Effects of Applicant Faking on Forced-Choice and Likert Scores

2018 ◽  
Vol 22 (3) ◽  
pp. 710-739 ◽  
Author(s):  
Goran Pavlov ◽  
Alberto Maydeu-Olivares ◽  
Amanda J. Fairchild
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher Huber ◽  
Nathan Kuncel ◽  
Katie Huber ◽  
Anthony Boyce

Despite the established validity of personality measures for personnel selection, their susceptibility to faking has been a persistent concern. However, the lack of studies that combine generalizability with experimental control makes it difficult to determine the effects of applicant faking. This study addressed this deficit in two ways. First, we compared a subtle incentive to fake with the explicit “fake-good” instructions used in most faking experiments. Second, we compared standard Likert scales to multidimensional forced choice (MFC) scales designed to resist deception, including more and less fakable versions of the same MFC inventory. MFC scales substantially reduced motivated score elevation but also appeared to elicit selective faking on work-relevant dimensions. Despite reducing the effectiveness of impression management attempts, MFC scales did not retain more validity than Likert scales when participants faked. However, results suggested that faking artificially bolstered the criterion-related validity of Likert scales while diminishing their construct validity.


2021 ◽  
Author(s):  
Goran Pavlov ◽  
Dexin Shi

The forced-choice response format has been proposed as a method for preventing applicant faking on self-report non-cognitive measures. This potential benefit of the format depends on how closely the items comprising each forced-choice block are matched in terms of desirability for the job. Current desirability matching procedures rely on differences in items’ mean desirability ratings to quantify similarity in items’ desirability. We argue that relying on means, while ignoring individual differences in desirability ratings, may yield inaccurate similarity values and result in inferior item matches. As an alternative, we propose a distance-based measure that considers differences in desirability ratings at the individual level and may thus yield accurate similarity values and optimal matches. We support our arguments on a set of desirability ratings obtained with an explicit instruction to rate desirability of items.


2007 ◽  
Author(s):  
Richard L. Griffith ◽  
Tina Malm ◽  
Michael J. Zickar
Keyword(s):  

2006 ◽  
Author(s):  
Richard L. Griffith ◽  
Yukiko Yoshita ◽  
Nicholas L. Vasilopoulos

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