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Incorporating Social Network Variables into Relational Turbulence Theory: Popping the Dyadic Bubble

Abstract Relational turbulence theory (RTT) has primarily explored the effects of relational uncertainty and partner interdependence on relational outcomes. While robust, the theory fails to account for uncertainties and perceived interdependence stemming from extra-dyadic factors (such as partners’ social networks). Thus, this dissertation had two primary goals. First, scales indexing measures of social network-based relational uncertainty (i.e., network uncertainty) and social network interdependence are tested for convergent and divergent validity. Second, measurements of network uncertainty and interdependence are tested alongside measures featured in RTT to explore predictive validity. Results confirmed both measurements and demonstrated numero... (more)
Created Date 2018
Contributor Stein, James B. (Author) / Mongeau, Paul A. (Advisor) / Guerrero, Laura (Committee member) / Dumka, Larry (Committee member) / Arizona State University (Publisher)
Subject Communication / social networks / structural equation modeling / turbulence / uncertainty
Type Doctoral Dissertation
Extent 163 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Communication Studies 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis