1. Acculturation revisited A model of personal network change José Luis Molina Universitat Autònoma de Barcelona Miranda J. Lubbers Universitat Autònoma de Barcelona Chris McCarty University of Florida National Science Foundation - BCS-0417429

2. 2 Acculturation... We are interested in Acculturation: the consequence of two cultures coming into contact. … And we think that personal networks can help us to understand this process … For our example we will look at migrants of Africa and South America moving to Spain

3. 3 Samples of first and second generation immigrants

4. 4 Sociocentric and egocentric networks … A sociocentric network refers to the pattern of relations within a defined (bounded) group. These could be a corporate office, a classroom of children, a church... An egocentric network is the subset of ties surrounding a given ego within the sociocentric network. So within a corporate office you might want to compare the characteristics of the networks of two staff members. So, sociocentric and egocentric networks refer to a single social setting.

5. 5 Personal Networks A Personal Network is the unconstrained set of network members that surround a person. Personal networks represent all of the sociocentric networks that a person belongs to (their family, work, clubs, church, etc.). Typically we use one or more Name Generators to get respondents to list alters, AND a Tie Definition for connecting her alters. If the list of alters is long enough (30 or more on average) most social settings in which ego participates will be represented (kin, friends, coworkers …).

6. 6 East York … (Wellman, 1999)

7. 7 Research questions Do the structure and composition of the personal networks of migrants vary with their years of residence in Spain? If so... What are the trends of those changes?

8. 8 Hypothesis (Spanish data) Three stages of acculturation 1: one dense cluster, largely consisting of alters from the country of origin. 2: multiple clusters, some primarily from Spain, some from country of origin, high betweenness. 3: the multiple clusters from stage 2 become interconnected and form 1 loosely connected cluster.

9. 9 Method For each personal network (excluding ego), we calculated structural and compositional characteristics ´Meta-analysis´ over 250 networks for which we had complete data about composition and structure Bivariate correlations between years of residence and various network characteristics K-means cluster analysis of various network characteristics (see slide 13), to identify homogeneous groups of networks ANOVA to relate cluster membership to years of residence

10. 10 Results: Significant bivariate correlations (p <.05) with years of residence Percentage of alters who are originally from Spain r =.36 Percentage of alters who live in Spainr =.33 Average age of altersr =.25 Average number of years ego knows altersr =.22 SD number of years ego knows altersr =.18 Diversity of rolesr =.13 Average closenessr = -.13

11. 11 Results: Significant bivariate correlations with years of residence.. age effects Percentage of alters who are originally from Spain r =.36 Percentage of alters who live in Spainr =.33 Average age of altersr =.25 Average number of years ego knows altersr =.22 SD number of years ego knows altersr =.18 Diversity of rolesr =.13 Average closenessr = -.13

12. 12 K-means cluster analysis Based on the variables (all standardized) : Proportion of alters whose country of origin is Spain Proportion of alters who live in Spain Number of clusters within network Cluster homogeneity regarding living in Spain Density Network betweenness centralization Average frequency of contact (scale 1-7) Average closeness (scale 1-5) Diversity of roles (scale 1-13)

13. 13 Results cluster analysis Four-cluster solution was best interpretable Characteristics that most contributed to the cluster partition: Number of clusters, percentage of alters living in Spain, density, and number of alters originally from Spain. Cluster sizes: Cluster 1: N = 41 Cluster 2: N = 86 Cluster 3: N = 33 Cluster 4: N = 90

14. 14 Cluster characteristics (a) (unstandardized)

15. 15 Cluster characteristics (b) (unstandardized)

16. 16 Cluster characteristics (c) (unstandardized)

17. 17 Summary of characteristics cluster 1 (N = 41) On average 1 quite homogeneous cluster High density & low betweenness Low percentages of alters who are originally from Spain and who live in Spain Relatively low diversity of roles

18. 18 Meet our representative of cluster 1, Senegalese, 1 year in Spain...

19. 19 Meet our representative of cluster 1, Senegalese, 1 year in Spain...

20. 20 Summary of characteristics cluster 2 (N = 86) On average 1 or 2 rather heterogeneous clusters Low density & high betweenness Somewhat higher percentages of alters who are originally from Spain and who live in Spain than those in cluster 1

21. 21 Our second representative (cl.2), Dominican, 4 years in Spain...

22. 22 Summary of characteristics cluster 3 (N = 33) High number of quite homogeneous clusters (4 to 5 on average). The whole network is more heterogeneous with respect to alters´ country of origin and alters´ country of living. Very low density.

23. 23 Representative of cluster 3, from Argentina, also 4 years in Spain

24. 24 Summary of characteristics cluster 4 (N = 90) On average 1 to 2 rather homogeneous clusters High betweenness Relatively high average frequency of contact Highest percentages of alters who are originally from and live in Spain

25. 25 Representative of cluster 4, Moroccan, 14 years in Spain

26. 26 Is partition related to years of residence? ANOVA: F (3, 229)= 4,932 p =.002 Post hoc tests Cl. 2 & 3 n.s. Cl. 3 & 4 n.s.

27. 27 Is years of residence a predictor of cluster membership? Multinominal logistic regression YES; years of residence predicts cluster membership Sex and employment status did not have a significant effect (and neither did age) Country of origin, however, influenced cluster membership significantly: e.g., Senegambians had a higher probability to be in cluster 1 than the others

28. 28 How do personal networks change over time? Cluster 1 Cluster 2 Cluster 3 Cluster 4 Years of residence

29. 29 One option would be... Cluster 1 Cluster 2 Cluster 3 Cluster 4 Years of residence

30. 30 Need for a longitudinal model To investigate whether there are different trajectories of network change, depending on (e.g.) culture and entry situation At the disaggregated level: investigate which alter characteristics (such as centrality), ego-alter characteristics (such as closeness to ego, role in ego’s network), or alter-alter characteristics (such as similarity country of origin) predict future edges

31. 31 Longitudinal study... The ECRP Project (Dynamics of actors and networks across levels: individuals, groups, organizations and social settings) will allow us to perform two more waves to a selection of informants from each cluster and study the evolution of their personal networks in order to test the model … … and gain a better understanding of the sources of change in personal networks, beliefs and behaviors.

32. 32 Thanks !