the local ties of the larger networks, however, tend to involve acquaintances tied only by interactions, instead of relations in Degenne (2009)’s sense. It is interesting to add that the above mentioned countrymen tend to be local (and have negative sign, as we saw), but the effect of local contacts is so strong that survives as a distinct and positive effect. Varied sociability Although varied sociability contributes to larger networks, it involves a different group of processes and is explained by different variables. I performed a similar test with linear regression of several variables to explain the total number of spheres as measure of varied sociability. The results are presented in the following table. As we can see, eight variables survived in the model. Table 2. Linear regression coefficients - # Spheres Unstandardized Standardized Coefficients Coefficients Std. B Error Beta t Sig. (Constant) 2,940 ,213 13,822 ,000 São Paulo ,266 ,106 ,116 2,510 ,013 Years of schooling ,053 ,013 ,182 3,914 ,000 Nodes ,018 ,002 ,453 7,829 ,000 # 2-clans/total -,910 ,307 -,173 -2,968 ,003 number of nodes Sphere church (%) ,020 ,005 ,172 3,866 ,000 Sphere work (%) ,016 ,004 ,166 3,569 ,000 persons (%) -,005 ,002 -,112 -2,442 ,015 Segregated -,203 ,103 -,089 -1,976 ,049 Notes: Adj R2=0.323; N=358 cases (three outliers); Durbin Watson=2.16; Condition Index= 12.40. In this case, city differences did not disappear in the multivariate model and the São Paulo cases present systematically higher variability of sociability, even in face of the other variables. Years of schooling appear again with a positive and significant effect, increasing the probability of more varied networks for more schooled individuals. The number of nodes appears as positive and significant, what was expected considering our interpretation of the results of the previous regression. Another variable which also appears again is
the local ties of the larger networks, however, tend to involve acquaintances tied only by interactions, instead of relations in Degenne (2009)’s sense. It is interesting to add that the above mentioned countrymen tend to be local (and have negative sign, as we saw), but the effect of local contacts is so strong that survives as a distinct and positive effect. Varied sociability Although varied sociability contributes to larger networks, it involves a different group of processes and is explained by different variables. I performed a similar test with linear regression of several variables to explain the total number of spheres as measure of varied sociability. The results are presented in the following table. As we can see, eight variables survived in the model. Table 2. Linear regression coefficients - # Spheres Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 2,940 ,213 13,822 ,000 São Paulo ,266 ,106 ,116 2,510 ,013 Years of schooling ,053 ,013 ,182 3,914 ,000 Nodes ,018 ,002 ,453 7,829 ,000 # 2-clans/total number of nodes -,910 ,307 -,173 -2,968 ,003 Sphere church (%) ,020 ,005 ,172 3,866 ,000 Sphere work (%) ,016 ,004 ,166 3,569 ,000 persons (%) -,005 ,002 -,112 -2,442 ,015 Segregated -,203 ,103 -,089 -1,976 ,049 Notes: Adj R2=0.323; N=358 cases (three outliers); Durbin Watson=2.16; Condition Index= 12.40. In this case, city differences did not disappear in the multivariate model and the São Paulo cases present systematically higher variability of sociability, even in face of the other variables. Years of schooling appear again with a positive and significant effect, increasing the probability of more varied networks for more schooled individuals. The number of nodes appears as positive and significant, what was expected considering our interpretation of the results of the previous regression. Another variable which also appears again is
1-1-Marques.docx