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In order to examine the role of the shaping factors on the composition
of personal networks, we ran six linear regression models on the network
size, proportion of kin, proportion of co-residence alters, proportion of
female alters, proportion of very recent acquaintances, and proportion of
very old acquaintances. As mentioned earlier, each model is composed of
three blocks: (a) country; (b) structural factors (birth-cohort, gender, and
education); (c) normative factors (attitudinal indexes of child- centredness
and openness to alternative family forms).
Model 1 in Table 3.6 shows the predictors of network size. First, coun-
try is a strong predictor of network size as its effect remains significant
while accounting for the structural and normative factors. In Model 1a,
we can see that those individuals who were born in Switzerland and
Lithuania show smaller networks than those born in Portugal. This first
association confirms the size variations across countries that were found
in the previous section. Model 1b shows the role of gender and educa-
tion. Being a woman and having higher levels of education is associated
with greater network size. Women often play the role of kin-keepers, as
they are more actively engaged in sociability practices and network
exchanges with relatives and friends than men. Also individuals with
higher levels of education are more likely to belong to more diversified
circles of interaction, accumulating more network members over their
educational and work trajectories. Also being open to new family forms
is associated with larger networks (Model 1c). This association demon-
strates how the openness towards diversity regarding the ways of organiz-
ing family life beyond more institutionalized scripts is reflected in the
openness of the boundaries to a higher number of ties.
Model 2 in Table 3.6 shows the main influences associated with the
proportion of kin in personal networks. Again, the country of origin is
strongly associated with the salience of kinship ties: the Swiss networks
show a lower proportion of kin compared to the Portuguese networks;
and Lithuanian networks show a higher proportion of kinship ties than
the Portuguese (Model 2a). Regarding the impact of the structural fac-
tors: those with a lower level of education show a higher proportion of
kinship ties; while those with a higher level of education show a lower
proportion of kinship ties (Model 2b). Again, the participation in more
diversified life-spheres and circles of interaction among those with higher
R. Gouveia et al.