DESCRIPTION (provided by applicant): This application proposes to develop three key components in social network methodology: (a) the assessment of the sensitivity of network statistics and model parameters to various types of missing data under different sampling schemes, (b) develops the first notions of effect sizes and power analysis for social network methodology, and (c) cost-effective sampling schemes that maximize the "return on investment" when network researchers are faced with a finite resource pool. These developments are accomplished through a series of modern simulations while being coupled with advanced combinatorial data analytic approaches. All advances are made freely available in a user-friendly software package.

PUBLIC HEALTH RELEVANCE: This application develops a foundation for assessing the quality of a sampling scheme for social network analysis to determine if study proposals will have adequate power (and/or sample size) to detect varying effects. Additional consideration is given to augment standard sampling schemes in such a manner as to identify the most informative areas of a network, insuring that funding mechanisms are more likely to exhibit a return on investment.