You may remember some time back I wrote a post on interdependence that focused on two different perspectives: dyadic interdependence and network interdependence. Since then there has been a bit more work that blend these two perspectives. I think it’s important to break that down because it may help us get a better picture of the ways in which our friends and families influence our relationship’s quality and lifespan.
To sum up my last post, dyadic interdependence works like this:
Person A and person B have their own life sequences, and interdependence is the extent to which they interrupt (i.e., facilitate or interfere with) each other’s daily goals and scripts1. That’s all well and good, but it’s also worth noting that the way in which we understand interdependence as a couple might spread to the way in which we understand interdependence as a network.
Specifically, and as described in my last post, network interdependence really just describes characteristics of a network: its size, density, reachability, overlap, and clustering2. But as of late, researchers have come to understand that we can actually measure interdependence in networks the same way that we measure it in ccouples3. This is kind of a big deal because interdependence plays a HUGE role in the ways that scientists study relationships. Theories of relationships even describe that interfering and facilitating behaviors from a partner are closely linked to emotional reactions4. In this way, we can literally measure the extent to which a couple’s overlapping interchain sequences affects their relationship.
If we consider the network as a group of people with who we are interdependent, we can measure the influence that they have on our relationship(s) as well. And wouldn’t you know it, the extent to which we believe ourselves to be interdependent with our social network relates to our relationship quality5! Network interdependence and dyadic interdependence, when measured, are very closely related to each other, such that levels of network interdependence at one time affect levels of dyadic interdependence at another time, and vice-versa. Most interesting of all, our network interdependenc