Date of Award

Spring 2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Psychology

Committee Director

Richard N. Landers

Committee Member

Xiaoxiao Hu

Committee Member

Ryan Klinger

Abstract

Unproctored internet testing (UIT) is used widely to administer employment tests (Fallaw, Solomonson, & McClelland, 2009), although cognitively loaded tests delivered by UIT are suspected to offer test takers greater opportunities to cheat and increase the risk of test taker cheating (Chapman & Webster, 2003; Tippins et al., 2006; Tippins, 2009). Despite the wide use and suspected cheating concerns, there is a dearth of research investigating cheating on cognitively loaded UITs (Naglieri et al., 2004; Beaty et al., 2011). Based on the lack of theoretically-grounded empirical studies, the current study had two goals: (1) identify which cheating methods are used by test takers to effectively raise test scores and (2) investigate the roles of general cognitive ability and effective cheating methods in raising test scores. To test the specific hypotheses, 340 adult participants recruited from Amazon MTurk completed a UIT used for employee selection first under honest conditions and then under cheating conditions. Results indicated that not all test takers were able to increase their scores by cheating; cheating effectiveness depended upon the interaction between cognitive ability and the use of effective cheating methods. These results suggest that increased cognitive ability may lead to increased cheating effectiveness on selection tests, but that score change is contingent on applicant awareness of appropriate cheating methods for those tests.

ISBN

9780355884104

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