IAT Faking Indices Revisited : Aspects of Replicability and Differential Validity (Poster)






Faculty/Professorship: Personality Psychology and Psychological Assessment  
Author(s): Röhner, Jessica ; Holden, Ronald R.; Schütz, Astrid  
Title of the compilation: Abstracts of the Psychonomic Society
Conference: Psychonomic Society 63rd Annual Meeting, November 17-20, 2022, Boston, Massachusetts, USA
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: 1
Language(s): English
Remark: 
Als Zeitschriftenartikel erschienen in: Behavior research methods : BRM. (2022), Online First. - DOI: 10.3758/s13428-022-01845-0.
Siehe auch FIS-Eintrag: 54527
DOI: 10.20378/irb-56855
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-568552
Abstract: 
Indices [slowing, speeding, increasing or reducing errors in congruent or incongruent blocks; Combined Task Slowing (CTS); Ratio 150-10000] allegedly detect faking in IATs, however, studies on these are inconclusive and statistically underpowered. Also, results’ stability, indices’ unique predictivity, combining indices, and variations in computing faking success remain unexplored. We reanalyzed a large sample (N = 750) completing an extraversion IAT, finding that faking strategies depend on the direction of faking. Successful faking of low scores was detected from slowing down on the congruent block, and less with CTS. Successful faking of high scores was detected from slowing down and increasing errors on the incongruent block, and with CTS. Results demonstrated stability across subsamples and different computations of faking success. Increasing errors had the strongest impact on the classification. Apparently, fakers use goal-dependent strategies which are not all successful. To detect faking, we recommend combining indices and considering the context.
GND Keywords: Implicit Association Test; Manipulation; Testergebnis
Keywords: faking, faking detection, Implicit Association Test (IAT), Replication
DDC Classification: 150 Psychology  
RVK Classification: CS 1500   
Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/56855
Release Date: 10. January 2023

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