatic responding). These were precisely the goals of the current project: to develop and validate new and existing relational implicit measures. Chapter 1 begins with a general introduction to the field of implicit measures, and the three ways in which researchers commonly define the “implicit” term. With the historical context of these varying definitions, the Chapter explores why the need for relational implicit measures has only recently emerged, and describes the relational implicit measures which have been developed to date. Chapters 2, 3, and 4 detail the development of three new relational implicit measures: the Truth Misattribution Procedure (TMP), the Propositional Concealed Information Test (pCIT), and the Mousetracking Propositional Evaluation Paradigm (MT-PEP), respectively. Chapters 5 and 6 consist of comparative investigations of multiple relational implicit measures. Specifically, Chapter 5 compares the RRT, aIAT, and pCIT (as well as the standard IAT) in the context of drinking self-identity and the prediction of problematic drinking outcomes, whereas Chapter 6 compares the RRT, aIAT, and MT-PEP (as well as the standard IAT) in the context of sensitivity to experimentally-manipulated relational information. Finally, Chapter 7 provides a summary and synthesis of the results of the empirical chapters. In addition, the Chapter offers reflections on the use of implicit measures more broadly, critiquing the ultimate goals of using such measures, as well as reflecting on why the behaviour of scientists in the field of implicit measures has tended to be relatively unreceptive to recommendations for improving the measures.

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Faking revisited: Exerting strategic control over performance on the Implicit Relational Assessment Procedure

Abstract: . Electronic mail should be sent to sean.hughes@ugent.be.

Cited by 17 publications

References 42 publications

Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions

Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions

Examining the effectiveness of brief interventions to strengthen a positive implicit relation between women and STEM across two timepoints

Implicit measures of beliefs

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