Date of Award

Winter 12-15-2016

Author's School

Graduate School of Arts and Sciences

Author's Department


Degree Name

Doctor of Philosophy (PhD)

Degree Type



Research is increasingly moving towards utilizing reports of personality from multiple sources to obtain a more comprehensive understanding of personality. However, it remains unclear how we might best utilize reports of personality from multiple sources. Simply aggregating self- and other-reports ignores their unique perspectives. Conversely, using self- and informant-reports separately excludes examination of self-other agreement and misses an opportunity to increase reliability of reports. The current paper presents a statistical method — structural equation bifactor models — that combines what self- and other-reports jointly know while at the same time preserving their unique views, allowing for each (self-, other-, and their joint-perspective) to predict outcomes. We then examine the predictive validity of each perspective for each Big Five trait across three independent studies examining social adjustment, community involvement, emotional well-being, and health as outcomes across studies. The joint- perspective proved most frequently predictive across outcomes and traits, demonstrating the increased reliability and robustness or combined reports. Self-reports also added predictive validity beyond the joint-perspective, indicating that self-knowledge also provides important information pertaining to life outcomes. Finally, the other-reports were predictive of select outcomes, suggesting that others do add important information, but that their predictive validity is more limited than that of the joint or self-perspectives.


English (en)

Chair and Committee

Joshua J. Jackson

Committee Members

Lee Konczak, Randy Larsen, Thomas F. Oltmanns, Michael Strube


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Available for download on Tuesday, December 15, 2116