Date of Award
Winter 12-15-2016
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
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.
Language
English (en)
Chair and Committee
Joshua J. Jackson
Committee Members
Lee Konczak, Randy Larsen, Thomas F. Oltmanns, Michael Strube
Recommended Citation
Mike, Anissa, "What do you and I know? Disentangling self- and other-perspectives of personality traits using bifactor models" (2016). Arts & Sciences Electronic Theses and Dissertations. 1000.
https://openscholarship.wustl.edu/art_sci_etds/1000
Comments
Permanent URL: https://doi.org/10.7936/K72Z13ZP