Alan K. Goodboy, Megan R. Dillow, Matt Shin, Rebekah M. Chiasson, Michael J. Zyphur
{"title":"利用动态结构方程模型检验日常生活中的关系湍流理论","authors":"Alan K. Goodboy, Megan R. Dillow, Matt Shin, Rebekah M. Chiasson, Michael J. Zyphur","doi":"10.1093/joc/jqae010","DOIUrl":null,"url":null,"abstract":"\n Using dynamic structural equation modeling (DSEM; Asparouhov et al., 2018), 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; Solomon et al., 2016). 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.","PeriodicalId":48410,"journal":{"name":"Journal of Communication","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Testing relational turbulence theory in daily life using dynamic structural equation modeling\",\"authors\":\"Alan K. Goodboy, Megan R. Dillow, Matt Shin, Rebekah M. Chiasson, Michael J. Zyphur\",\"doi\":\"10.1093/joc/jqae010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Using dynamic structural equation modeling (DSEM; Asparouhov et al., 2018), 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; Solomon et al., 2016). 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.\",\"PeriodicalId\":48410,\"journal\":{\"name\":\"Journal of Communication\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2024-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Communication\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1093/joc/jqae010\",\"RegionNum\":1,\"RegionCategory\":\"文学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMMUNICATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Communication","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1093/joc/jqae010","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
Testing relational turbulence theory in daily life using dynamic structural equation modeling
Using dynamic structural equation modeling (DSEM; Asparouhov et al., 2018), 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; Solomon et al., 2016). 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.
期刊介绍:
The Journal of Communication, the flagship journal of the International Communication Association, is a vital publication for communication specialists and policymakers alike. Focusing on communication research, practice, policy, and theory, it delivers the latest and most significant findings in communication studies. The journal also includes an extensive book review section and symposia of selected studies on current issues. JoC publishes top-quality scholarship on all aspects of communication, with a particular interest in research that transcends disciplinary and sub-field boundaries.