Dyad vs. network effects: Modeling relationships in personal networks using contextual effects |
| |
| Institution: | 1. Department of Psychology, University of Connecticut, Storrs, CT, USA;2. Carman and Ann Adams Department of Pediatrics, Prevention Research Center, Wayne State University School of Medicine, Detroit, Michigan, USA;3. Guangxi Center for Diseases Control and Prevention, Nanning, Guangxi, China;4. Department of Allied Health Sciences, University of Connecticut, Storrs, CT, USA;1. University of Texas, Permian Basin, 4901 East University, Odessa, TX 79762, USA;2. University of Münich and CESifo, Shackstrasse 4, 80539 Münich, Germany;1. Department of Sports Medicine and Vascular Investigations, University Hospital, Angers, France;2. Ecole supérieure d''électronique de l''Ouest, Institute of Science & Technology, Angers, France;3. LAUM - UMR CNR6613, Angers, France;4. Department of Vascular and Thoracic Surgery, University Hospital, Angers, France;5. UMR INSERM 1083/CNRS 6214, Angers University, Angers, France;1. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan;2. JST, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan;3. Department of Mathematics and naXys, University of Namur, 5000 Namur, Belgium |
| |
| Abstract: | This paper proposes using contextual models to disentangle the effects of dyad characteristics from the effects of characteristics of the networks in which they reside. Multilevel models that nest dyads in personal networks can be coded for contextual analysis by entering both the dyad value of a predictor and the network mean of that predictor into the prediction equation. These models can then be used to measure a within-network effect for dyads and a network contextual effect. This paper conducts an example analysis of how dyad redundancy, and the network's average dyad redundancy, impact discussions of job opportunities. The findings suggest that the dyad and network effects of redundancy are in opposite directions: redundancy has a positive effect at the dyad level and a negative effect at the network level when predicting number of jobs discussed. These results support the major social capital tenets of closure and brokerage, respectively. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
