Abstract
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.
Committee Chair
Joshua J. Jackson
Committee Members
Lee Konczak, Randy Larsen, Thomas F. Oltmanns, Michael Strube
Degree
Doctor of Philosophy (PhD)
Author's Department
Psychology
Document Type
Dissertation
Date of Award
Winter 12-15-2016
Language
English (en)
DOI
https://doi.org/10.7936/K72Z13ZP
Recommended Citation
Mike, Anissa, "What do you and I know? Disentangling self- and other-perspectives of personality traits using bifactor models" (2016). Arts & Sciences Theses and Dissertations. 1000.
The definitive version is available at https://doi.org/10.7936/K72Z13ZP
Comments
Permanent URL: https://doi.org/10.7936/K72Z13ZP