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dc.contributor.advisor Beier, Margaret E.
dc.creatorMcDaniel, Max Julian
dc.date.accessioned 2018-12-03T18:33:05Z
dc.date.available 2018-12-03T18:33:05Z
dc.date.issued 2009
dc.identifier.urihttps://hdl.handle.net/1911/103722
dc.description.abstract The current research examined behavior prediction of implicit and explicit measures of personality. Specifically, I examined the role of cognitive load and information processing in the prediction of behavior by implicit and explicit self-concept personality measures. Undergraduate students, N=83, completed self-report (explicit) and Implicit Association Test (implicit) measures of two Big Five factors (extraversion and conscientiousness), and their personality-relevant behaviors were coded in multiple work-related tasks, including video-recorded telephone interviews. Participants completed all tasks in single-task and dual-task conditions. Results did not provide support for a dual process model of personality self-concept which posits that implicit measures are better predictors of behavior under conditions of cognitive load, and self-report measures are better predictors of behavior under conditions of low cognitive load. The limitations of the current study are discussed. Overall, the results did provide some evidence of the validity of implicit measures in the personality domain.
dc.format.extent 104 pp
dc.language.iso eng
dc.subjectOccupational psychology
Personality psychology
Psychology
Behavior prediction
Big Five Dual process
Implicit Association Test
Implicit personality
Personality
dc.title Dual process model of personality: Implications for prediction of behavior
dc.identifier.digital 750350233
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Psychology
thesis.degree.discipline Social Sciences
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.callno THESIS PSYCH. 2010 MCDANIEL
dc.identifier.citation McDaniel, Max Julian. "Dual process model of personality: Implications for prediction of behavior." (2009) Diss., Rice University. https://hdl.handle.net/1911/103722.


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