Statistical power of the social network autocorrelation model
| Autor: | Daniel A. Newman, Wei Wang, Eric J. Neuman |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Dynamic network analysis
Sociology and Political Science Social network business.industry Computer science Autocorrelation Social network analysis (criminology) General Social Sciences Statistical power Power (physics) Nonlinear system Anthropology Statistics Econometrics business Network effect General Psychology |
| Zdroj: | Social Networks. 38:88-99 |
| ISSN: | 0378-8733 |
| DOI: | 10.1016/j.socnet.2014.03.004 |
| Popis: | The network autocorrelation model has become an increasingly popular tool for conducting social network analysis. More and more researchers, however, have documented evidence of a systematic negative bias in the estimation of the network effect (ρ). In this paper, we take a different approach to the problem by investigating conditions under which, despite the underestimation bias, a network effect can still be detected by the network autocorrelation model. Using simulations, we find that moderately-sized network effects (e.g., ρ = .3) are still often detectable in modest-sized networks (i.e., 40 or more nodes). Analyses reveal that statistical power is primarily a nonlinear function of network effect size (ρ) and network size (N), although both of these factors can interact with network density and network structure to impair power under certain rare conditions. We conclude by discussing implications of these findings and guidelines for users of the autocorrelation model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect