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.

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. AbstractThis paper describes an empirical comparison of four specifications of the exponential family of random graph models (ERGM), distinguished by model specification (dyadic independence, Markov, partial conditional dependence) and, for the Markov model, by estimation method (Maximum Pseudolikelihood, Maximum Likelihood). This was done by reanalyzing 102 student networks in 57 junior high school classes. At the level of all classes combined, earlier substantive conclusions were supported by all specifications. However, the different specifications led to different conclusions for individual classes. PL produced unreliable estimates (when ML is regarded as the standard) and had more convergence problems than ML. Furthermore, the estimates of covariate effects were affected considerably by controlling for network structure, although the precise specification of the structural part (Markov or partial conditional dependence) mattered less.

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This article investigates whether personal networks influenced ethnic self-identifications of migrants in Spain. During the years 2004-6, data were collected about personal networks of migrants in Spain (N = 294) through a questionnaire and a structured interview. The networks were classified into five network profiles on the basis of both variables about structure (e.g. density, betweenness and number of cohesive subgroups) and composition (e.g. country of origin and percentage of family members). Profiles were related to the different ways in which migrants identified themselves. Personal networks in which network members, mostly family and people from the country of origin, formed one dense cluster were associated with ethnic exclusive self-identifications, whereas more heterogeneous personal networks tended to exhibit more plural definitions of belonging. The results show that both individual and network characteristics contribute to an understanding of ethnic self-identification.

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We propose a method to visually summarize collections of networks on which a clustering of the vertices is given. Our method allows for efficient comparison of individual networks, as well as for visualizing the average composition and structure of a set of networks. As a concrete application we analyze a set of several hundred personal networks of migrants. On the individual level the network images provide visual hints for assessing the mode of acculturation of the respondent. On the population level they show how cultural integration varies with specific characteristics of the migrants such as country of origin, years of residence, or skin color.

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