a multilevel path analysis of social networks and social interaction in the neighbourhood pauline...
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A multilevel path analysis of social networks and social interaction in the neighbourhood
Pauline van den Berg
Harry Timmermans
Introduction
• Neighbourhood-based social contacts
• Social network literature: declining role of neighbourhoods
• Urban planning literature: increasing attention (urban renewal policies)
• Empirical findings are scarce and inconclusive
Introduction
Neighbourhood-based social contacts
Socio-demographic characteristics
Neighbourhood characteristics
Structure
Data collection
Descriptives
Path analysis results
Data collection
• Quality of life questionnaire• May 2011• In 70 neighbourhoods in Eindhoven
Personal approach
• 751 completed questionnaires
Sample
Mean St. dev.
Age 47.11 16.86
Full time work: >36 hours (dummy) 0.23 0.42
No work (dummy) 0.42 0.49
Low income: < € 1000,- per month after tax (dummy) 0.11 0.32
High income > € 3000,- per month after tax (dummy) 0.29 0.46
Low education: primary (dummy) 0.40 0.49
High education: BSc or higher (dummy) 0.07 0.26
Child(ren) under 18 in household (dummy) 0.38 0.49
Club memberships (nr) 0.96 1.20
Western immigrant (dummy) 0.04 0.19
Non-western immigrant (dummy) 0.06 0.24
Years in current address 13.90 12.81
Mean household income in neighbourhood (x €1000) 23.91 5.66
% non-western immigrants in neighbourhood 16.34 9.02
Urban: >2500 addresses per km2 (dummy) 0.39 0.49
Network size and share of neighbours
Think about the people you feelvery close to: - discuss important matters,
- regularly keep in touch with,
- are there if you need help
somewhat close to: - more than just casual acquaintances
• Network size mean 24.85 st. dev. 25.68
• Share of neighbours mean 10.14 st. dev. 13.50
# direct relatives ………… # other relatives ………..
# colleagues …………….. # club members ………..
# neighbours ……………… # other friends ………….
Interaction with neighbours
Frequency of interaction N %
Never (0) 49 6.5
Once a month or less (1) 112 14.9
2 or 3 times per month (2.5) 89 11.9
Once a week (4) 130 17.3
Several times per week (12) 237 31.6
(almost) every day (24) 133 17.7
Methods
• Path analysis− Can capture the relationships between several dependent
and independent variables− Special case of structural equation modeling (SEM)− Deals only with measured variables
Methods
• Path analysis− Can capture the relationships between several dependent
and independent variables− Special case of structural equation modeling (SEM)− Deals only with measured variables
• Multilevel path analysis− Captures hierarchical structure of the data
Model structure
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results single-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Results multi-level model
% neighbours in network
Age
Work
Income
Children
Club memberships
Education
Non-western immigrant
Western immigrant
Years in address
Neighbourhood income
% Non-west. immigrants
Urban
Social network size
Interaction frequency
Conclusions
• Socio-demographics are more important than neighbourhood characteristics in explaining contacts with neighbours
Conclusions
• Socio-demographics are more important than neighbourhood characteristics in explaining contacts with neighbours
• Contacts with neighbours higher for people with longer residence and more time at home
Conclusions
• Socio-demographics are more important than neighbourhood characteristics in explaining contacts with neighbours
• Contacts with neighbours higher for people with longer residence and more time at home
• Limited effects of neighbourhood characteristics
Conclusions
• Socio-demographics are more important than neighbourhood characteristics in explaining contacts with neighbours
• Contacts with neighbours higher for people with longer residence and more time at home
• Limited effects of neighbourhood characteristics
• Difference between single and multi-level model
A multilevel path analysis of social networks and social interaction in the neighbourhood
Pauline van den Berg
Harry Timmermans