Testing relational turbulence theory in daily life using dynamic structural equation modeling

被引:2
|
作者
Goodboy, Alan K. [1 ]
Dillow, Megan R. [1 ]
Shin, Matt [1 ]
Chiasson, Rebekah M. [1 ]
Zyphur, Michael J. [2 ]
机构
[1] West Virginia Univ, Dept Commun Studies, Morgantown, WV 26506 USA
[2] Univ Queensland, UQ Business Sch, St Lucia, Qld, Australia
关键词
Relational turbulence theory; daily life; dynamic structural equation modeling; time series; intensive longitudinal methods; EXPLAINING VARIATION; MULTILEVEL; INTERDEPENDENCE; COMMUNICATION; EMOTION; INERTIA;
D O I
10.1093/joc/jqae010
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
Using dynamic structural equation modeling (DSEM; ), this study tests how partner disruptions of daily routines create a chaotic relational state through intensified emotions directed at partners, as posited by relational turbulence theory (RTT; ). To test this affective process, individuals in dating relationships (N = 130) completed daily surveys for 30 days (T = 30; 3,478 total observations), measuring that day's interference from their partner, anger experienced while interacting with their partner, and their relational turbulence. DSEM accounted for the intensive longitudinal aspects of the data while modeling three types of person-specific random effects: random intercepts to account for subject-specific averages; random slopes to account for subject-specific effects; and random variances to account for subject-specific volatility. RTT processes were supported, as greater than typical interference of routines in daily life predicted more relational turbulence that day via increased daily anger (controlling for the previous day's levels). The use of DSEM allowed us to further test RTT by modeling person-specific inertia and volatility (for levels of interference, anger, and relational turbulence throughout a month). The use of a multilevel "location-scale" DSEM with random intercepts and random variances revealed that attachment avoidance and anxiety predicted a variety of person-specific features of the studied longitudinal processes: averages, inertia, and volatility over time. We provide our data and a supplemental primer to illustrate how to test communication theory with DSEM and model the intensive dynamics of daily life.
引用
收藏
页码:249 / 264
页数:16
相关论文
共 50 条