Exploring the Predictive and Theoretical Validity of the Network Interdependence Measure

ABSTRACT

This study used measures of social network interference and facilitation to add to the heuristic value of relational turbulence theory. This manuscript advances theory and the value of network interdependence measures by relating them to measures of partner interdependence, as well as measures of negative emotions. Longitudinal results revealed that time 1 network interference predicted time 2 partner interference. Moreover, time 1 partner facilitation predicted time 2 network facilitation. Competing mediated models revealed that time 2 partner interference mediates the relationship between time 1 network interference and time 2 negative emotions. Time 2 network interference partially mediated the relationship between time 1 partner interference and time 2 negative emotions. Results are discussed in terms of theoretical, conceptual, and statistical value.

KEYWORDS:

• Interdependence
• social networks
• relational turbulence theory

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. A MANOVA concluded that responses did not statistically differ between participants who completed only time 1 questions and participants who completed time 1 and time 2 questions. A separate MANOVA showed nonsignificant results across relationship types for these same outcome variables. (η² < .02 in all cases).

2. Prior to answering social network related questions, participants were provided with a description of a social network as “people who you generally like and are close with, and who you spend time with regularly.”

3. Prior to analyses, it was revealed that length of relationship correlated significantly with nearly every variable in this study. For this reason, relationship length was controlled for in all substantive analyses.

4. Note. After consultation of modification indices, two covariations were drawn between items in the latent variable negative emotion. One item was removed from this variable to decrease multicollinearity. These modifications also apply to the results of model 4.