IAT faking indices revisited : Aspects of replicability and differential validity

Faculty/Professorship: Personality Psychology and Psychological Assessment  
Author(s): Röhner, Jessica  ; Holden, Ronald R.; Schütz, Astrid  
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2023
Pages: 670–693
Source/Other editions: Behavior research methods : BRM, 55 (202), 2, S. 670–693 . - ISSN: 1554-3528
is version of: 10.3758/s13428-022-01845-0
Year of first publication: 2023
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-588155
Research demonstrates that IATs are fakeable. Several indices [either slowing down or speeding up, and increasing errors or reducing errors in congruent and incongruent blocks; Combined Task Slowing (CTS); Ratio 150–10000] have been developed to detect faking. Findings on these are inconclusive, but previous studies have used small samples, suggesting they were statistically underpowered. Further, the stability of the results, the unique predictivity of the indices, the advantage of combining indices, and the dependency on how faking success is computed have yet to be examined. Therefore, we reanalyzed a large data set (N = 750) of fakers and non-fakers who completed an extraversion IAT. Results showed that faking strategies depend on the direction of faking. It was possible to detect faking of low scores due to slowing down on the congruent block, and somewhat less with CTS—both strategies led to faking success. In contrast, the strategy of increasing errors on the congruent block was observed but was not successful in altering the IAT effect in the desired direction. Fakers of high scores could be detected due to slowing down on the incongruent block, increasing errors on the incongruent block, and with CTS—all three strategies led to faking success. The results proved stable in subsamples and generally across different computations of faking success. Using regression analyses and machine learning, increasing errors had the strongest impact on the classification. Apparently, fakers use various goal-dependent strategies and not all are successful. To detect faking, we recommend combining indices depending on the context (and examining convergence).
GND Keywords: IAT; Betrug; Erkennung
Keywords: Implicit Association Test (IAT), Faking detection, Faking strategies, Faking indices, Machine learning
DDC Classification: 150 Psychology  
RVK Classification: CT 5300   
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/58815
Release Date: 14. April 2023
Project: Open-Access-Publikationskosten 2022 - 2024

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