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

Abstract

The purposes of this study were to (a) develop an empirical typology of the social networks in Korean adults aged 18 years or older and (b) examine the relation of network types on depressive symptoms and satisfaction with life. Data for this study were drawn from the survey with 1,017 community-dwelling adults aged 18 years or older in South Korea representing three life stages: young adults, middle-aged adults, and older adults. Latent profile analysis 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. A model with four network types was identified as being the most optimal, and the groups were labeled as: diverse-family, diverse-friend, solo-restricted, and coresident-restricted. Regression analyses showed that in comparison with the coresident-restricted group referent, diverse-friend and solo-restricted groups exhibited elevated levels of depressive symptoms; diverse-family and diverse-friend type had higher levels of satisfaction with life. Implications of the study findings were discussed in line with current research and intervention efforts.

Keywords

network typology psychological well-being Korean adults

Once a community-oriented and generations-integrated society, Korea has been engaged in the process of transforming into a more nuclear family-oriented and age-segregated society for several decades (A.-R. Lee, 2003; Yang, 2003). Rapid capital growth and cultural transformation may bring greater risk of citizens becoming socially disconnected and demoralized (Berkman, Glass, Brissette, & Seeman, 2000; Hawkley & Cacioppo, 2010). It is concerning, for example, that Korea has experienced a surge in mental health issues and suicide rates across age spans (S. Cho et al., 2009; E. Park & Choi, 2013). Consequently, efforts have been made to address mental health issues and to better integrate individuals across different life stages. Given that the social connectedness of individuals across life stages may indicate how well a society is socially integrated (McPherson, Smith-Lovin, & Brashears, 2006; Putnam, 1995; Riley & Riley, 2000; Uhlenberg, 2000), it would be critical to examine patterns of social connectedness (or disconnectedness) at different life stages and the linkages of these patterns to mental health. Considering the need, the article aimed to identify empirically based typologies of social networks in Korean adults of differing ages and to examine the linkage between different types of social networks and individual well-being.

Variable-Centered Versus Person-Centered Approaches

Researchers have extensively documented the beneficial effects of social networks on individuals’ health and psychological well-being (Holt-Lunstad, Smith, & Layton, 2010; Kawachi & Berkman, 2001; Smith & Christakis, 2008; Thoits, 2011). One approach to explore and compare networks in cross-cultural settings may involve developing empirically based network typologies on the basis of variable profiles shared by individuals (e.g., Fiori, Antonucci, & Akiyama, 2008). In contrast with approaches that focus on relationships between variables, person-centered approaches identify subgroups of individuals who share certain characteristics, and these shared characteristics distinguish one group from others (Everitt, Landau, Leese, & Stahl, 2011; Muthén & Muthén, 2000). The current study used latent profile analysis (LPA), a variant of person-centered approaches, to meaningfully classify naturally occurring subgroups on the basis of social network characteristics. LPA is based on the related assumptions that population consists of heterogeneous and latent subgroups that can be identified based on their shared characteristics and that the results may be generalized to the subgroups of individuals (Nylund-Gibson & Choi, 2018; Oberski, 2016; Schmiege, Meek, Bryan, & Petersen, 2012; Vermunt, Lewis-Beck, Bryman, & Liao, 2004).

Empirical Social Network Typologies

Using person-centered approaches, previous studies have identified four common types of social networks across various cultural settings (Cheng, Lee, Chan, Leung, & Lee, 2009; Fiori et al., 2008; Litwin, 2001). The four types include: diverse (having strong connections with family and friends as well as actively participating in social activities), family (focusing on relationships and contacts with family), friend (maintaining close relationships and contacts with friends and participating in social activities), and restricted (having limited social connections and social activities). These different network types have been found to influence individuals’ psychological well-being. While individuals who are embedded in more diverse, enriched networks tend to have better psychological well-being, those with more restricted, disconnected networks often have poor mental health. Cultural variations in networks and their associations with mental health have also been documented. For example, studies with older Chinese or Koreans or Korean Americans identified family networks but no or weak friend networks (Cheng et al., 2009; Cheon, 2010; N. S. Park et al., 2015). Not only were family-focused networks more pronounced but also they were consistently associated with psychological well-being in Asian older adults (e.g., Sohn et al., 2017).

Scholars also have identified variations in network types specific to particular groups. For example, Fiori, Antonucci, and Cortina (2006) identified two types of restricted networks in an older American sample including nonfamily restricted (deficient in family relationships and contacts) and nonfriend restricted (deficient in ties with friends and limited participation in social activities). Cheng et al. (2009) identified two family-related networks in a sample of older Chinese in Hong Kong: family (focusing on immediate family) and distant family (having connections with distant kin). In a sample of older Koreans, S. Park, Smith, and Dunkle (2014) found a couples-focused network (maintaining moderate family ties and contacts, yet keeping distance from children), instead of a family-focused network. Similarly, Fiori et al. (2008) identified a “married and distal” type in an older Japanese sample. Friend-focused networks emerged in an older Korean sample (N. S. Park, Jang, Lee, Chiriboga, Chang, & Kim, 2017) while family-related networks, without friend-related networks, were dominant in a sample of older Korean immigrants (N. S. Park et al., 2015). In studies with low-income older Koreans who are at high risk of depression, family networks found to be more important than friend networks (Chung, 2004; Chung, Jeon, & Song, 2016). Overall, evidence suggests that social networks may differ across cultural settings (Litwin, 2009).

Rational of the Study

Although studies have identified both general network types and types specific to a cultural group, most studies have focused on older adults. To our knowledge, no study of social network types have included a broad range of ages. Considering that social networks may be shaped in the social contexts through the life course (Antonucci, Ajrouch, & Birditt, 2014; Wrzus, Hänel, Wagner, & Neyer, 2013), it is important to examine how social networks differ for people whose networks formed at different life stages. The current study also contributes to the knowledge base of social network types by including contact via phone or social media as this form of social interactions gains popularity.

Theoretical Frameworks

The current study draws upon perspectives offered by social capital and life course theories as a means of understanding the association of social networks with psychological well-being. From the social capital perspective, individuals may create capital through their personal attributes (e.g., education, social skills, and wealth) and also from social capital resulting from membership in groups that are perceived as valuable (Carpiano & Fitterer, 2014; Kawachi, 1999; Putnam, 1995; Robbins & Judge, 2009). Networks with high levels of social capital may nurture an individual’s growth and provide network-mediated resources; a tight-knit social network, for example, is linked with feelings of closeness, influence, and information flow (Nyqvist, Forsman, Giuntoli, & Cattan, 2013; Portes, 1998). What distinguishes social capital from other resources is the role of relationships among individuals (Abbasi, Wigand, & Hossain, 2014; Tsai & Ghoshal, 1998); more diverse and enriched networks may offer individuals greater resources and personal growth, whereas more restricted and disconnected networks deprive individuals of gaining a springboard to valuable resources and support.

Similarly, life course perspectives view social relationships in the context of historical, social, and cultural trends (e.g., Antonucci, Fiori, Birditt, & Jackey, 2010; Elder, 1994). Scholars suggest that core networks involving family are relatively stable over the life course, while nonfamily networks including friends, neighbors, and coworkers may be more affected by the life stage of individuals (Ertel, Glymour, & Berkman, 2009; Sander, Schupp, & Richter, 2017; Wrzus et al., 2013). The historic times shared by a cohort may also affect the composition and expectations of social relationships (Elder, 1994).

The last point, concerning the experiences of different cohorts, is particularly pertinent to the present study. The three age-groups in the sample have been exposed to very distinct historical, social, and cultural influences. For example, individuals in the group aged 65 years and older were born in 1950 or earlier, and to varying degrees directly experienced the Korean War (1950–1953) and its immediate aftermath. Social and economic resources were scarce during their formative years and a majority received education below high school. The middle-aged group, those aged 45 to 64 years, includes the Korean War baby boomers. During their formative years, the birth rates hiked up to six children (Dong, 1993), and this birth cohort experienced rapid social, economic, and political changes that followed the Korean War (Kwon & Park, 2018). The youngest group, those aged 18 to 44 years, includes individuals born after 1972 and those who have experienced a period of unprecedented economic and political growth, increased participation of women in the labor forces, urbanization, and declining birth rates.

Study Purposes and Hypotheses

Based on the theoretical perspectives and previous research, the objectives of the current study were to (a) develop an empirical typology of the social networks in Korean adults aged 18 years or older and (b) examine the association of network types with depressive symptoms and satisfaction with life. In addition to structural aspects of social networks (e.g., marital status, size of family or friend confidant networks, and frequency of contact), the current study included a subjective variable (perceived closeness to family) as a criterion variable. We hypothesized that there would be types similar to those reported in cross-cultural studies as well as types unique to Korean adults. We also hypothesized that the identified types would be differentially associated with depressive symptoms and satisfaction with life. Based on previous research, it was also expected that those who were embedded in more diverse and enriched networks would have lower levels of depressive symptoms and greater satisfaction with life than those belonging to more restricted and disconnected networks.

Method

Participants

Data for this study were drawn from the Ewha Study of Intergenerational Issues. After the study was approved by the University institutional review board, face-to-face-interviews were conducted in the spring of 2017 with participants aged 18 years or older. A multistage, stratified random sampling method was used to recruit participants and to increase the representativeness of sampling. First, the research team selected seven metropolitan cities (Seoul, Busan, Daegu, Incheon, Daejeon, Gwangju, and Ulsan) and eight provinces (Gyeonggi-do, Gangwon-do, Chungcheongbuk-do, Chungcheongnam-do, Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, and Gyeongsangnam-do) in South Korea. Second, 33 smaller administrative districts were randomly selected. In the third and final recruitment step, households in those smaller administrative districts were randomly selected, stratified by age-groups (18–44, 45–64, and 65 years and older) and gender. Trained interviewers visited the selected households and conducted interviews with eligible participants in their homes once the respondents agreed to participate. If there were more than one potential participants in a household, interviewers chose the oldest person for participation to follow Korean tradition, which is likely to select older people first before considering younger people in Korea. The final sample included a total of 1,017 respondents (508 men and 509 women, 307 aged 18–44 years, 357 aged 45–64 years, and 353 aged 65 years or older).

Measures

Outcome variables

The two outcome measures were depressive symptoms and satisfaction with life. Depressive symptoms were assessed with a 10-item short form of the Center for Epidemiologic Studies-Depression Scale (Andresen, Malmgren, Carter, & Patrick, 1994; Radloff, 1977). Each question asked the frequency of depressive symptoms during the past week on a 4-point scale: rarely or none of the time (0), some of the time (1), much of the time (2), and most or all of the time (3). The scale included two positively worded items (“I felt I had good days” and “I felt I did well”) and eight negatively worded items (e.g., “I felt depressed” and “I could not get going”). After reversely coding the two positively worded items, the responses were summed into a total score that ranged from 0 (no depressive symptom) to 30 (severe depressive symptoms). The Center for Epidemiologic Studies-Depression Scale has been translated into the Korean language, and its psychometric properties have been validated in previous studies (M. J. Cho, Nam, & Suh, 1998; Noh, Avison, & Kaspar, 1992). Cronbach’s alpha for the present sample was .78. Life satisfaction was assessed with the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). Participants were asked how strongly they agreed with each of the five items, for example, “in most ways my life is close to my ideal” on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). The total score could range from 5 to 35 with higher scores indicating greater satisfaction with life. Cronbach’s alpha for the present sample was .90.

Social network variables

The network typology was developed based on eight social network-related variables: marital status, living arrangement, number of family confidants, number of friend confidants, frequency of contract with friends, frequency of participation in social groups, frequency of contact with neighbors, and perceived closeness to family. Marital status was dichotomously coded (0 = not married, 1 = married), so was living arrangement (0 = living with others, 1 = living alone). The number of family or friend confidants was each measured with a single question from the social network inventory (Lubben & Gironda, 2003): How many relatives (or friends) do you feel at ease with that you can talk about private matters? Each item had six response categories (0 = none, 1 = one, 2 = two, 3 = three or four, 4 = five to eight, 5 = nine or more). Frequency of contact with friends was assessed by how frequently respondents had contacted with friends via phone or social media (0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = always). Similar questions and response formats were used for assessing frequency of participation in social groups (how frequently do you participate in activities with social groups of leisure, hobby, sports, or culture?) and frequency of conversation with neighbors (how frequently do you talk with your neighbors?). For subjective evaluations of how close respondents felt toward their family, there were four response categories (1 = not at all, 2 = somewhat close, 3 = close, 4 = very close).

Sociodemographic variables

Sociodemographic information included age (1 = 18–44 years, 2 = 45–64 years, 3 = 65 years or older), gender (0 = male, 1 = female), education (0 = ≥ high school graduation, 1 = < high school graduation), and self-rated health (1 = very poor, 2 = poor, 3 = fair, 4 = good, and 5 = excellent). Perceived financial status was originally assessed with seven response categories (from not at all to very much) to a question regarding how satisfied the respondents were for their financial status; responses were recoded into three categories (1 = below average, 2 = average, and 3 = above average).

Data Analysis

LPAs were conducted using the eight network-related variables as criteria. Starting from a two-cluster model, the number of clusters was sequentially increased to identify the optimal number of classes. The model fit was evaluated at each increase in the number of clusters based on five statistical criteria: the Bayesian Information Criterion (BIC), entropy (an index of classification quality), the Lo-Mendell-Rubin-likelihood ratio test (LMR-LRT), the bootstrap likelihood ratio test, and posterior probabilities. Model fit is more favorably evaluated with lower BIC values, higher entropy values, and significant LRTs (Muthén, 2001; Nylund, Asparouhov, & Muthén, 2007). The two LRTs (LMR-LRT and bootstrap likelihood ratio test) compare two adjacent models: the (c–1)-cluster model versus the c-cluster model; significant p values suggest that the current model performs better than the preceding model (Nylund et al., 2007). In addition, posterior probabilities (i.e., matrix of conditional probabilities for cases to be placed in their respective cluster) suggest that classification quality is good when diagonal values are high and off-diagonal values are low (Berlin, Williams, & Parra, 2014).

Once a best-fit model was identified, clusters were compared with respect to sociodemographic characteristics, depressive symptoms, and satisfaction with life, using a series of chi-square and analysis of variance tests. In the final step of analyses, multiple regression models of depressive symptoms and satisfaction with life were estimated conditional on social network types and sociodemographic characteristics. Data analyses were conducted using Mplus version 8 (Muthén & Muthén, 1998–2017) and SPSS statistics version 25.

Results

Latent Profile Analysis

Table 1 presents the results of a series of LPAs including two- to five-cluster solutions. As evaluated by the multiple criteria, a four-cluster model was the best fit. The two LRTs were significant up to that model (significant p value showed that it performed better than the preceding model). The results suggested that four-cluster and five-cluster solutions were comparable in entropy values (.80 and .81, respectively). Although BIC value was lower for the five-cluster model, the result of LMR-LRT deteriorated from the four-to five cluster models (p > .05). The diagonal values of the matrix of conditional probabilities in the four-cluster solution (not shown in the table) ranged from .79 to .97, demonstrating decent classification quality. Considering the LRTs, model parsimony, and distribution of cluster sizes, the four-cluster model performed best.

Table 1.
Model Fit Statistics for Selecting the Optimal Number of the Classes.

Model BIC Entropy LMR-LRT (H₀ = k − 1 cluster) BLRT (H₀ = k − 1 cluster)

Two clusters 17,503.138 .92 p < .001 p < .001

Three clusters 17,412.460 .74 p < .001 p < .001

Four clusters 17,286.041 .80 p < .05 p < .001

Five clusters 17,224.040 .81 p > .05 p < .001

Note. BIC = Bayesian information criterion; LMR-LRT = Lo-Mendell-Rubin likelihood ratio test; BLRT = bootstrap likelihood ratio test.

The best cluster solutions can be achieved with low BIC values, high entropy (i.e., an index of the classification quality). In addition, the LMR-LRT and BLRT compare the current model (c cluster) with prior model (c–1 cluster). The significant p value suggests that the current model performs better than the prior model. Selected model is bolded.

Profiles of Social Network Types

Table 2 presents the profiles of the four social network subgroups in relations to the eight criterion variables. A graphical illustration of the profiles using standardized scores is provided in Figure 1. Based on their distribution on social network characteristics, the groups were named diverse-family, diverse-friend, solo-restricted, and coresident-restricted.

Figure 1.
Standardized mean scores of criterion variables in social network types.

Table 2.
Profiles of Social Network Types.

Network variables (range) The total sample (N = 1,017) 1. Diverse family (n = 163) 2. Diverse friend (n = 395) 3. Solo restricted (n = 76) 4. Coresident restricted (n = 383) Test statistic

% or M (SD) % or M (SD) % or M (SD) % or M (SD) % or M (SD)

Married (0, 1) 76.7 94.4 77.7 0.0 83.3 χ² = 284.68*

Live alone (0, 1) 9.4 0.0 5.1 100 0.0 χ² = 794.89*

Number of family confidants 2.39 (.83) 3.15 (.51) 2.53 (.85) 2.20 (.85) 1.98 (.64) F = 106.19*

(0–5) [2,3,4] [1,3,4] [1,2] [1,2]

None .9 0.0 .8 3.9 .8

One 11.1 0.0 8.1 11.8 18.8

Two 44.1 6.1 40.8 48.7 62.7

Three or four 36.3 73.6 40.0 31.6 17.5

Five to eight 7.0 19.6 8.9 3.9 .3

Nine or more .7 .6 1.5 0.0 0.0

Number of friend confidants 2.48 (.95) 3.27 (.68) 2.72 (.90) 2.21 (1.06) 1.96 (.72) F = 117.30*

(0–5) [2,3,4] [1,3,4] [1,2] [1,2]

None 1.6 0.0 .5 3.9 2.9

One 10.8 .6 5.6 17.1 19.3

Two 39.3 4.3 34.7 47.4 57.4

Three or four 37.4 69.3 44.1 21.1 20.1

Five to eight 8.0 19.0 11.1 6.6 .3

Nine or more 2.9 6.7 4.1 3.9 0.0

Frequency of contact with friends using phone or social media (0–4) 2.61 (.98) 2.50 (.95) 2.82 (.85) 2.09 (1.16) 2.55 (1.02) F = 14.91*

[2,3] [1,3,4] [1,2,4] [2,3]

Never 3.1 4.3 1.5 11.8 2.6

Rarely 8.4 8.0 3.8 17.1 11.5

Sometimes 30.7 33.1 26.3 30.3 34.2

Often 39.6 42.9 47.8 31.6 31.3

Always 18.2 11.7 20.5 9.2 20.4

Frequency of participation in social groups (0–4) 1.27 (1.23) .40 (.49) 2.64 (.60) .62 (.75) .36 (.49) F = 1303.74*

[2,3] [1,3,4] [1,2,4] [2,3]

Never 3.1 59.5 0.0 53.9 64.2

Rarely 8.4 40.5 0.0 30.3 35.2

Sometimes 30.7 0.0 42.0 15.8 .5

Often 39.6 0.0 51.6 0.0 0.0

Always 18.2 0.0 6.3 0.0 0.0

Frequency of conversation with neighbors (0–4) 2.43 (.94) 2.71 (.80) 2.51 (.92) 2.72 (.87) 2.17 (.96) F = 19.06***

[4] [4] [4] [1,2,3]

Never 2.6 .6 2.0 0.0 4.4

Rarely 14.1 5.5 12.7 9.2 20.1

Sometimes 31.5 30.7 29.1 27.6 35.0

Often 42.0 49.1 45.1 44.7 35.2

Always 9.9 14.1 11.1 18.4 5.2

Perceived closeness to family (1–4) 3.06 (.60) 3.09 (.48) 3.10 (.61) 2.91 (.68) 3.03 (.62) F = 2.68*

[3] [2]

Not at all .9 0.0 1.5 2.6 .3

Somewhat close 12.7 7.4 9.6 19.7 16.7

Close 66.3 76.1 66.3 61.8 62.9

Very close 20.2 16.6 22.5 15.8 20.1

Note. Highest % or mean is presented in bold, and lowest % or mean is underlined.

Statistically different groups in the post hoc Tukey comparison at p ≤ .05 are listed in brackets under mean and standard deviation.

p < .05. p < .001.

Including 16% of the sample, the diverse-family group had the highest proportion of individuals who were married and the largest number of family confidents and friend confidents. None lived alone. Their levels of contact with friends and participation in social groups were below average, yet they maintained high levels of conversation with neighbors and felt close to their family. Composing of 38.8% of the sample, the diverse-friend group was the largest. About 78% were married, and this group had the highest levels of contact with friends via media and participation in activities with social groups. The group also had above average number of family and friend confidents and frequent conversation with neighbors. Like the diverse-family group, members in this group felt very close to their family.

The solo-restricted group was the smallest, representing only 7.5% of the sample. All members lived alone, and none were married. The group maintained below average number of family and friend confidants and had the least frequent contact with friends via media. They reported low levels of participation in social groups and felt least close to family, yet interacted with neighbors at the highest level. The second largest group, coresident restricted, made up of 37.7% of the sample. Well over 80% were married, and none lived alone. They had the most restricted social connections in terms of number of family and friend confidants, participation in social groups, and conversation with neighbors. However, the group maintained connections through contact with friends via phone or social media and was average in feeling close to family.

Sociodemographic Characteristics and Psychological Well-Being by Network Types

Table 3 shows the comparison of the four social network groups with respect to sociodemographic characteristics and well-being. The majority of the diverse-family group fell into the middle and older age-groups (80% combined). The group had the lowest proportion of women and was less likely to have completed high school education. Their self-rated health was below average, yet their perception of financial status was average or above average. The group had the lowest level of depressive symptoms and above average level of satisfaction with life. The diverse-friend group included the highest proportions of younger and middle agers (73%) and the lowest proportion of those 65 years and older (27%). About half of the group were women, and most had attained at least high school education. Self-rated health was the highest and they were about average in perceived financial status. The members also had an average level of depressive symptoms and the highest level of satisfaction with life.

Table 3.
Sociodemographic Characteristics and Psychological Well-Being by Network Types.

Variable The total sample (N = 1,017) 1. Diverse family (n = 163) 2. Diverse friend (n = 395) 3. Solo restricted (n = 76) 4. Coresident restricted (n = 383) Test statistic and group comparison

Sociodemographic characteristics

Age (%)

18–44 30.2 20.2 35.7 14.5 31.9 χ² = 62.09*

45–64 35.1 36.8 37.2 15.8 36.0

> 65 34.7 42.9 27.1 69.7 32.1

Gender (%)

Female 50.0 41.1 48.1 77.6 50.4 χ² = 28.96*

Education (%)

< High school 23.8 27.0 17.2 56.6 22.7 χ² = 55.64*

Self-rated health 3.58 (.85) 3.47 (.73) 3.77 (.86) 2.88 (.88) 3.58 (.80) F = 26.30*

(range 1 − 5), M (SD) [2,3] [1,3,4] [1,2,4] [2,3]

Perceived financial status (%) χ² = 25.21*

Below average 33.6 27.0 29.4 55.3 36.6

Average 36.8 39.3 37.7 26.3 36.8

Above average 29.6 33.7 32.9 18.4 26.6

Psychological well-being

Depressive symptoms 6.48 (4.01) 6.05 (3.76) 6.55 (4.14) 8.83 (4.0) 6.13 (3.82) F = 10.64*

(range 0–24), M (SD) [3] [3] [1,2,4] [3]

Satisfaction with life 19.19 (4.49) 19.77 (4.61) 19.96 (4.45) 17.08 (4.81) 18.57 (4.19) F = 13.30*

(range 5–32), M (SD) [3,4] [3,4] [1,2,4] [1,2,3]

Note. ***p < .001.

Highest % or mean is presented in bold, and lowest % or mean is underlined.

Statistically different groups in the post hoc Tukey comparison at p < .05 are listed in brackets under mean values.

Nearly 70% of the solo-restricted group were aged 65 years or older. Over three quarters were women (78%), and less than half had completed high school education. Self-rated health was lowest, and more than half perceived their financial status below average. Not surprisingly, the group had the highest level of depressive symptoms and lowest level of satisfaction with life. The coresident-restricted group was proportionally represented by the three age-groups and gender in the sample. About a quarter of this group had received less than a high school education, and their self-rated health mirrored the average self-rating of health in the sample. The same trend was found for their perceived financial status. The group reported the second lowest levels of depressive symptoms and satisfaction with life.

Multiple Regression Models of Psychological Well-Being

Table 4 summarizes the results of multiple regression analyses testing the associations of social network types and socioeconomic characteristics with the two outcome variables: depressive symptoms and satisfaction with life. Using the coresident-restricted group as the reference, diverse-friend and solo-restricted groups were associated with higher levels of depressive symptoms. For background characteristics, less than high school education, poorer self-ratings of health, and lower levels of perceived financial status also contributed to elevated levels of depressive symptoms. With respect to satisfaction with life, members in the diverse-family and diverse-friend groups were more likely to be satisfied with their life compared with that of the solo-restricted group. In addition, older age, higher education and self-rating of health, and greater perceived financial status were independently linked with higher levels of satisfaction with life.

Table 4.
Regression Models of Depressive Symptoms and Satisfaction With Life.

Variable Depressive symptoms Satisfaction with life

b SE b SE

Social network types

Diverse family –0.10 .36 0.18* .09*

Diverse friend 0.69* .28 0.22 .07

Solo restricted 1.71 .50 –0.07 .12

Coresident restricted [reference] [reference] [reference] [reference]

Background characteristics

Age −0.02 .01 0.01 .00

Female −0.10 .25 0.01 .06

<high school 1.16 .35 −0.31* .08

Self-rated health −1.01* .17 0.11 .04

Perceived financial status −0.40 .15 0.48*** .04

Note. The coresident-restricted group in the social network types was used as a reference group and omitted from analysis.

p < .05. p < .01. p < .001.

Discussion

Main Findings of the Study

Using a representative lifespan sample of Korean adults aged 18 years and older, the present study developed an empirical typology of social networks on a basis of commonly used network variables. Findings supported the hypotheses that both common and unique network types would emerge from the sample of Korean adults, and that those embedded in more diverse, enriched networks would have lower depression and greater satisfaction with life than those belonging to more restricted, disengaged networks. Overall findings were in accord with what would be expected on the basis of social capital theory and the life course perspective: that those who have advantages in social resources cultivate benefits from social relationships and that older adults are more likely to belong to a network typology emphasizing family relationships (Kawachi, 1999; Nyqvist et al., 2013).

The four identified network groups (diverse family, diverse friend, solo restricted, and coresident restricted) represent permutations of the four types usually obtained in studies with older adults: diverse, family-focused, friend-focused, and restricted (e.g., Cheng et al., 2009; Litwin, 2001). The identified typology included two variants of a diverse type and two variants of a restricted type. Of interest is that the standard diverse type was split into two variants: diverse family (more typical of the middle and older respondents) and diverse friend (more typical of the younger and middle-aged respondents). In both cases, levels of depressive symptoms were relatively low, and life satisfaction was considerably high, compared with the solo-restricted group.

As expected, the four network groups differed significantly on sociodemographic characteristics and psychological well-being. For example, the diverse-friend group included disproportionate numbers of those falling into the younger and middle-aged categories. In nearly all indicators of sociodemographic status and health, they stood out as being most favored; they were highly educated, over two thirds rated themselves to be average or above average in financial well-being, had an average number of depressive symptoms, and were highest in life satisfaction. The sociodemographic characteristics of this friend-focused group and favorable psychological well-being are in line with previous findings about beneficial effects of friend networks on positive psychological well-being (Fiori et al., 2006; Miche, Huxhold, & Stevens, 2013; Nicolaisen & Thorsen, 2017). From the life course perspective, younger and middle-aged adults may invest on social relationships outside the family more than older adults (Antonucci et al., 2010; Carstensen, 1995; C. W. Sherman, Wan, & Antonucci, 2015). Although having friends enriches lives of adults of all ages, the importance may be more salient for younger or middle-aged adults.

In contrast, a disproportionate number of middle-aged and older adults were categorized as belonging to the diverse-family group. This finding is in line with previous findings that the networks of older adults are likely to be family focused (Cheon, 2010; N. S. Park et al., 2015). This group seemed to take advantage of strong networks of both family and friends although they did not actively participate in social groups.

The two diverse groups maintained strong relationships in multiple domains. The diverse family was more likely to be married and kept both family and friend confidants, yet had relatively low contact with friends or participation in activities. The diverse friend was actively involved with friends and social groups, yet they were less likely to be married and have smaller family and friend confidant networks. The existence of these two variants of the standard diverse network suggest that these networks are reflecting lifestyles perhaps most attuned to the life and circumstances of people at different stages of life who are functioning optimally (Fuller-Iglesias, Webster, & Antonucci, 2015; Nicolaisen & Thorsen, 2017). The younger respondents in the diverse-friend group are active across a broad spectrum of domains, but especially that of friends. The older group, diverse family, may be slightly slowed down in terms of overall activity and may be focusing on family. It is possible that their family focus may reflect the restrictions in social capital that were common in post-war Korea.

The two restricted networks overall lacked social ties on multiple domains compared with the two diverse networks. In particular, the solo-restricted group was structurally isolated where all of the members lived alone and none were married; their involvement with other social ties and activities was weak and had lower perception of closeness to family except strong contact with their neighbors. The structural isolation coupled with poor sociodemographic profiles put the solo-restricted group at particular risk. In contrast, while members in the coresident-restricted group had overall low levels of social ties and involvement in social groups, none lived alone, and the majority were married and maintained frequent contacts with friends over phone or social media.

Results from multiple regression analyses showed that compared with the coresident-restricted group, the diverse-family and diverse-friend groups were associated with greater satisfaction with life and that diverse-friend and solo-restricted groups were linked with higher levels of depressive symptoms. Despite the overall lack of social connections, the finding that the coresident-restricted group manifested fewer mental distresses than the diverse-friend group as well as the solo-restricted group invites further speculation. One explanation could be that the coresident-restricted group might be structurally connected through being married and living with others and engaging in meaningful relationships through contact with friends over media and feeling close to family; such characteristics are linked with psychological well-being (Birditt & Antonucci, 2007; Birditt, Jackey, & Antonucci, 2009; A. M. Sherman, de Vries, & Lansford, 2000).

The poor psychological well-being outcomes for the solo-restricted group compared with the coresident-restricted group may be linked with social isolation factors such as living alone and not having confidants; all of the solo-restricted group lived alone and 3.9% had no family confidants, whereas all of the coresident group lived with others and only .8% had no family confidants. Thus, the finding may suggest that structural engagement and having some close relationships might have protected psychological well-being of the coresident-restricted group, whereas the solo-restricted group had no such protection. Despite cultural and gender variations, living alone and lack of a confidant have been associated with poor emotional outcomes such as loneliness and depressive symptoms (Cornwell & Waite, 2009; Greenfield & Russell, 2011; Lim & Ng, 2010).

Implications of Findings

Overall results suggest that special attention should be paid to those whose networks resemble the solo-restricted group consisting of older persons with limited social capital. It is also noteworthy that although less than a third of the solo-restricted group were made up of younger and middle-aged groups, these individuals may be positioned with disadvantages in social capital in their later years. Given that deficiency in social capital and resources in the early life course is linked with adversity in the later life course (Dannefer, 2003; DiPrete & Eirich, 2006; O’Rand, 1996), special efforts should be made to identify and provide resources to younger and middle-aged adults with low social capital. Evidence suggests that earlier intervention could change the life trajectories of individuals to be more successful at their later years (Nurius, Prince, & Rocha, 2015; O’Rand, 1996).

The uneven age distribution in network groups may provide insight on the effort to integrate individuals with varying age spans in the Korean society, which has experienced a consistent growth of older adults and expect the trend to continue (United Nations, 2002). Aging of the country coupled with low birth rates and diminished younger population have pronounced the issues of elder care and segregation of generations especially as caring for older adults become a social issue from family responsibility (I. Lee, 2015; Sung, 2001). The demographic and social change led to efforts to integrate population of young and old and find a common ground (A.-R. Lee, 2003; Yang, 2003). Findings of this study suggest that it is critical to identify those who are embedded in social networks with poor social capital and address their economic and social needs at different life stages.

Limitations of the Study

There are some limitations of the study to consider interpreting and applying the results. First, the cross-sectional nature of research design is limited drawing causal inferences about the influence of network types on depressive symptoms and satisfaction with life. Possibly, individuals’ psychological well-being may influence the choice, interactions, and maintenance of social relationships. In addition, rather than drawing inferences about age-group differences from cross-sectional data, longitudinal studies should provide more rigorous rationale for observing change over the duration of the life course. Second, a strength of the study (inclusion of wide age ranges) may present a weakness as well. Sample sizes of the three age-groups are smaller (ranging from 307 to 353), thus may limit generalizing findings to population of different life spans. Lastly, although social capital theory was relevant to the constructs of the study, the study lacks more specific components of social capital such as quality of interactions and emotional components (Putnam, 1995).

Conclusion

Despite limitations, the present study offers insight on the composition of social networks across life course and the association of specific network groups with psychological well-being in a representative sample of Korean adults. Using a person-centered approach, the study identified subgroups of individuals who were at greater risk of poor social capital and psychological well-being. Results of the study could be used guiding intervention efforts to target risk and marginal social groups and address their sociodemographic needs and health or well-being. It would be important to pay a special attention to within-group differences (e.g., age and economic status) as well as between-group differences in order to tailor efforts to specific needs of individuals and groups.

Model	BIC	Entropy	LMR-LRT (H₀ = k − 1 cluster)	BLRT (H₀ = k − 1 cluster)
Two clusters	17,503.138	.92	p < .001	p < .001
Three clusters	17,412.460	.74	p < .001	p < .001
Four clusters	17,286.041	.80	p < .05	p < .001
Five clusters	17,224.040	.81	p > .05	p < .001

Network variables (range)	The total sample (N = 1,017)	1. Diverse family (n = 163)	2. Diverse friend (n = 395)	3. Solo restricted (n = 76)	4. Coresident restricted (n = 383)	Test statistic
Married (0, 1)	76.7	94.4	77.7	0.0	83.3	χ² = 284.68***
Live alone (0, 1)	9.4	0.0	5.1	100	0.0	χ² = 794.89***
Number of family confidants	2.39 (.83)	3.15 (.51)	2.53 (.85)	2.20 (.85)	1.98 (.64)	F = 106.19***
(0–5)		[2,3,4]	[1,3,4]	[1,2]	[1,2]
None	.9	0.0	.8	3.9	.8
One	11.1	0.0	8.1	11.8	18.8
Two	44.1	6.1	40.8	48.7	62.7
Three or four	36.3	73.6	40.0	31.6	17.5
Five to eight	7.0	19.6	8.9	3.9	.3
Nine or more	.7	.6	1.5	0.0	0.0
Number of friend confidants	2.48 (.95)	3.27 (.68)	2.72 (.90)	2.21 (1.06)	1.96 (.72)	F = 117.30***
(0–5)		[2,3,4]	[1,3,4]	[1,2]	[1,2]
None	1.6	0.0	.5	3.9	2.9
One	10.8	.6	5.6	17.1	19.3
Two	39.3	4.3	34.7	47.4	57.4
Three or four	37.4	69.3	44.1	21.1	20.1
Five to eight	8.0	19.0	11.1	6.6	.3
Nine or more	2.9	6.7	4.1	3.9	0.0
Frequency of contact with friends using phone or social media (0–4)	2.61 (.98)	2.50 (.95)	2.82 (.85)	2.09 (1.16)	2.55 (1.02)	F = 14.91***
	[2,3]	[1,3,4]	[1,2,4]	[2,3]
Never	3.1	4.3	1.5	11.8	2.6
Rarely	8.4	8.0	3.8	17.1	11.5
Sometimes	30.7	33.1	26.3	30.3	34.2
Often	39.6	42.9	47.8	31.6	31.3
Always	18.2	11.7	20.5	9.2	20.4
Frequency of participation in social groups (0–4)	1.27 (1.23)	.40 (.49)	2.64 (.60)	.62 (.75)	.36 (.49)	F = 1303.74***
	[2,3]	[1,3,4]	[1,2,4]	[2,3]
Never	3.1	59.5	0.0	53.9	64.2
Rarely	8.4	40.5	0.0	30.3	35.2
Sometimes	30.7	0.0	42.0	15.8	.5
Often	39.6	0.0	51.6	0.0	0.0
Always	18.2	0.0	6.3	0.0	0.0
Frequency of conversation with neighbors (0–4)	2.43 (.94)	2.71 (.80)	2.51 (.92)	2.72 (.87)	2.17 (.96)	F = 19.06***
	[4]	[4]	[4]	[1,2,3]
Never	2.6	.6	2.0	0.0	4.4
Rarely	14.1	5.5	12.7	9.2	20.1
Sometimes	31.5	30.7	29.1	27.6	35.0
Often	42.0	49.1	45.1	44.7	35.2
Always	9.9	14.1	11.1	18.4	5.2
Perceived closeness to family (1–4)	3.06 (.60)	3.09 (.48)	3.10 (.61)	2.91 (.68)	3.03 (.62)	F = 2.68*
		[3]	[2]
Not at all	.9	0.0	1.5	2.6	.3
Somewhat close	12.7	7.4	9.6	19.7	16.7
Close	66.3	76.1	66.3	61.8	62.9
Very close	20.2	16.6	22.5	15.8	20.1

Variable	The total sample (N = 1,017)	1. Diverse family (n = 163)	2. Diverse friend (n = 395)	3. Solo restricted (n = 76)	4. Coresident restricted (n = 383)	Test statistic and group comparison
Sociodemographic characteristics
Age (%)
18–44	30.2	20.2	35.7	14.5	31.9	χ² = 62.09***
45–64	35.1	36.8	37.2	15.8	36.0
> 65	34.7	42.9	27.1	69.7	32.1
Gender (%)
Female	50.0	41.1	48.1	77.6	50.4	χ² = 28.96***
Education (%)
< High school	23.8	27.0	17.2	56.6	22.7	χ² = 55.64***
Self-rated health	3.58 (.85)	3.47 (.73)	3.77 (.86)	2.88 (.88)	3.58 (.80)	F = 26.30***
(range 1 − 5), M (SD)		[2,3]	[1,3,4]	[1,2,4]	[2,3]
Perceived financial status (%)						χ² = 25.21***
Below average	33.6	27.0	29.4	55.3	36.6
Average	36.8	39.3	37.7	26.3	36.8
Above average	29.6	33.7	32.9	18.4	26.6
Psychological well-being
Depressive symptoms	6.48 (4.01)	6.05 (3.76)	6.55 (4.14)	8.83 (4.0)	6.13 (3.82)	F = 10.64***
(range 0–24), M (SD)		[3]	[3]	[1,2,4]	[3]
Satisfaction with life	19.19 (4.49)	19.77 (4.61)	19.96 (4.45)	17.08 (4.81)	18.57 (4.19)	F = 13.30***
(range 5–32), M (SD)		[3,4]	[3,4]	[1,2,4]	[1,2,3]

Variable	Depressive symptoms	Satisfaction with life
Social network types
Diverse family	–0.10	.36	0.18*	.09*
Diverse friend	0.69*	.28	0.22**	.07
Solo restricted	1.71**	.50	–0.07	.12
Coresident restricted	[reference]	[reference]	[reference]	[reference]
Background characteristics
Age	−0.02	.01	0.01**	.00
Female	−0.10	.25	0.01	.06
<high school	1.16**	.35	−0.31***	.08
Self-rated health	−1.01***	.17	0.11**	.04
Perceived financial status	−0.40**	.15	0.48***	.04

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Foundation of Korea and funded by the Korean Government (NRF-2016-S1A3A2924582).

Author Biographies

Nan Sook Park, PhD, MSW, is an associate professor in the School of Social Work at the University of South Florida, USA. Dr. Park received her MSW and PhD from University of North Carolina at Chapel Hill. Dr. Park’s research interests include the roles of social isolation and psychosocial resources in community and long-term care settings. She is also interested in issues around health disparities, minority aging, and Alzheimer’s disease and related dementia.

David A. Chiriboga, PhD, is a professor in the Department of Child and Family Studies, University of South Florida, USA. His research involves health and mental health disparities, home and community based services for individuals with Alzheimer’s disease and related dementia, and barriers to health and health care posed by older immigrants with limited English proficiency.

Soondool Chung, PhD, MA, is a professor in the Department of Social Welfare at Ewha Womans University, South Korea. Her research interests include psychosocial well-being of older adults, aging family, single household of older adults, successful aging, case management practice for older adults, evidence-based social work practice, age integration and intergenerational issues, and community disaster and coping strategy for the elderly.

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Network variables (range)	The total sample (N = 1,017)	1. Diverse family (n = 163)	2. Diverse friend (n = 395)	3. Solo restricted (n = 76)	4. Coresident restricted (n = 383)	Test statistic
Network variables (range)	% or M (SD)	% or M (SD)	% or M (SD)	% or M (SD)	% or M (SD)	Test statistic
Married (0, 1)	76.7	94.4	77.7	0.0	83.3	χ² = 284.68***
Live alone (0, 1)	9.4	0.0	5.1	100	0.0	χ² = 794.89***
Number of family confidants	2.39 (.83)	3.15 (.51)	2.53 (.85)	2.20 (.85)	1.98 (.64)	F = 106.19***
(0–5)		[2,3,4]	[1,3,4]	[1,2]	[1,2]
None	.9	0.0	.8	3.9	.8
One	11.1	0.0	8.1	11.8	18.8
Two	44.1	6.1	40.8	48.7	62.7
Three or four	36.3	73.6	40.0	31.6	17.5
Five to eight	7.0	19.6	8.9	3.9	.3
Nine or more	.7	.6	1.5	0.0	0.0
Number of friend confidants	2.48 (.95)	3.27 (.68)	2.72 (.90)	2.21 (1.06)	1.96 (.72)	F = 117.30***
(0–5)		[2,3,4]	[1,3,4]	[1,2]	[1,2]
None	1.6	0.0	.5	3.9	2.9
One	10.8	.6	5.6	17.1	19.3
Two	39.3	4.3	34.7	47.4	57.4
Three or four	37.4	69.3	44.1	21.1	20.1
Five to eight	8.0	19.0	11.1	6.6	.3
Nine or more	2.9	6.7	4.1	3.9	0.0
Frequency of contact with friends using phone or social media (0–4)	2.61 (.98)	2.50 (.95)	2.82 (.85)	2.09 (1.16)	2.55 (1.02)	F = 14.91***
		[2,3]	[1,3,4]	[1,2,4]	[2,3]
Never	3.1	4.3	1.5	11.8	2.6
Rarely	8.4	8.0	3.8	17.1	11.5
Sometimes	30.7	33.1	26.3	30.3	34.2
Often	39.6	42.9	47.8	31.6	31.3
Always	18.2	11.7	20.5	9.2	20.4
Frequency of participation in social groups (0–4)	1.27 (1.23)	.40 (.49)	2.64 (.60)	.62 (.75)	.36 (.49)	F = 1303.74***
Frequency of participation in social groups (0–4)		[2,3]	[1,3,4]	[1,2,4]	[2,3]
Never	3.1	59.5	0.0	53.9	64.2
Rarely	8.4	40.5	0.0	30.3	35.2
Sometimes	30.7	0.0	42.0	15.8	.5
Often	39.6	0.0	51.6	0.0	0.0
Always	18.2	0.0	6.3	0.0	0.0
Frequency of conversation with neighbors (0–4)	2.43 (.94)	2.71 (.80)	2.51 (.92)	2.72 (.87)	2.17 (.96)	F = 19.06***
Frequency of conversation with neighbors (0–4)		[4]	[4]	[4]	[1,2,3]
Never	2.6	.6	2.0	0.0	4.4
Rarely	14.1	5.5	12.7	9.2	20.1
Sometimes	31.5	30.7	29.1	27.6	35.0
Often	42.0	49.1	45.1	44.7	35.2
Always	9.9	14.1	11.1	18.4	5.2
Perceived closeness to family (1–4)	3.06 (.60)	3.09 (.48)	3.10 (.61)	2.91 (.68)	3.03 (.62)	F = 2.68*
Perceived closeness to family (1–4)			[3]	[2]
Not at all	.9	0.0	1.5	2.6	.3
Somewhat close	12.7	7.4	9.6	19.7	16.7
Close	66.3	76.1	66.3	61.8	62.9
Very close	20.2	16.6	22.5	15.8	20.1