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Drug abuse and HIV prevention research: Expanding paradigms and network contributions to risk reduction
 Connections
, 1995
"... This paper identifies an important paradigm shift in social research on HIV transmission, drug abuse, and risk reduction research. The article describes the key research trends and the institutional support for social network analysis in the HIV and drug risk field for the past decade. Key hypothese ..."
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This paper identifies an important paradigm shift in social research on HIV transmission, drug abuse, and risk reduction research. The article describes the key research trends and the institutional support for social network analysis in the HIV and drug risk field for the past decade. Key hypotheses and recommended areas for future research are identified.
CONNECTIONS 18(1):10410 ©1995 INSNA Commentary: Sampling in Social Networks
"... In classic statistical theory, if a random sample is drawn from a population whose underlying distribution is known, it may be assumed that the properties of the sample mirror those of the population (Snedecor and Cochran, 1972). On that cornerstone is built a statistical superstructure that permits ..."
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In classic statistical theory, if a random sample is drawn from a population whose underlying distribution is known, it may be assumed that the properties of the sample mirror those of the population (Snedecor and Cochran, 1972). On that cornerstone is built a statistical superstructure that permits estimation, hypothesis testing, assurance of internal validity, generalizability, and modeling. For a variety of actual sampling schemes — simple random, stratified, probability proportional to size, systematic, cluster, multistage — considerable mathematical work has established appropriate point estimate and variance formulas, and has defined the potential for bias and other threats to validity (Levy and Lemeshow, 1980). This body of work provides satisfying precision for the estimation of uncertainty in defining population characteristics. Random Graphs In the field of network analysis, sampling theory has been associated with defining the mathematical properties of random graphs. Though others preceded them, Erdos and Renyi (1959, 1960) are credited with establishing the theoretical base for estimation of such properties. During the past several decades considerable effort has been invested in describing graphs, and many familiar properties of social network have been established for random graphs. Investigators have explored the mean and variance of degree in a graph (Frank, 1980; Rapoport, 1979a); the probability that a graph will be connected (Gilbert, 1959); the distribution of connected components in a graph (Frank, 1978a; Ling, 1975; Naus and Rabinowitz, 1975); and general types of estimation in large graphs under various sampling schemes (Frank, 1980, 1981, 1978b) One specific type of network investigation — snowball sampling (Goodman, 1961) —