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Volume 63
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FROM SOCIAL STRUCTURAL FACTORS TO PERCEPTIONS OF RELATIONSHIP QUALITY AND
LONELINESS: THE CHICAGO HEALTH, AGING, AND SOCIAL RELATIONS STUDY
Louise C. Hawkley,
Louise C. Hawkley
Address correspondence to Louise C. Hawkley, PhD, Department of Psychology,
University of Chicago, Biopsychological Sciences Building, 940 East 57th Street,
Chicago, IL 60637. E-mail: hawkley@uchicago.edu
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Mary Elizabeth Hughes,
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Linda J. Waite,
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Christopher M. Masi,
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Ronald A. Thisted,
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John T. Cacioppo
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The Journals of Gerontology: Series B, Volume 63, Issue 6, November 2008, Pages
S375–S384, https://doi.org/10.1093/geronb/63.6.S375
Published:
01 November 2008
Article history
Received:
20 February 2008
Accepted:
28 July 2008
Published:
01 November 2008
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CITE
Louise C. Hawkley, Mary Elizabeth Hughes, Linda J. Waite, Christopher M.
Masi, Ronald A. Thisted, John T. Cacioppo, From Social Structural Factors to
Perceptions of Relationship Quality and Loneliness: The Chicago Health,
Aging, and Social Relations Study, The Journals of Gerontology: Series B,
Volume 63, Issue 6, November 2008, Pages S375–S384,
https://doi.org/10.1093/geronb/63.6.S375
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ABSTRACT
Objectives. The objective of this study was to test a conceptual model of
loneliness in which social structural factors are posited to operate through
proximal factors to influence perceptions of relationship quality and
loneliness.
Methods. We used a population-based sample of 225 White, Black, and Hispanic men
and women aged 50 through 68 from the Chicago Health, Aging, and Social
Relations Study to examine the extent to which associations between
sociodemographic factors and loneliness were explained by socioeconomic status,
physical health, social roles, stress exposure, and, ultimately, by network size
and subjective relationship quality.
Results. Education and income were negatively associated with loneliness and
explained racial/ethnic differences in loneliness. Being married largely
explained the association between income and loneliness, with positive marital
relationships offering the greatest degree of protection against loneliness.
Independent risk factors for loneliness included male gender, physical health
symptoms, chronic work and/or social stress, small social network, lack of a
spousal confidant, and poor-quality social relationships.
Discussion. Longitudinal research is needed to evaluate the causal role of
social structural and proximal factors in explaining changes in loneliness.
Loneliness risk factors, Health, Chronic stress, Social network, Relationship
quality
Issue Section:
Journal of Gerontology: Social Sciences
LONELINESS is the painful feeling of social isolation that accompanies perceived
deficiencies in the number or quality of one's social relationships (Peplau &
Perlman, 1982). Perceptions are critical to this definition: People can live
rather solitary lives and not feel lonely, or they can have many social
relationships and nevertheless feel lonely. Consequently, loneliness is more
closely related to the perceived quality than the quantity of social
relationships (Pinquart & Sörensen, 2003).
Feelings of loneliness have serious consequences for health outcomes.
Prospective studies have shown that loneliness predicts depressive symptoms
(Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006; Heikkinen & Kauppinen,
2004), mental health and cognition (Wilson et al., 2007), nursing home admission
(Russell, Cutrona, de la Mora, & Wallace, 1997), and mortality in older adults
(Penninx et al., 1997). Loneliness is also a potent risk factor for suicidal
ideation (Stravynski & Boyer, 2001) and alcoholism (Akerlind & Hörnquist, 1992).
Loneliness is sometimes confused with depression (i.e., depressive symptoms) and
poor social support. It is clear, however, that these related constructs are
theoretically and empirically distinct (Cacioppo, Hawkley, et al., 2006;
Cacioppo, Hughes, et al., 2006; Rook, 1987; Russell, 1996). Furthermore,
loneliness, rather than depressive symptomatology or social support, uniquely
predicts elevated blood pressure (Hawkley, Masi, Berry, & Cacioppo, 2006), a
number of risk factors for cardiovascular disease (Caspi, Harrington, Moffitt,
Milne, & Poulton, 2006), poor sleep (Cacioppo et al., 2002), greater morning
rise in cortisol (Adam, Hawkley, Kudielka, & Cacioppo, 2006), and alterations in
gene transcription control pathways that favor heightened inflammation (Cole et
al., 2007). Moreover, prior research on loneliness suggests it is related to
faster aging and physiological decline (Hawkley & Cacioppo, 2007). Loneliness
thus appears to be a unique and underappreciated psychosocial risk factor of
clear relevance for those concerned about age-related health problems. Given the
importance of loneliness for health, the goal of the present study was to
develop a more thorough understanding of risk factors for loneliness in a
middle-aged population. A parallel goal was to assess the extent to which
relationship quality is the final determinant of feelings of loneliness.
Our conceptual model is a filtration model in which distal socially ascribed
characteristics operate through more proximal factors to influence loneliness
(cf. Berkman & Glass, 2000). Distal factors do not “cause” proximal factors but
are shaped by those factors to impact outcomes. In our model, distal demographic
factors (age, gender, race/ethnicity) operate through structural factors
(income, education) and in turn through health, social roles, and
stress—proximal factors that are more directly associated with social network
size and relationship quality. Our underlying assumption is that the number and
frequency of social contacts, and especially the quality of social
relationships, are the ultimate arbiters of the influence of distal factors on
loneliness. To the extent that distal factors filter down to affect social
contacts and relationship quality, they will have an impact on loneliness.
For instance, we posit that an association between age and loneliness, if it
exists, will be attributable to fewer social contact opportunities that arise
because of age-related health problems and functional limitations, and/or
because of age- or health-related reductions in social roles that previously
afforded social contact opportunities (e.g., retirement, widowhood). Poor health
and physical limitations are associated with increased loneliness (Pinquart &
Sörensen, 2003), and we examined whether diminished social contacts and poor
relationship quality help explain loneliness differences between healthy and
unhealthy adults. Similarly, racial/ethnic differences in loneliness (Adams,
Kaufman, & Dressler, 1989) are posited to be attributable to the disadvantaged
position of minority populations on key social and economic dimensions. In U.S.
society, educational and economic advantages favor Whites over Blacks and
Hispanics. Low levels of education and income are associated with higher levels
of loneliness (Savikko, Routasalo, Tilvis, Strandberg, & Pitkälä, 2005) and also
tend to lead to worse health (Adler et al., 1994). In addition, fewer financial
resources means less opportunity to engage in commercial activities that could
increase social contacts (e.g., gym memberships). Indeed, low socioeconomic
status has been associated with smaller, less diverse social networks
(Antonucci, Ajrouch, & Janevic, 1999). Our data provided an opportunity to
examine the degree to which effects of race/ethnicity on loneliness are mediated
by one or more of the proximal pathways. Similar pathways may operate in
explaining gender differences in loneliness. However, on the basis of prior
literature showing small and inconsistent gender effects (Borys & Perlman,
1985), we do not hypothesize gender differences in loneliness.
Loneliness may differ as a function of social roles. Being married, for example,
assures an individual of at least one social connection, usually a relatively
potent one in terms of protection against loneliness (Pinquart & Sörensen,
2003). Being employed and having opportunities to establish social ties with
coworkers, clients, and supervisors or supervisees are important means of
feeling socially connected and can foster feelings of belonging that are
effective in staving off loneliness (Hawkley, Browne, & Cacioppo, 2005).
Similarly, being a member of a group (e.g., neighborhood society, athletic team,
political organization, bridge club) and/or a regular church attender can foster
a sense of belonging as well as increase opportunities for the development of
friendships and supportive relationships that diminish the likelihood or level
of loneliness. We tested the extent to which associations between social
structural factors and loneliness are explained by social roles that influence
loneliness through their impact on social network size and relationship quality.
The stress of under- or unemployment, inadequate financial resources, and
marital or family conflict have been associated with increased loneliness
(Jones, 1992; Salamah, 1991; Segrin, 1999). These chronic stressors are more
prevalent in socioeconomically disadvantaged populations (Baum, Garofalo, &
Yali, 1999) and may provide a pathway through which socioeconomic status
influences loneliness. Our data allowed us to examine the extent to which life
stress influences the transduction of distal factors to affect network size,
relationship quality, and, ultimately, loneliness.
Prior research has focused on associations between individual predictors and
loneliness, and, with few exceptions (e.g., De Jong Gierveld, 1987; Mullins,
Elston, & Gutkowski, 1996), none have taken a multivariate approach to
relationships among predictors of loneliness. The goal of the present study was
to use a multivariate approach to examine a cascade of factors from demographic
characteristics, education and income, health, social roles, life events and
chronic stress, and social network size to social relationship quality and to
determine the impact of distal and more proximal factors on loneliness in an
urban, population-based sample of middle-aged adults.
METHODS
PARTICIPANTS
Data for this study were collected in the first year of the Chicago Health,
Aging, and Social Relations Study (CHASRS), a longitudinal, population-based
study of non-Hispanic White, African American, and non-Black Latino American
persons born between 1935 and 1952 and living in Cook County, Illinois. The
sample was selected using a multistage probability design in which the first
stage involved identifying a subset of households estimated to have high
probability of containing at least one adult aged 50 to 65 years (24% of the
total frame). A stratified, equal-probability-of-selection sample was drawn from
this subset. The three strata were (a) households from census tracts in which at
least 80% of the residents were African American, (b) households for which the
associated surname was identified by the U.S. Census Bureau as “Hispanic,” and
(c) all remaining households. The second stage involved selecting one
age-eligible individual per household and screening selected individuals to
include only those who belonged to one of the three racial/ethnic groups of
interest and who were sufficiently ambulatory to come to the University of
Chicago and participate in the study. A quota sampling strategy was used at both
the household and individual levels to achieve an approximately equal
distribution of participants across the six gender by racial/ethnic group
combinations. Response rates approached 45% overall, an impressive rate given
that participation in our study involved predominantly working adults spending
an entire day at the university. The distribution of our sample on a number of
characteristics (e.g., marital status, working status, self-rated health)
compared quite closely to that obtained from the national population-based
Health and Retirement Study. Participants in our sample tended to be better
educated than the target population as a whole.
The final sample size for Year 1 of CHASRS was 229. Participants were paid $90
for completing the day-long laboratory protocol.
PROCEDURES
Participants arrived at the laboratory between 8 a.m. and 9 a.m. for
approximately 8 hr of testing, including informed consent, questionnaires,
interviews, lunch, and a cardiovascular protocol. This report uses self-report
data from questionnaires and interviews.
MEASURES
Table 1 provides sample characteristics and descriptive data for each of the
measures.
REVISED UCLA LONELINESS SCALE
The Revised UCLA (University of California, Los Angeles) Loneliness Scale is a
20-item validated measure of general loneliness and feelings of social isolation
(Russell, Peplau, & Cutrona, 1980). Examples of the items are “I lack
companionship” and “There are people I can talk to.” These items assess the
perception that one lacks companionship or has people to talk to (for example)
and are to be distinguished from other measures that ask participants how many
companions they have and how often they talk to others (see measures of social
contact). Cronbach's alpha across all 20 items was.91 in our sample. The
response scale ranges from 1 (never) to 4 (often), and the range of possible
scores is 20 to 80, with higher scores signifying greater loneliness.
DEMOGRAPHIC VARIABLES
We measured age in years. Binary variables indicated gender (male was the
reference category) and Black and Hispanic race/ethnicity (non-Hispanic White
was the reference category).
SOCIOECONOMIC STATUS
We indexed education as having obtained a high school diploma or its equivalent.
Participants reported household income in 12 categories (less than $5,000 to
more than $200,000); we used the natural log-transformed category median in
analyses to minimize positive skew in the distribution.
HEALTH
We assessed chronic conditions (e.g., diabetes, stroke) by self-report
questionnaire and used the Charlson Comorbidity Index (Charlson, Pompei, Ales, &
MacKenzie, 1987) to obtain a measure of number of chronic conditions weighted by
severity (Katz, Chang, Sangha, Fossel, & Bates, 1996). We resolved a positive
skew in this distribution by creating four categories of chronic conditions (0 =
none, 1 = 1, 2 = 2–3, and 3 = >3 conditions). Symptoms were represented by a
count of the number of symptoms (e.g., frequent headaches, joint pain)
experienced in the past year. We summed restrictions in activities of daily
living (Mahoney & Barthel, 1965) and, because a large portion of the sample
reported no restrictions, dichotomized activity of daily living restrictions to
contrast some with none.
SOCIAL ROLES
Married indicated participants who were currently married or living with a
partner. We binary-coded retired status and “other” employment statuses (full-
and part-time employment was the reference category). Following the procedure
employed by Cohen, Doyle, Skoner, Rabin, and Gwaltney (1997), we coded regular
church attendance as present if participants attended at least twice a month. We
coded group membership (e.g., charity organizations, social clubs) as present if
one or more groups involved social interactions at least every 2 weeks.
STRESS EXPOSURE
Participants used a 51-item checklist (based on the revised Social Readjustment
Rating Scale; Hobson et al., 1998) to endorse life events that had occurred in
the prior 12 months. We summed life events, counting multiple occurrences of the
same event separately and omitting health-related events to avoid redundancy
with measures of health. We used a natural log transformation to correct
positive skew in the distribution of the life event count.
We assessed chronic stress exposure by using a series of questions about the
presence of stress in eight domains (e.g., financial, employment,
marital/romantic; Turner, Wheaton, & Lloyd, 1995). We coded chronic stress
exposure as present or absent in each domain. We also conducted analyses using
the count of endorsed statements within each stress domain. Results did not
differ substantively from those reported here, and they are available from
Louise C. Hawkley upon request.
SOCIAL CONTACT
Participants were asked to identify, in three separate categories, individuals
“[with whom] you most often discuss matters important to you,” “who have been
very demanding of you, or who have caused you a lot of stress or anxiety,” and
“who have been very supportive of you during the past year.” Participants also
identified the roles played by each of these individuals (e.g., spouse, parent,
child, friend, neighbor, coworker, relative). Preliminary analyses indicated
that the number of demanding individuals contributed to lower loneliness scores
beyond what was predicted by the number of individuals in the two positive
network categories, so we summed individuals identified in all three categories
to create a measure of social network size. Frequency of interaction with each
network member ranged from less than once a year to every day. We averaged
median response categories endorsed across all network members to create a
measure of interaction frequency with network members.
RELATIONSHIP QUALITY
We coded as having a spousal confidant participants who identified a spouse as
someone with whom they discussed important matters or someone who was a source
of support. In addition, we averaged ratings of enjoyment and satisfaction with
each of the identified network members to create a measure of overall network
satisfaction (range = 0–4, or not at all to extremely).
CONCEPTUAL MODEL AND DATA ANALYSIS STRATEGY
The conceptual model guiding this research is that distal influences on
loneliness tend to operate through more proximal factors to explain individual
differences in loneliness. The most distal factors we consider are socially
ascribed status indicators (age, gender, race/ethnicity), and we then consider a
series of more proximal factors that may play a role in explaining loneliness
differences. We group factors into conceptual categories and test them in a
sequence that moves from demographic characteristics, socioeconomic
characteristics, health, social roles, stress exposure, and social contact
opportunities to social relationship quality. The sequence is not intended to
represent a causal sequence but is meant to test the degree to which distal
factors operate through more proximal factors, and ultimately social network
size and relationship quality, to influence loneliness.
We conducted ordinary linear regression models in accordance with the conceptual
model to examine the independent predictive capacity of each measure within a
conceptual block of related variables, and then the independent predictive
capacity of measures across blocks of predictor variables. We set statistical
significance at α =.05, two-tailed, unless the test of an association was a
replication of an effect reported in prior literature, in which case we used a
statistical criterion of α =.05, one-tailed. One-tailed tests therefore applied
to evaluation of the effects of high school diploma, household income, and
marital status. At each step of the modeling sequence, we eliminated
nonsignificant variables before proceeding to the next block. This strategy
reduced the likelihood of overfitting the models with many nonsignificant
variables and permitted identification of the empirically important measures
within a block of conceptually related measures.
We first conducted the entire sequence of models bearing the cost of missing
data (i.e., for the most comprehensive model, 26 cases were missing data on at
least one variable). An analysis of variance contrasting those with missing
versus complete data on all variables revealed that participants with missing
data were less likely to have a high school diploma (57% vs 81%; p <.01) and had
higher loneliness scores (42.8 vs 35.1; p <.01) than those with complete data.
In addition, participants with missing data were more likely to be Hispanic
(21.2% were missing data on at least one of the predictor variables) than White
(4.9% with missing data), χ2(1) = 9.133, p <.01 (N = 148); whereas Blacks (12.3%
with missing data) did not differ from Hispanics or Whites in the prevalence of
missing data (ps >.05). We imputed values for missing data by regressing
variables on race/ethnicity, high school diploma, and loneliness scores
(categorized into quintiles). We repeated the entire model sequence using
imputed data to maximize statistical power (n = 225). Results did not differ
substantively from the results obtained using only those cases with complete
data, and we report here results obtained using imputed data. Results from the
regression model sequence that used only cases with complete data are available
from Louise C. Hawkley upon request.
RESULTS
Table 2<--CO?1--> displays results of the modeling sequence (i.e.,
unstandardized coefficients and standard errors).
DEMOGRAPHIC CHARACTERISTICS
As shown in Table 2, Model 1A, Hispanics were lonelier, and Blacks tended to be
lonelier, than Whites. Age and gender were not associated with loneliness in our
sample (ps >.05), although women tended to be less lonely than men (B = −2.49,
SE = 1.29, p =.055). Race/ethnicity explained 3% of the variance in loneliness
(Model 1B).
SOCIOECONOMIC CHARACTERISTICS
In combination, household income and high school education explained a
substantial portion of the race/ethnicity variance in loneliness, approximately
halving the coefficients for Black and Hispanic race/ethnicity. In Model 2B,
high school diploma and household income combined to explain 7% of the variance
in loneliness.
PHYSICAL AND FUNCTIONAL HEALTH
A preliminary model indicated that the Charlson Comorbidity Index showed a
nonsignificant positive association with loneliness (B = 1.27, SE = 0.80, p
>.1), but the addition of number of symptoms and activity of daily living
restrictions to the model showed that symptoms largely explained the comorbidity
effect. Health measures also reduced the effect of household income, suggesting
that health differences explain part of the association between income and
loneliness. In Model 3B, a high school diploma and health symptoms combined to
explain 14% of the variance in loneliness.
SOCIAL ROLES
In Model 4A, only being a group member was independently associated with lower
loneliness. Being married, retired or unemployed, or a regular church attender
was not associated with loneliness. Being a group member explained an additional
2% of the variance in loneliness in Model 4B relative to Model 3B.
STRESS EXPOSURE
In Model 5A, chronic marital/romantic stress and chronic social stress were
independently associated with loneliness. Ancillary analyses revealed that the
influences of diploma and symptoms on loneliness were at least partly explained
by chronic stress in everyday life. Relative to Model 5A, the effect sizes for
marital stress and social stress (i.e., not having the desired time or company
to enjoy social activities) increased in Model 5B when the other chronic stress
measures were eliminated, a pattern that reflects shared variance among these
stress measures. Variables retained in Model 5B combined to explain 27% of the
variance in loneliness, an 11% increase in variance explained relative to Model
4B.
SOCIAL CONTACT
In Model 6A, only network size was significantly associated with loneliness,
such that each additional person in the network reduced the loneliness score by
approximately 1 point on the 60-point scale. The inclusion of network size
resulted in a 43% reduction in the coefficient for group membership relative to
Model 5B. Ancillary analyses confirmed that group members had significantly
larger social networks than non-group members, M's = 7.9 (SD = 2.1) vs. 6.7 (SD
= 2.4), p <.01. In addition, the association of loneliness with high school
diploma was substantively reduced in Model 6A relative to Model 5B. Ancillary
analyses showed that those with a high school diploma had larger social networks
than their less educated counterparts (M = 7.5, SD = 2.3, vs M = 6.4, SD = 2.2;
p <.01), indicating that education-related differences in loneliness are at
least partially attributable to differences in network size. Significant
predictors retained in Model 6B explained an additional 5% of the variance in
loneliness relative to Model 5B.
RELATIONSHIP QUALITY
In Model 7A, having a spousal confidant was significantly associated with lower
levels of loneliness, but being married to a non-confidant was not associated
with loneliness and was no more protective than not being married at all. Being
satisfied with network relationships had an additional protective effect
independent of network size and marital relationship quality. Adding the two
relationship quality measures diminished the size of the adverse effect of
chronic marital and social stress relative to Model 6B, consistent with the idea
that chronic stress in these domains reflects, in part, qualitatively inferior
social relationships. In addition, adding the relationship quality measures
further reduced the effects of having a high school diploma on loneliness,
indicating that positive social relationships help to explain why those with a
high school diploma are at decreased risk for loneliness. All predictors in
Model 7A were statistically significant and combined to explain 37% of the
variance in loneliness.
We constructed a final model (Model 8) in which all predictors were returned to
the model to examine evidence that eliminated variables, if retained in the
model, might release the effect of other variables that would otherwise go
unnoticed (i.e., statistical suppression; MacKinnon, Krull, & Lockwood, 2000).
To avoid redundant predictors, spousal confidant and spousal non-confidant
replaced marital status as a social role, and the unmarried continued to serve
as the reference category. In this model, the effect of having a high school
diploma was further reduced, the effect of marital stress on loneliness was
sizably diminished, and work stress emerged as a predictor of loneliness.
Ancillary analyses revealed that although work stress was equally likely in
married as in unmarried individuals, work stress had a more marked effect on
loneliness among the unmarried than the married, especially the married with a
spousal confidant, indicating that good-quality marital relationships mask the
impact of chronic work stress on loneliness.
We conducted supplementary analyses that reintroduced gender as a predictor
variable in each of Models 2 through 7. Results showed that women were
significantly less lonely than men in this sample, and this was true from Model
3 through Model 7. Follow-up analyses showed that women reported significantly
more health symptoms than men (Mwomen = 12.81, SD = 10.96; Mmen = 10.88, SD =
8.54) and that the gender difference in loneliness emerged only when health
symptoms were held constant. In other words, the effect of symptoms on
loneliness suppressed the gender difference in loneliness.
We conducted additional supplementary analyses for each of Models 2 through 7 to
examine whether the predictor variables operate in a similar manner in men and
women. The full gender interaction models revealed only one instance of men and
women differing in an association between predictor and loneliness.
Specifically, in Model 5, women exhibited a larger association between life
events and loneliness than did men (Bfemale × life events = 3.57, p <.05;
Bfemale = −8.35, p <.01; Blife events = −0.51, p >.6) in a model that adjusted
for high school diploma, health symptoms, group membership, and chronic marital
and social stress. The coefficient for this interaction term was reduced only
slightly when added to the final model, Model 8 (Bfemale × life events = 2.58, p
=.06). Otherwise, no gender interaction term was significant (ps >.05).
DISCUSSION
We formulated our conceptual model under the assumption that explanations for
individual differences in loneliness are improved by considering the filtering
of distal social structural factors through more proximal factors that influence
loneliness to the extent that social network size and especially social
relationship quality are affected. Our results provide evidence consistent with
this filtering process, and the outcome is a relatively comprehensive profile of
risk factors for loneliness in an urban setting in the United States.
Specifically, our final model shows that men, people who are unhealthy, people
undergoing chronic work stress, people unable to satisfy a desire to engage in
social activities with others, people in small social networks, and people
suffering from poor-quality relationships in marriage and in their broader
social networks are likely to be disproportionately represented among lonely
individuals. These results support our hypothesis that social network size and
particularly relationship quality are key determinants of loneliness.
Viewing this in another way, we see that factors representing latent social
opportunity (i.e., education, income, health) influence loneliness to the degree
that they affect manifest social relationships (quantity and quality), and this
translation from latent to manifest social relationships is shaped by factors
that are potentially toxic to social relationships (e.g., chronic stress). In
combination, the distal and proximal factors in our model explained
approximately 37% of the variance in loneliness in our sample.
Our final set of risk factors does not rule out a role for race/ethnicity and
socioeconomic status in predicting loneliness. Rather, our results showed higher
levels of loneliness in Hispanics and, to a lesser degree, in Blacks in this
urban population-based sample of middle-aged adults, but we found that these
racial/ethnic differences were explained in large part by education and income
differences. Similarly, household income was associated with less loneliness,
but this effect was explained by its association with better health, a proximal
factor that protected against loneliness. In addition, education proved to be a
potent protective factor against loneliness, and its effect was largely
explained by proximal indicators of less chronic stress, a larger network size,
and good-quality marital and social relationships. These findings indicate that
possession of a high school diploma may be a surrogate measure for social class,
marital stability, self-esteem, and other factors that could enhance the
likelihood of success in various life domains, including social relationships.
Extending prior research (Pinquart & Sörensen, 2003), we found that being
married was negatively associated with loneliness, but only if the marital
partner served as a confidant. If the spouse was not a confidant, being married
was no more protective against loneliness than not being married. These results
correspond to observations that intimacy and communication in marriage, but not
agreement or marital satisfaction per se, protect against loneliness (Olson &
Wong, 2001). In addition, having a spousal confidant minimized the effect of
chronic work stress on loneliness. This is consistent with research showing that
adequate and appropriate social support from a spouse reduces perceptions of
stress (Dehle, Larsen, & Landers, 2001). Chronic work stress took its toll in
feelings of loneliness among the middle-aged and older adults in our sample who
lacked a spouse or a spousal confidant.
Having a spousal confidant also significantly reduced the effect of chronic
marital stress on loneliness. In other words, a close marital relationship may
diminish the impact of marital stress on feelings of loneliness. Research on
commitment in close relationships supports this conjecture. Committed partners
behave toward each other in pro-relationship ways, including a greater
willingness to sacrifice for the good of the relationship, and these acts
enhance their trust in each other. As trust increases, marital stresses and
strains (e.g., conflictual interactions, disagreements on financial priorities)
are less likely to foster reactionary behavior and are more likely to be
accommodated for the sake of the relationship (Wieselquist, Rusbult, Foster, &
Agnew, 1999). What might have been a significant source of stress is instead
transformed into an opportunity for greater interdependence and commitment, and
the consequence is protection against feelings of loneliness.
Having a large social network was associated with less loneliness independent of
the overall quality of the social relationships with network members. Given that
loneliness is most robustly associated with qualitative aspects of social
relationships (De Jong Gierveld, 1987; Pinquart & Sörensen, 2003), the fact that
network size continued to exhibit an inverse association with loneliness is
quite remarkable and suggests that participants' ability to create and/or avail
themselves of opportunities to form social connections is itself a potentially
protective mechanism against feelings of loneliness. This conclusion is
supported by the additional finding that chronically undesirable or inadequate
opportunities to socialize were associated with greater loneliness independent
of existing network size. It is possible that a larger network offers a better
cushion of good-quality relationships to counter the effects of any one or more
network members who are demanding and burdensome. Alternatively, even burdensome
network members may be better than no or fewer network members in protecting
against loneliness. Ancillary analyses indicated support for the latter
hypothesis: Burdensome and close network members contributed additively to lower
levels of loneliness. Additional research is needed to replicate this finding
and examine the types (i.e., children, friends, spouse) and qualities (i.e.,
negative and positive) of these relationships more closely. For instance,
ambivalent feelings about a relationship partner (i.e., high feelings of
positivity and negativity) are toxic to the relationship (Holt-Lunstad, Uchino,
Smith, & Hicks, 2007) and may be even more potent predictors of loneliness than
indifferent feelings (i.e., moderate levels of positivity and negativity) or
pure negativity.
Our finding, in the final model and in supplementary analyses of Models 3
through 7, that women were significantly less lonely than men, is consistent
with some prior research (De Jong Gierveld, Kamphuis, & Dykstra, 1987; Mullins,
Tucker, Longino, & Marshall, 1989; Pinquart & Sörensen, 2003) but is at odds
with other reports of either no gender differences or women being lonelier than
men (Borys & Perlman, 1985; De Jong Gierveld, 1987; Mullins et al., 1996). We
should note that our gender difference would not have been evident had we not
included health symptoms in the model. Our results suggest that unmeasured
and/or untested factors (e.g., health) and less comprehensive statistical
modeling (i.e., no checks for suppressive effects) may bias estimates of gender
differences in loneliness in unexpected ways and contribute to gender difference
discrepancies in the literature. An alternative explanation that has been
offered for discrepant gender effects is that gender differences in loneliness
are observed only when respondents are asked to rate how “lonely” they feel, in
which case women report being lonelier than men because they may be less
reluctant to admit having these feelings (Borys & Perlman, 1985). The Revised
UCLA Loneliness Scale does not ask blatant questions about “loneliness,” and our
finding of greater loneliness in men may be attributable, in part, to this
feature of our loneliness measure. Longitudinal studies that examine predictors
of change in loneliness and differences in trajectories of loneliness over time
will enhance researchers' understanding of factors that contribute to gender
differences in loneliness. For instance, loneliness differences between men and
women at one point in time may be attributable to gender differences in the
timing of precursors to loneliness that preceded the present loneliness
assessment (e.g., divorce and/or widowhood that occurred earlier in one gender
than the other, or gender differences in years since retirement or since changes
in household income). This hypothesis awaits testing in future longitudinal
research that examines changes in loneliness as a function of changes in life
circumstances and psychosocial status in the CHASRS.
Independent of gender differences in loneliness, we observed one significant
gender difference in the correlates of loneliness: Number of life events had a
larger association with loneliness in women than in men. This single significant
gender interaction speaks to Tornstam's (1992) argument that gender differences
in loneliness may be attributable to gender differences in reactions to stress.
It warrants replication using a larger sample of men and women, a closer
examination of the types of life events experienced by men and women, and
subjective assessments of stress intensity. No other gender interaction term was
statistically significant in our sample.
One concern of the present study is its relatively small sample. However, the
CHASRS sample is a representative population-based sample of middle-aged and
older adults that had sufficient statistical power to detect effects in spite of
its size. We observed no nonsignificant effects that we would have expected to
be significant based on prior literature. The limited size of our sample is
offset by its rich set of measures (e.g., comprehensive measures of social
relationship quantity and quality) that surpasses what is possible in large,
national surveys. Whereas large-scale surveys can detect small effects of social
structural factors, the strength of our smaller scale study is its broad and
deep data that permit a thorough examination of the processes through which
social structural factors operate to influence loneliness.
In summary, our multivariate approach has extended prior research and enhanced
our understanding of loneliness by (a) adopting a filtration model that traces
pathways through which distal factors may operate, (b) considering a wider range
of loneliness risk factors than have been considered heretofore, and (c)
employing a population-based sample of middle-aged and older adults. Now that we
have shown that male gender, health, chronic stress, network size, and spousal
and broader social relationship quality help to explain cross-sectional
associations between social structural factors and loneliness, it remains to be
seen the extent to which distal and proximal factors help to ameliorate any
tendencies toward increasing loneliness over time in the aging CHASRS sample.
Feelings of loneliness appear to hasten physiological decline (Hawkley &
Cacioppo, 2007), and it is therefore important to identify harbingers of
loneliness that, with appropriate intervention, could minimize not only the
psychological distress but also the physiological decline associated with felt
deficits in social connections.
Decision Editor: Kenneth F. Ferraro, PhD
Table 1.
Measures of Loneliness and Covariates, 2002 Chicago Health, Aging, and Social
Relations Study.
Measure . N . Statistic . Revised UCLA Loneliness Scale, M (SD) 225 36.0
(9.8) Age in years, M (SD) 229 57.4 (4.5) Female, % 229 52.4 Ethnicity,
% 229 White 35.8 Black 35.4 Hispanica 28.8 High school diploma
or equivalent, % 229 77.7 Household
incomeb 216 ≤$20,000 15.7 $20–50,000 33.8 $50–100,000 35.6 >$100,000 14.8 Charlson
Comorbidity Index,
% 229 0 67.7 1 17.9 2–3 11.8 >3 2.6 Number of symptoms, M
(SD) 227 14.0 (10.3) Activity of daily living restrictions, % 224 30.8 Marital
status, % 229 Married/living with a
partner 61.6 Widowed 9.2 Divorced/separated 23.6 Never
married 5.7 Employment status, % 229 Work full or part
time 58.5 Retired 24.0 Other 17.5 Church attendance, % 226 <2–3
times a month 43.8 ≥2–3 times a month 56.2 Group member with interactions
at least every 2 weeks, % 229 20.5 Life event count, M (SD) 227 6.4
(7.1) Chronic stress exposure, % 221 General 81.0 Money and financial
matters 73.8 Employment 78.7 Love and marriage 62.4 Family and
children 55.5 Social life and
recreation 64.3 Health 70.1 Residence 52.9 Number of network members,
M (SD) 227 7.3 (2.3) Median interaction frequency with network members, M
(SD) 226 6.5 (0.8) Marital relationship quality, % of those married or living
with a partner 141 Spousal non-confidant 22.7 Spousal
confidant 77.3 Network relationship satisfaction, M (SD) 227 2.9 (0.5)
Measure . N . Statistic . Revised UCLA Loneliness Scale, M (SD) 225 36.0
(9.8) Age in years, M (SD) 229 57.4 (4.5) Female, % 229 52.4 Ethnicity,
% 229 White 35.8 Black 35.4 Hispanica 28.8 High school diploma
or equivalent, % 229 77.7 Household
incomeb 216 ≤$20,000 15.7 $20–50,000 33.8 $50–100,000 35.6 >$100,000 14.8 Charlson
Comorbidity Index,
% 229 0 67.7 1 17.9 2–3 11.8 >3 2.6 Number of symptoms, M
(SD) 227 14.0 (10.3) Activity of daily living restrictions, % 224 30.8 Marital
status, % 229 Married/living with a
partner 61.6 Widowed 9.2 Divorced/separated 23.6 Never
married 5.7 Employment status, % 229 Work full or part
time 58.5 Retired 24.0 Other 17.5 Church attendance, % 226 <2–3
times a month 43.8 ≥2–3 times a month 56.2 Group member with interactions
at least every 2 weeks, % 229 20.5 Life event count, M (SD) 227 6.4
(7.1) Chronic stress exposure, % 221 General 81.0 Money and financial
matters 73.8 Employment 78.7 Love and marriage 62.4 Family and
children 55.5 Social life and
recreation 64.3 Health 70.1 Residence 52.9 Number of network members,
M (SD) 227 7.3 (2.3) Median interaction frequency with network members, M
(SD) 226 6.5 (0.8) Marital relationship quality, % of those married or living
with a partner 141 Spousal non-confidant 22.7 Spousal
confidant 77.3 Network relationship satisfaction, M (SD) 227 2.9 (0.5)
Notes: UCLA = University of California, Los Angeles.
aThe majority of these 66 individuals were Mexican (72%), with the remaining
individuals representing a wide range of ethnicities (e.g., Puerto Rican, Cuban,
Chilean, Colombian, Dominican).
bHousehold income was reported in 12 categories that were collapsed to 4
categories here for summary purposes.
Open in new tab
Table 1.
Measures of Loneliness and Covariates, 2002 Chicago Health, Aging, and Social
Relations Study.
Measure . N . Statistic . Revised UCLA Loneliness Scale, M (SD) 225 36.0
(9.8) Age in years, M (SD) 229 57.4 (4.5) Female, % 229 52.4 Ethnicity,
% 229 White 35.8 Black 35.4 Hispanica 28.8 High school diploma
or equivalent, % 229 77.7 Household
incomeb 216 ≤$20,000 15.7 $20–50,000 33.8 $50–100,000 35.6 >$100,000 14.8 Charlson
Comorbidity Index,
% 229 0 67.7 1 17.9 2–3 11.8 >3 2.6 Number of symptoms, M
(SD) 227 14.0 (10.3) Activity of daily living restrictions, % 224 30.8 Marital
status, % 229 Married/living with a
partner 61.6 Widowed 9.2 Divorced/separated 23.6 Never
married 5.7 Employment status, % 229 Work full or part
time 58.5 Retired 24.0 Other 17.5 Church attendance, % 226 <2–3
times a month 43.8 ≥2–3 times a month 56.2 Group member with interactions
at least every 2 weeks, % 229 20.5 Life event count, M (SD) 227 6.4
(7.1) Chronic stress exposure, % 221 General 81.0 Money and financial
matters 73.8 Employment 78.7 Love and marriage 62.4 Family and
children 55.5 Social life and
recreation 64.3 Health 70.1 Residence 52.9 Number of network members,
M (SD) 227 7.3 (2.3) Median interaction frequency with network members, M
(SD) 226 6.5 (0.8) Marital relationship quality, % of those married or living
with a partner 141 Spousal non-confidant 22.7 Spousal
confidant 77.3 Network relationship satisfaction, M (SD) 227 2.9 (0.5)
Measure . N . Statistic . Revised UCLA Loneliness Scale, M (SD) 225 36.0
(9.8) Age in years, M (SD) 229 57.4 (4.5) Female, % 229 52.4 Ethnicity,
% 229 White 35.8 Black 35.4 Hispanica 28.8 High school diploma
or equivalent, % 229 77.7 Household
incomeb 216 ≤$20,000 15.7 $20–50,000 33.8 $50–100,000 35.6 >$100,000 14.8 Charlson
Comorbidity Index,
% 229 0 67.7 1 17.9 2–3 11.8 >3 2.6 Number of symptoms, M
(SD) 227 14.0 (10.3) Activity of daily living restrictions, % 224 30.8 Marital
status, % 229 Married/living with a
partner 61.6 Widowed 9.2 Divorced/separated 23.6 Never
married 5.7 Employment status, % 229 Work full or part
time 58.5 Retired 24.0 Other 17.5 Church attendance, % 226 <2–3
times a month 43.8 ≥2–3 times a month 56.2 Group member with interactions
at least every 2 weeks, % 229 20.5 Life event count, M (SD) 227 6.4
(7.1) Chronic stress exposure, % 221 General 81.0 Money and financial
matters 73.8 Employment 78.7 Love and marriage 62.4 Family and
children 55.5 Social life and
recreation 64.3 Health 70.1 Residence 52.9 Number of network members,
M (SD) 227 7.3 (2.3) Median interaction frequency with network members, M
(SD) 226 6.5 (0.8) Marital relationship quality, % of those married or living
with a partner 141 Spousal non-confidant 22.7 Spousal
confidant 77.3 Network relationship satisfaction, M (SD) 227 2.9 (0.5)
Notes: UCLA = University of California, Los Angeles.
aThe majority of these 66 individuals were Mexican (72%), with the remaining
individuals representing a wide range of ethnicities (e.g., Puerto Rican, Cuban,
Chilean, Colombian, Dominican).
bHousehold income was reported in 12 categories that were collapsed to 4
categories here for summary purposes.
Open in new tab
Table 2.
Unstandardized Coefficients (SE) From Regression of Loneliness on Covariates in
Linear Regression Models (N = 225).
. Model 1: Demographics
--------------------------------------------------------------------------------
. . Model 2: Socioeconomic Status
--------------------------------------------------------------------------------
. . Model 3: Health and Functioning
--------------------------------------------------------------------------------
. . Covariate . 1A . 1B . 2A . 2B . 3A . 3B . Age −0.10
(0.15) Female −2.49 (1.29) Race/ethnicity: White Black 2.79
(1.53)† 2.72 (1.53)† 1.24 (1.58) Hispanic 4.15 (1.65)* 4.41 (1.61)* 2.69
(1.67) Diploma −3.71 (1.61)* −4.36 (1.56)* −4.57 (1.49)* −5.03
(1.48)* Household income −1.55 (0.73)* −1.73 (0.71)* −1.00 (0.70) Chronic
conditions 0.02 (0.82) Number of symptoms 0.25 (0.07)* 0.30
(0.06)* Activity of daily living restrictions 2.07
(1.46) Spouse/partner Work role: Working Retired Not
working Regular church attender Group member Life event
count Chronic stressors General Money and
financial Employment Love and marriage Family and
children Social life and
recreation Health Residence Network
size Frequency of contact Marital relationship: Not
married Spousal non-confidant Spousal
confidant Network satisfaction R2 .05 .03 .08 .07 .16 .14
. Model 1: Demographics
--------------------------------------------------------------------------------
. . Model 2: Socioeconomic Status
--------------------------------------------------------------------------------
. . Model 3: Health and Functioning
--------------------------------------------------------------------------------
. . Covariate . 1A . 1B . 2A . 2B . 3A . 3B . Age −0.10
(0.15) Female −2.49 (1.29) Race/ethnicity: White Black 2.79
(1.53)† 2.72 (1.53)† 1.24 (1.58) Hispanic 4.15 (1.65)* 4.41 (1.61)* 2.69
(1.67) Diploma −3.71 (1.61)* −4.36 (1.56)* −4.57 (1.49)* −5.03
(1.48)* Household income −1.55 (0.73)* −1.73 (0.71)* −1.00 (0.70) Chronic
conditions 0.02 (0.82) Number of symptoms 0.25 (0.07)* 0.30
(0.06)* Activity of daily living restrictions 2.07
(1.46) Spouse/partner Work role: Working Retired Not
working Regular church attender Group member Life event
count Chronic stressors General Money and
financial Employment Love and marriage Family and
children Social life and
recreation Health Residence Network
size Frequency of contact Marital relationship: Not
married Spousal non-confidant Spousal
confidant Network satisfaction R2 .05 .03 .08 .07 .16 .14
. Model 4: Social Roles . . Model 5: Stress Exposure . . Model 6: Social
Contacts . . Covariate . 4A . 4B . 5A . 5B . 6A . 6B
. Age Female Race/ethnicity:
White Black Hispanic Diploma −4.23 (1.50)* −4.38
(1.49)* −3.98 (1.40)* −3.80 (1.40)* −2.89 (1.36)* −3.19 (1.35)* Household
income Chronic conditions Number of symptoms 0.28 (0.06)* 0.30
(0.06)* 0.22 (0.06)* 0.26 (0.06)* 0.28 (0.05)* 0.29 (0.05)* Activity of daily
living restrictions Spouse/partner −1.51 (1.28) Work role:
Working Retired 0.51 (1.45) Not working 1.78
(1.76) Regular church attender −0.30 (1.27) Group member −2.89
(1.28)* −2.93 (1.23)* −2.94 (1.19)* −2.81 (1.15)* −1.65 (1.14) Life event
count 1.24 (0.76) Chronic stressors General −1.71
(1.54) Money and financial 1.71 (1.39) Employment 2.25
(1.54) Love and marriage 3.82 (1.27)* 4.71 (1.21)* 4.13 (1.18)* 4.19
(1.18)* Family and children −0.03 (1.22) Social life and
recreation 3.36 (1.26)* 3.92 (1.22)* 3.93 (1.18)* 3.88
(1.18)* Health 0.28 (1.21) Residence 0.84 (0.56) Network
size −1.10 (0.25)* −1.15 (0.24)* Frequency of contact −0.40
(0.70) Marital relationship: Not married Spousal
non-confidant Spousal confidant Network
satisfaction R2 .18 .16 .31 .27 .33 .32
. Model 4: Social Roles . . Model 5: Stress Exposure . . Model 6: Social
Contacts . . Covariate . 4A . 4B . 5A . 5B . 6A . 6B
. Age Female Race/ethnicity:
White Black Hispanic Diploma −4.23 (1.50)* −4.38
(1.49)* −3.98 (1.40)* −3.80 (1.40)* −2.89 (1.36)* −3.19 (1.35)* Household
income Chronic conditions Number of symptoms 0.28 (0.06)* 0.30
(0.06)* 0.22 (0.06)* 0.26 (0.06)* 0.28 (0.05)* 0.29 (0.05)* Activity of daily
living restrictions Spouse/partner −1.51 (1.28) Work role:
Working Retired 0.51 (1.45) Not working 1.78
(1.76) Regular church attender −0.30 (1.27) Group member −2.89
(1.28)* −2.93 (1.23)* −2.94 (1.19)* −2.81 (1.15)* −1.65 (1.14) Life event
count 1.24 (0.76) Chronic stressors General −1.71
(1.54) Money and financial 1.71 (1.39) Employment 2.25
(1.54) Love and marriage 3.82 (1.27)* 4.71 (1.21)* 4.13 (1.18)* 4.19
(1.18)* Family and children −0.03 (1.22) Social life and
recreation 3.36 (1.26)* 3.92 (1.22)* 3.93 (1.18)* 3.88
(1.18)* Health 0.28 (1.21) Residence 0.84 (0.56) Network
size −1.10 (0.25)* −1.15 (0.24)* Frequency of contact −0.40
(0.70) Marital relationship: Not married Spousal
non-confidant Spousal confidant Network
satisfaction R2 .18 .16 .31 .27 .33 .32
. Model 7: Relationship Quality . Model 8 . Covariate . 7A . . Age −0.23
(0.14) Female −3.11 (1.35)* Race/ethnicity: White Black 0.86
(1.45) Hispanic −0.84 (1.56) Diploma −2.58 (1.32)* −2.07 (1.37) Household
income −0.06 (0.74) Chronic conditions 0.47 (0.81) Number of symptoms 0.25
(0.05)* 0.21 (0.06)* Activity of daily living restrictions 1.43
(1.36) Spouse/partner 1.62 (1.79) Work role: Working Retired 2.75
(1.57) Not working 1.58 (1.64) Regular church attender 1.24 (1.19) Group
member −1.78 (1.21) Life event count 0.72 (0.75) Chronic
stressors General −1.11 (1.58) Money and financial 1.92
(1.37) Employment 3.28 (1.59)* Love and marriage 3.58 (1.16)* 1.78
(1.25) Family and children −0.10 (1.19) Social life and recreation 3.39
(1.16)* 3.19 (1.22)* Health 0.72 (0.53) Residence −0.59 (1.15) Network
size −1.13 (0.24)* −0.99 (0.26)* Frequency of contact −0.28 (0.72) Marital
relationship: Not married Spousal non-confidant 0.80 (1.70) Spousal
confidant −2.50 (1.17)* −4.79 (1.73)* Network satisfaction −2.98 (1.11)* −2.92
(1.15)* R2 .37 .45
. Model 7: Relationship Quality . Model 8 . Covariate . 7A . . Age −0.23
(0.14) Female −3.11 (1.35)* Race/ethnicity: White Black 0.86
(1.45) Hispanic −0.84 (1.56) Diploma −2.58 (1.32)* −2.07 (1.37) Household
income −0.06 (0.74) Chronic conditions 0.47 (0.81) Number of symptoms 0.25
(0.05)* 0.21 (0.06)* Activity of daily living restrictions 1.43
(1.36) Spouse/partner 1.62 (1.79) Work role: Working Retired 2.75
(1.57) Not working 1.58 (1.64) Regular church attender 1.24 (1.19) Group
member −1.78 (1.21) Life event count 0.72 (0.75) Chronic
stressors General −1.11 (1.58) Money and financial 1.92
(1.37) Employment 3.28 (1.59)* Love and marriage 3.58 (1.16)* 1.78
(1.25) Family and children −0.10 (1.19) Social life and recreation 3.39
(1.16)* 3.19 (1.22)* Health 0.72 (0.53) Residence −0.59 (1.15) Network
size −1.13 (0.24)* −0.99 (0.26)* Frequency of contact −0.28 (0.72) Marital
relationship: Not married Spousal non-confidant 0.80 (1.70) Spousal
confidant −2.50 (1.17)* −4.79 (1.73)* Network satisfaction −2.98 (1.11)* −2.92
(1.15)* R2 .37 .45
Notes: “A” models introduce blocks of conceptually related variables. “B” models
drop individual nonsignificant predictors within blocks. See text for a
description of directional hypotheses.
*p <.05; †p <.1.
Open in new tab
Table 2.
Unstandardized Coefficients (SE) From Regression of Loneliness on Covariates in
Linear Regression Models (N = 225).
. Model 1: Demographics
--------------------------------------------------------------------------------
. . Model 2: Socioeconomic Status
--------------------------------------------------------------------------------
. . Model 3: Health and Functioning
--------------------------------------------------------------------------------
. . Covariate . 1A . 1B . 2A . 2B . 3A . 3B . Age −0.10
(0.15) Female −2.49 (1.29) Race/ethnicity: White Black 2.79
(1.53)† 2.72 (1.53)† 1.24 (1.58) Hispanic 4.15 (1.65)* 4.41 (1.61)* 2.69
(1.67) Diploma −3.71 (1.61)* −4.36 (1.56)* −4.57 (1.49)* −5.03
(1.48)* Household income −1.55 (0.73)* −1.73 (0.71)* −1.00 (0.70) Chronic
conditions 0.02 (0.82) Number of symptoms 0.25 (0.07)* 0.30
(0.06)* Activity of daily living restrictions 2.07
(1.46) Spouse/partner Work role: Working Retired Not
working Regular church attender Group member Life event
count Chronic stressors General Money and
financial Employment Love and marriage Family and
children Social life and
recreation Health Residence Network
size Frequency of contact Marital relationship: Not
married Spousal non-confidant Spousal
confidant Network satisfaction R2 .05 .03 .08 .07 .16 .14
. Model 1: Demographics
--------------------------------------------------------------------------------
. . Model 2: Socioeconomic Status
--------------------------------------------------------------------------------
. . Model 3: Health and Functioning
--------------------------------------------------------------------------------
. . Covariate . 1A . 1B . 2A . 2B . 3A . 3B . Age −0.10
(0.15) Female −2.49 (1.29) Race/ethnicity: White Black 2.79
(1.53)† 2.72 (1.53)† 1.24 (1.58) Hispanic 4.15 (1.65)* 4.41 (1.61)* 2.69
(1.67) Diploma −3.71 (1.61)* −4.36 (1.56)* −4.57 (1.49)* −5.03
(1.48)* Household income −1.55 (0.73)* −1.73 (0.71)* −1.00 (0.70) Chronic
conditions 0.02 (0.82) Number of symptoms 0.25 (0.07)* 0.30
(0.06)* Activity of daily living restrictions 2.07
(1.46) Spouse/partner Work role: Working Retired Not
working Regular church attender Group member Life event
count Chronic stressors General Money and
financial Employment Love and marriage Family and
children Social life and
recreation Health Residence Network
size Frequency of contact Marital relationship: Not
married Spousal non-confidant Spousal
confidant Network satisfaction R2 .05 .03 .08 .07 .16 .14
. Model 4: Social Roles . . Model 5: Stress Exposure . . Model 6: Social
Contacts . . Covariate . 4A . 4B . 5A . 5B . 6A . 6B
. Age Female Race/ethnicity:
White Black Hispanic Diploma −4.23 (1.50)* −4.38
(1.49)* −3.98 (1.40)* −3.80 (1.40)* −2.89 (1.36)* −3.19 (1.35)* Household
income Chronic conditions Number of symptoms 0.28 (0.06)* 0.30
(0.06)* 0.22 (0.06)* 0.26 (0.06)* 0.28 (0.05)* 0.29 (0.05)* Activity of daily
living restrictions Spouse/partner −1.51 (1.28) Work role:
Working Retired 0.51 (1.45) Not working 1.78
(1.76) Regular church attender −0.30 (1.27) Group member −2.89
(1.28)* −2.93 (1.23)* −2.94 (1.19)* −2.81 (1.15)* −1.65 (1.14) Life event
count 1.24 (0.76) Chronic stressors General −1.71
(1.54) Money and financial 1.71 (1.39) Employment 2.25
(1.54) Love and marriage 3.82 (1.27)* 4.71 (1.21)* 4.13 (1.18)* 4.19
(1.18)* Family and children −0.03 (1.22) Social life and
recreation 3.36 (1.26)* 3.92 (1.22)* 3.93 (1.18)* 3.88
(1.18)* Health 0.28 (1.21) Residence 0.84 (0.56) Network
size −1.10 (0.25)* −1.15 (0.24)* Frequency of contact −0.40
(0.70) Marital relationship: Not married Spousal
non-confidant Spousal confidant Network
satisfaction R2 .18 .16 .31 .27 .33 .32
. Model 4: Social Roles . . Model 5: Stress Exposure . . Model 6: Social
Contacts . . Covariate . 4A . 4B . 5A . 5B . 6A . 6B
. Age Female Race/ethnicity:
White Black Hispanic Diploma −4.23 (1.50)* −4.38
(1.49)* −3.98 (1.40)* −3.80 (1.40)* −2.89 (1.36)* −3.19 (1.35)* Household
income Chronic conditions Number of symptoms 0.28 (0.06)* 0.30
(0.06)* 0.22 (0.06)* 0.26 (0.06)* 0.28 (0.05)* 0.29 (0.05)* Activity of daily
living restrictions Spouse/partner −1.51 (1.28) Work role:
Working Retired 0.51 (1.45) Not working 1.78
(1.76) Regular church attender −0.30 (1.27) Group member −2.89
(1.28)* −2.93 (1.23)* −2.94 (1.19)* −2.81 (1.15)* −1.65 (1.14) Life event
count 1.24 (0.76) Chronic stressors General −1.71
(1.54) Money and financial 1.71 (1.39) Employment 2.25
(1.54) Love and marriage 3.82 (1.27)* 4.71 (1.21)* 4.13 (1.18)* 4.19
(1.18)* Family and children −0.03 (1.22) Social life and
recreation 3.36 (1.26)* 3.92 (1.22)* 3.93 (1.18)* 3.88
(1.18)* Health 0.28 (1.21) Residence 0.84 (0.56) Network
size −1.10 (0.25)* −1.15 (0.24)* Frequency of contact −0.40
(0.70) Marital relationship: Not married Spousal
non-confidant Spousal confidant Network
satisfaction R2 .18 .16 .31 .27 .33 .32
. Model 7: Relationship Quality . Model 8 . Covariate . 7A . . Age −0.23
(0.14) Female −3.11 (1.35)* Race/ethnicity: White Black 0.86
(1.45) Hispanic −0.84 (1.56) Diploma −2.58 (1.32)* −2.07 (1.37) Household
income −0.06 (0.74) Chronic conditions 0.47 (0.81) Number of symptoms 0.25
(0.05)* 0.21 (0.06)* Activity of daily living restrictions 1.43
(1.36) Spouse/partner 1.62 (1.79) Work role: Working Retired 2.75
(1.57) Not working 1.58 (1.64) Regular church attender 1.24 (1.19) Group
member −1.78 (1.21) Life event count 0.72 (0.75) Chronic
stressors General −1.11 (1.58) Money and financial 1.92
(1.37) Employment 3.28 (1.59)* Love and marriage 3.58 (1.16)* 1.78
(1.25) Family and children −0.10 (1.19) Social life and recreation 3.39
(1.16)* 3.19 (1.22)* Health 0.72 (0.53) Residence −0.59 (1.15) Network
size −1.13 (0.24)* −0.99 (0.26)* Frequency of contact −0.28 (0.72) Marital
relationship: Not married Spousal non-confidant 0.80 (1.70) Spousal
confidant −2.50 (1.17)* −4.79 (1.73)* Network satisfaction −2.98 (1.11)* −2.92
(1.15)* R2 .37 .45
. Model 7: Relationship Quality . Model 8 . Covariate . 7A . . Age −0.23
(0.14) Female −3.11 (1.35)* Race/ethnicity: White Black 0.86
(1.45) Hispanic −0.84 (1.56) Diploma −2.58 (1.32)* −2.07 (1.37) Household
income −0.06 (0.74) Chronic conditions 0.47 (0.81) Number of symptoms 0.25
(0.05)* 0.21 (0.06)* Activity of daily living restrictions 1.43
(1.36) Spouse/partner 1.62 (1.79) Work role: Working Retired 2.75
(1.57) Not working 1.58 (1.64) Regular church attender 1.24 (1.19) Group
member −1.78 (1.21) Life event count 0.72 (0.75) Chronic
stressors General −1.11 (1.58) Money and financial 1.92
(1.37) Employment 3.28 (1.59)* Love and marriage 3.58 (1.16)* 1.78
(1.25) Family and children −0.10 (1.19) Social life and recreation 3.39
(1.16)* 3.19 (1.22)* Health 0.72 (0.53) Residence −0.59 (1.15) Network
size −1.13 (0.24)* −0.99 (0.26)* Frequency of contact −0.28 (0.72) Marital
relationship: Not married Spousal non-confidant 0.80 (1.70) Spousal
confidant −2.50 (1.17)* −4.79 (1.73)* Network satisfaction −2.98 (1.11)* −2.92
(1.15)* R2 .37 .45
Notes: “A” models introduce blocks of conceptually related variables. “B” models
drop individual nonsignificant predictors within blocks. See text for a
description of directional hypotheses.
*p <.05; †p <.1.
Open in new tab
This research was supported by Grant P01 AG18911 from the National Institute on
Aging and by the John Templeton Foundation.
L. C. Hawkley helped plan the study, performed all statistical analyses, and
wrote the paper. M. E. Hughes, L. J. Waite, and C. M. Masi contributed to
writing and revising the paper. R. A. Thisted consulted on the data analysis and
contributed to writing and revising the paper. J. T. Cacioppo helped plan the
study and contributed to writing and revising the paper.
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