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This paper discusses and illustrates various approaches for the longitudinal analysis of personal networks (multilevel analysis, regression analysis, and SIENA). We combined the different types of analyses in a study of the changing personal networks of immigrants. Data were obtained from 25 Argentineans in Spain, who were interviewed twice in a two-year interval. Qualitative interviews were used to estimate the amount of measurement error and to isolate important predictors. Quantitative analyses showed that the persistence of ties was explained by tie strength, network density, and alters' country of origin and residence. Furthermore, transitivity appeared to be an important tendency, both for acquiring new contacts and for the relationships among alters. At the network level, immigrants' networks were remarkably stable in composition and structure despite the high turnover. Clustered graphs have been used to illustrate the results. The results are discussed in light of adaptation to the host society.

Estimating sizes of hidden or hard-to-reach populations is an important problem in public health. For example, estimates of the sizes of populations at highest risk for HIV and AIDS are needed for designing, evaluating and allocating funding for treatment and prevention programmes. A promising approach to size estimation, relatively new to public health, is the network scale-up method (NSUM), involving two steps: estimating the personal network size of the members of a random sample of a total population and, with this information, estimating the number of members of a hidden subpopulation of the total population. We describe the method, including two approaches to estimating personal network sizes (summation and known population). We discuss the strengths and weaknesses of each approach and provide examples of international applications of the NSUM in public health. We conclude with recommendations for future research and evaluation.

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The authors have developed and tested scale-up methods, based on a simple social network theory, to estimate the size of hard-to-count subpopulations. The authors asked a nationally representative sample of respondents how many people they knew in a list of 32 subpopulations, including 29 subpopulations of known size and 3 of unknown size. Using these responses, the authors produced an effectively unbiased maximum likelihood estimate of the number of people each respondent knows. These estimates were then used to back-estimate the size of the three populations of unknown size. Maximum likelihood values and 95% confidence intervals are found for seroprevalence, 800,000 +/- 43,000; for homeless, 526,000 +/- 35,000; and for women raped in the last 12 months, 194,000 +/- 21,000. The estimate for seroprevalence agrees strikingly with medical estimates, the homeless estimate is well within the published estimates, and the authors' estimate lies in the middle of the published range for rape victims.

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