A multilevel path analysis of social networks and social interaction in the neighbourhood

Abstract

Abstract. Neighborhood-based social interactions have gained attention as a research topic in recent decades in light of urban policies that aim to improve livability in urban areas. Social interactions are anticipated to play an important role in neighborhood livability; however, empirical studies investigating the extent to which neighborhood characteristics can improve social contacts among residents are scarce and inconclusive. Therefore, this paper studies the role of socio-demographics and neighborhood characteristics in the formation of social network ties and social interactions with neighbors. These relationships are analyzed using a multi-level path analysis approach based on data collected in 2011 from a survey of 751 respondents in 70 neighborhoods of Eindhoven, the Netherlands. The results indicate that neighborhood-based contacts are influenced by personal and household characteristics, such as education, income, work status, ethnicity, household composition, and years at the current address. The effect of neighborhood characteristics is limited, and only one significant relationship was found -between neighborhood income and the number of neighbors in the network. This finding is inconsistent with the assumption that an adjustment of neighborhood characteristics can lead to increasing social interaction among neighbors.

Where do People Interact in High-Rise Apartment Buildings? Exploring the Influence of Personal and Neighborhood Characteristics

“…Age is found to have a significant effect on number of social interactions which is in line with the studies of Forrest and Kearns (2001) [ 66 ], Van Den Berg (2015) [ 42 ] and Moor and Mohammadi (2019) [ 43 ]. The oldest group (55 and older) recorded the lowest number of social interactions.…”

“…Age is found to have significant influence on the number of social interactions. Older people are less likely to interact with neighbors compared to younger age residents [ 42 , 43 ]. Home ownership [ 44 ] and length of residence [ 45 , 46 ] are positive indicators that increase social interaction between neighbors.…”

A Multi-Level Path Analysis of the Relationships between the Momentary Experience Characteristics, Satisfaction with Urban Public Spaces, and Momentary- and Long-Term Subjective Wellbeing

“…This method is able to simultaneously analyze relationships between several dependent and independent variables, which is required for this study. Another of the advantages of a multi-level path model is that this approach is able to take into account the hierarchical structure of the data (multiple momentary experiences per participant, which have to be treated as clusters) [73]. The multi-level path model was estimated using the statistical software package LISREL version 8.54 [74].…”

“…To build the model structure, a stepwise model selection was used by adding independent variables (categorical variables were recoded into dummy variables) and their relationships that were expected to have a relationship based on the literature review and found to be significant using bivariate analyses. Correlations of personal characteristics and experience characteristics are allowed in the model [73]. To reduce the number of variables in the path analysis and not overfitting the model, the links that were not found to be significant in the path model, at the 0.05 significance level ( t ≥ 1.96), were subsequently removed.…”

“…To reduce the number of variables in the path analysis and not overfitting the model, the links that were not found to be significant in the path model, at the 0.05 significance level ( t ≥ 1.96), were subsequently removed. This common backward stepwise process (see e.g., [73]) was repeated until a model was estimated with only significant relationships and the best model fit.…”