Abstract: Typology of Social Networks and Its Relationship to Psychological Well-Being in Korean Adults (Society for Social Work and Research 23rd Annual Conference - Ending Gender Based, Family and Community Violence)

243P Typology of Social Networks and Its Relationship to Psychological Well-Being in Korean Adults

Schedule:
Friday, January 18, 2019
Continental Parlors 1-3, Ballroom Level (Hilton San Francisco)
* noted as presenting author
Nan Sook Park, PhD, Associate Professor, University of South Florida, Tampa, FL
Soondool Chung, PhD, Professor, Ewha Womans University, Seoul, Korea, Republic of (South)
David Chiriboga, PhD, Professor, University of South Florida, Tampa, FL
Background and Purpose

Literature has documented that there are several types of social networks common across cultures, including “diverse” (maintaining diverse social ties), “family” (focusing on family relationships), “friends” (focusing on relationships with friends), and “restricted” (generally lacking social ties). Few network studies, however, have included individuals of different stages of life, mostly focusing on older adults. The purposes of this study were to: (1) develop an empirical typology of the social networks in Korean adults aged 18 and older; and (2) examine the relation of network types on depressive symptoms and satisfaction with life. We hypothesized that there would be both common and unique network types in Korean adults and that different types of network would be differentially associated with depressive symptoms and satisfaction with life.

Methods

Data for this study were drawn from the survey with community-dwelling adults aged 18 and older in South Korea. Multi-stage quota sampling was used to collect a sample of 1,017 respondents representing three life stages: young adults (age 18-44), middle-aged adults (age 45-64), and older adults (age 65 and over). Latent profile analysis (LPA) was conducted based on eight social network-related variables: marital status, living arrangement, number of family confidants, number of friend confidants, frequency of contact with friends using phone or social media, frequency of participation in social groups, frequency of conversation with neighbors, and perceived closeness of family. The identified typologies were then regressed on depressive symptoms and satisfaction with life. Data analyses were performed using Mplus (v.8) and SPSS statistical program (v.25).

Results

LPA identified a model with four network types as being the most optimal (BIC=17286.04, Entropy=.80, LMR-LRT= p < .05, BLRT= p < .001). The groups were labeled as “diverse-family” (enriched networks with strong endorsement with family members), “diverse-friend” (enriched networks with strong endorsement with friends and social activities), “marginal-neighbor” (structurally disengaged and marginally engaged with social relationships except neighbors), and “restricted-media” (having limited relationships but connected with friends via phone or social media). The results from regression models showed that when compared to the “marginal-neighbor” group, all other social network types were associated with reduced levels of depressive symptoms, and the “diverse-friend” type had higher levels of satisfaction with life.

Conclusions and Implications

The overall results support hypotheses that identified networks would be in line with common network types (family, friend) and that unique network types would emerge (marginal-neighbor, restricted-media). The network types were also differentially associated with depressive symptoms and satisfaction with life. It is noteworthy that the marginal-neighbor group that was structurally disengaged (not married and living alone) yet marginally engaged had lower psychological well-being than the restricted-media group whose networks were mostly restricted yet connected with friends via media. Findings point out the importance of understanding risk groups in network vulnerabilities when considering interventions. For example, the majority of the marginal-neighbor group were older, female, poorly educated, and economically disadvantaged. Network-based interventions could help identify such vulnerable groups (e.g., economically disadvantaged older women) and provide needed support.