Wilt Joshua, Revelle William
Case Western Reserve University.
Northwestern University.
Pers Individ Dif. 2019 Jan 1;136:140-147. doi: 10.1016/j.paid.2017.12.032. Epub 2017 Dec 29.
Prior research shows that personality traits predict time spent with different people and frequency of engagement in different activities. Further, personality traits, company, and activity are related to the experience of affect. However, little research has examined personality, context, and affect together in the same study. In the current study, 78 people described their Big Five traits and took part in a 1-week experience sampling study using mobile phones as a means for data collection. Participants indicated their current company, activity, and momentary affect along the dimensions of energetic arousal (EA), tense arousal (TA), and hedonic tone (HT). Poisson regressions revealed that traits predicted higher frequencies of trait-consistent contexts: for example, extraversion was related to more frequently being with various types of company. Results predicting contexts from multilevel logistic regressions were sparser. Multilevel models revealed that traits and contexts had main effects on affect, yet there were relatively few interactions of traits X contexts predicting affect. We discuss more specific implications of these findings.
先前的研究表明,人格特质能够预测与不同人群相处的时间以及参与不同活动的频率。此外,人格特质、陪伴对象和活动与情感体验相关。然而,很少有研究在同一研究中同时考察人格、情境和情感。在当前的研究中,78人描述了他们的大五人格特质,并参与了一项为期1周的经验取样研究,该研究使用手机作为数据收集工具。参与者报告了他们当前的陪伴对象、活动以及在精力唤醒(EA)、紧张唤醒(TA)和享乐基调(HT)维度上的即时情感。泊松回归分析显示,特质能够预测特质一致情境的更高频率:例如,外向性与更频繁地与各类陪伴对象相处相关。多层逻辑回归分析预测情境的结果则较为有限。多层模型显示,特质和情境对情感有主效应,但特质与情境预测情感的交互作用相对较少。我们将讨论这些发现的更具体含义。