特质
心理学
情感(语言学)
多级模型
感觉
优势(遗传学)
发展心理学
特质理论
社会心理学
临床心理学
人格
五大性格特征
统计
沟通
数学
基因
生物化学
化学
程序设计语言
计算机科学
作者
Korena S. Klimczak,Sarah Schwartz,Marissa L. Donahue,Leila K. Capel,Janice Snow,Michael E. Levin
标识
DOI:10.1016/j.jcbs.2023.05.006
摘要
An individual's trait-like thoughts, feelings, and behaviors are characteristic patterns that occur across time, whereas state-like iterations of these variables are isolated to specific moments in time. Although highly correlated, variables at the trait and state levels measure different phenomena and should be examined separately. In this longitudinal study, we examine the disaggregation of trait and state-level psychological inflexibility among college students. Specifically, we investigated which psychological inflexibility subprocess would significantly predict positive affect, negative affect, and meaningful activity, both at the trait and state-levels. In addition to pre- and post-assessments, participants (n = 168) completed ecological momentary assessment (EMA) surveys (n = 2251) assessing each of these variables via text message three times per day over the course of a week. Results suggested that while a greater number of state-like subprocesses significantly predict negative affect, positive affect, and meaningful activity, trait-like subprocesses hold more weight. Dominance analyses showed trait-level inaction to be the most important predictor for positive and negative affect, and trait-level of lack of contact with values to be the most important predictor for meaningful activity. Differentiating trait and state variables can enable contextual behavioral scientists to better understand pathological and therapeutic processes of change.
科研通智能强力驱动
Strongly Powered by AbleSci AI