The Prospective Predictive Power of Parent-Reported Personality Traits and Facets in First-Onset Depression in Adolescent Girls

Author:

Zhong Yiming,Perlman Greg,Klein Daniel N.,Jin Jingwen,Kotov Roman

Abstract

AbstractCertain personality traits and facets are well-known risk factors that predict first-onset depression during adolescence. However, prior research predominantly relied on self-reported data, which has limitations as a source of personality information. Reports from close informants have the potential to increase the predictive power of personality on first-onsets of depression in adolescents. With easy access to adolescents’ behaviors across settings and time, parents may provide important additional information about their children’s personality. The same personality trait(s) and facet(s) rated by selves (mean age 14.4 years old) and biological parents at baseline were used to prospectively predict depression onsets among 442 adolescent girls during a 72-month follow-up. First, bivariate logistic regression was used to examine whether parent-reported personality measures predicted adolescent girls’ depression onsets; then multivariate logistic regression was used to test whether parent reports provided additional predictive power above and beyond self-reports of same trait or facet. Parent-reported personality traits and facets predicted adolescents’ depression onsets, similar to findings using self-reported data. After controlling for the corresponding self-report measures, parent-reported higher openness (at the trait level) and higher depressivity (at the facet-level) incrementally predicted first-onset of depression in the sample. Findings demonstrated additional variance contributed by parent-reported personality measures and validated a multi-informant approach in using personality to prospectively predict onsets of depression in adolescent girls.

Publisher

Springer Science and Business Media LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3