Merz Erin L, Roesch Scott C
SDSU/UCSD Joint Doctoral Program in Clinical Psychology, 6363 Alvarado Court, Suite 103, San Diego, CA 92120-4913.
J Res Pers. 2011 Feb 1;45(1):2-9. doi: 10.1016/j.jrp.2010.11.003.
This study used daily diary data to model trait and state Positive Affect (PA) and Negative Affect (NA) using the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). Data were collected from 364 college students over five days. Intraclass correlation coefficients suggested approximately equal amounts of variability at the trait and state levels. Multilevel factor analysis revealed that the model specifying two correlated factors (PA, NA) and correlated uniqueness terms among redundant items provided the best fit. Trait and state PA and NA were generally associated with stress, anxiety, depression, and three types of self-esteem (performance, academic, social). The coefficients describing these relationships differed somewhat, suggesting that trait and state measurement may have different predictive utility.
本研究使用每日日记数据，采用积极和消极情绪量表（PANAS；Watson、Clark和Tellegen，1988）对特质性和状态性积极情绪（PA）及消极情绪（NA）进行建模。数据是在五天时间里从364名大学生中收集的。组内相关系数表明，特质水平和状态水平的变异性大致相等。多水平因素分析显示，指定两个相关因素（PA、NA）以及冗余项目间相关独特项的模型拟合度最佳。特质性和状态性PA及NA通常与压力、焦虑、抑郁以及三种自尊类型（表现、学业、社交）相关。描述这些关系的系数略有不同，这表明特质测量和状态测量可能具有不同的预测效用。
