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Loneliness can be seen as a social failure subject to causal search: Why am I lonely? Why do I lack friends? According to attribution theory, answers to these questions can influence emotions, motivation, and behaviours. This study examined the relationships between various affiliative causal beliefs (i.e., beliefs about loneliness and friendship development), social participation, and loneliness among older adults (72+ years). Cross-sectional and longitudinal (over five years) results showed that more strongly endorsing internal/controllable causal beliefs (i.e., believing that making friends depends on effort) related to greater social participation. Moreover, greater social participation related to less loneliness. External/uncontrollable causal beliefs predicted greater loneliness. In fully addressing loneliness, it may be important to focus on people's causal beliefs.
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Journal of Social and Personal
DOI: 10.1177/0265407509106718
2009; 26; 273 Journal of Social and Personal Relationships
U. Swift and Joelle C. Ruthig
Nancy E. Newall, Judith G. Chipperfield, Rodney A. Clifton, Raymond P. Perry, Audrey
longitudinal study
Causal beliefs, social participation, and loneliness among older adults: A
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Causal beliefs, social participation,
and loneliness among older
adults: A longitudinal study
Nancy E. Newall, Judith G. Chipperfield, Rodney A. Clifton,
Raymond P. Perry, & Audrey U. Swift
University of Manitoba
Joelle C. Ruthig
University of North Dakota
Loneliness can be seen as a social failure subject to causal
search: Why am I lonely? Why do I lack friends? According to
attribution theory, answers to these questions can influence
emotions, motivation, and behaviours. This study examined
the relationships between various affiliative causal beliefs (i.e.,
beliefs about loneliness and friendship development), social
participation, and loneliness among older adults (72+ years).
Cross-sectional and longitudinal (over five years) results
showed that more strongly endorsing internal/controllable
causal beliefs (i.e., believing that making friends depends on
effort) related to greater social participation. Moreover, greater
social participation related to less loneliness. External/un-
controllable causal beliefs predicted greater loneliness. In fully
addressing loneliness, it may be important to focus on people’s
causal beliefs.
KEY WORDS: attribution theory • causal attributions • health •
loneliness • social participation
Journal of Social and Personal Relationships Copyright © 2009 SAGE Publications
(, Vol. 26(2–3): 273–290. DOI: 10.1177/0265407509106718
This research was supported by a Canadian Institutes of Health Research (CIHR) Canada
Graduate Scholarships Doctoral Award to the first author, and a CIHR operating grant
(MOP-64335) and Mid Career Award in Aging to the second author. Preliminary findings of
this research were presented in a poster at the Annual Scientific and Educational Meeting
of the Canadian Association on Gerontology, Victoria, B.C., Canada, October 2004. Address
correspondence to Nancy E. Newall, Department of Psychology, University of Manitoba,
Winnipeg, Manitoba, Canada, R3T 2N2 [e-mail:]. Duncan Cramer
was the Action Editor on this article.
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Loneliness is commonly conceived of as a feeling resulting from perceived
deficits in social relationships (Dykstra & Fokkema, 2007).These deficits can
appear to take on an almost physiological form as lonely individuals often
describe themselves as feeling empty inside (e.g., Schultz & Moore, 1984;
Weiss, 1973). Loneliness is a negative experience in its own right. Further-
more, research focusing on older adults has shown that loneliness is related
to other negative outcomes such as poor health (e.g., Russell, 1996) and
depression (e.g., Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006).These
findings emphasize the importance of identifying and understanding the
causes of loneliness in older adults in order to develop intervention strat-
egies aimed at reducing loneliness.
The percentage of older adults reporting moderate loneliness appears to
range from 20%–40% (Pinquart & Sorensen, 2001; Weeks, 1994; Wenger &
Burholt, 2004). In considering research which has focused on age differences
in the prevalence of loneliness, it appears that older adults may be no more
lonely than younger adults (e.g., Green, Richardson, Lago, & Schatten-Jones,
2001; Schulz & Moore, 1988). In their meta-analysis of correlates of loneli-
ness in older adults, Pinquart and Sorensen (2001) found that the relation
between age and loneliness appeared to be U-shaped, such that loneliness
decreased with age for the youngest subgroup (age < 60 years), was un-
related to age for the next oldest subgroup (between 60.1–80 years), and
increased with age for the oldest subgroup (age > 80.1). Therefore, the
oldest may be especially vulnerable to loneliness and this possibility under-
scores the importance of studying the development of loneliness.
Loneliness has been studied from various perspectives. The present study
examined a representative sample of older adults to consider whether lone-
liness develops in part from the way people think about their affiliations
with others. In particular, we examined whether older adults’ explanations
about their affiliations with others (e.g., friendship development) predicted
social participation in activities, as well as their current level of loneliness and
their subsequent loneliness five years later. This focus on the role of cogni-
tive factors in understanding loneliness follows past research on the relation-
ship between loneliness and causal beliefs or attributions (e.g., Anderson,
Horowitz, & French, 1983; Peplau & Caldwell, 1978) as well as perceptions
of control or locus of control (e.g., Moore & Schulz, 1987; Solano, 1987).
Causal beliefs and loneliness
Although Weiner’s attribution theory (e.g., Weiner, 1985) has been devel-
oped and applied most extensively in the academic achievement domain
(e.g., Menec et al., 1994; Perry, Hechter, Menec, & Weinberg, 1993; Ruthig,
Perry, Hall, & Hladkyj, 2004), it also offers a particularly useful framework
for studying causal beliefs in the affiliation domain.This theoretical perspec-
tive is based on the premise that people generally want to understand why
events happen in their lives. For example, people who are lonely may seek
to find an explanation for their loneliness or lack of satisfying relationships.
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According to attribution theory (e.g., Weiner, 1985), the types of explana-
tions that individuals generate can have profound effects on their subsequent
emotions, expectations, behaviours, and motivation. Proposing a cognition–
emotion–action sequence, Weiner (1985) argues that all perceived causes
can be characterized in terms of three dimensions: locus of causality (internal
vs. external), controllability (uncontrollable vs. controllable), and stability
(unstable vs. stable). Each dimension predicts unique emotions, which, in
turn, guide actions.Thus, attribution theory focuses on the underlying dimen-
sions of attributions, rather than specific attributions per se.
Peplau, Perlman, and colleagues proposed that loneliness reflects a dis-
crepancy between individuals’ desired and achieved social networks (Peplau
& Caldwell, 1978; Perlman, 2004). Thus, loneliness can be precipitated by
changes in a person’s achieved social contacts (e.g., widowhood, moving)
or by changes in a person’s desired social contacts (e.g., through social
norms) (Peplau, Russell, & Heim, 1979). Peplau et al. (1979) also argued
that understanding the causes for loneliness can help people regain control
over their social relationships so as to maintain a balance between achieved
and actual social relations. Using Weiner’s theoretical framework, they put
forth that the types of explanations given for loneliness might greatly affect
emotional reactions to loneliness as well as the enactment of coping beha-
viours (Peplau et al., 1979). In terms of emotional reactions, they noted that
embarrassment would follow from people believing that they are lonely
because they are unattractive (an internal and uncontrollable cause). On the
other hand, blaming others for loneliness (external and controllable cause)
might result in feelings of anger towards others. Furthermore, in terms of
coping behaviours, Peplau et al. argued that people may be motivated to
overcome their loneliness only if they perceive that it is due to personally
controllable reasons such as lack of effort. Thus, framed within the discrep-
ancy model of loneliness, high perceived controllability may imply a confi-
dence in the ability to improve actual social networks so that they match
desired social networks (we would like to thank an anonymous reviewer
for this suggestion).
The importance of the controllability dimension in causal attributions is
highlighted by Anderson and his colleagues’ work on loneliness and attri-
butional style among college students (e.g., Anderson & Arnoult, 1985;
Anderson et al., 1983; Anderson & Riger, 1991). Their research shows that
the controllability dimension is most commonly associated with what they
refer to as “problems in living”, namely loneliness, depression, and shyness.
That is, people who are lonely are likely to see their interpersonal failures
as being due to uncontrollable causes. Based on this work, Anderson and
his colleagues developed the Controllability Attributional Model (CAM)
which predicts that attributing failures to uncontrollable causes leads to low
success expectancies, negative affect, and low motivation (Anderson &
Riger, 1991).
However, it should be noted that some researchers have speculated that,
in people who have tried and failed to establish relationships, blaming the
self (i.e., self-blame, making an internal and controllable attribution) may
Newall et al.: Loneliness among older adults 275
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lead to greater feelings of shame and social withdrawal as compared to
blaming others or blaming circumstances beyond control (making an
external and uncontrollable attribution; see Peplau et al., 1979). Related to
this, Perlman (2004) noted that socially-isolated individuals may avoid
feeling lonely by attributing their isolation to uncontrollable external forces.
Notably, these studies focus on the emotional effects (i.e., negative affect,
intensity of loneliness) of attributions in people who are chronically lonely
or socially isolated. In contrast, our study focuses more on the motivational
effects of general causal beliefs about friendship development and lone-
liness. Thus, this may explain different expected relationships between the
beliefs and loneliness.
The present study
The primary objective of this study was to examine older individuals’ causal
beliefs about affiliation as immediate and long-term predictors of loneliness.
In particular, we examined whether older adults’ explanations about their
affiliations with others (e.g., friendship development) predicted social parti-
cipation and subsequent loneliness. Because attributing interpersonal failures
to uncontrollable causes is associated with loneliness and low motivation in
the affiliative domain (Anderson & Riger, 1991), it was hypothesized that
external/uncontrollable causal beliefs would predict lower social partici-
pation and greater loneliness, whereas internal/controllable affiliative causal
beliefs would predict greater social participation and lower loneliness. This
conforms to the logic that an external/uncontrollable belief (e.g., believing
that friends are made through luck) is de-motivating, resulting in a passive
approach to friendship development or social contacts and, in turn, foster-
ing loneliness. In contrast, a more proactive approach to making friends
may be taken by someone who believes that making friends is caused by
one’s own efforts (a controllable/internal attribution), which could then
lead to less loneliness.
Our approach is an improvement over past studies that have typically
examined the role of causal beliefs and loneliness without considering the
contribution of other predictor variables. In order to determine whether or
not causal beliefs predicted social participation and loneliness above and
beyond various demographic and health variables, we specified structural
equation models in which these background variables, together with causal
beliefs, predicted social participation, and, in turn, social participation pre-
dicted loneliness (see Figure 1). We examined this model using both cross-
sectional and longitudinal data.
Age was included in the models to capture possible age-related factors,
although it may be widowhood or physical incapacity rather than age per se
that influences loneliness (e.g., Perlman, 2004; Pinquart & Sorensen, 2001).
Gender was also included because it has been generally been found that
older women are more likely to be lonely than older men (e.g., Jylha, 2004;
Pinquart & Sorensen, 2001); however, this may be due to an artifact of how
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loneliness is measured, resulting in women’s greater willingness to admit to
feeling lonely (Perlman, 2004) or to factors that covary with gender (e.g.,
women being older in age, living alone, or in poorer health). Education level
was included to account for differences in socioeconomic status that relate
to social isolation (Wenger, Davies, Shahtahmasebi, & Scott, 1996) and
may provide more available resources and social opportunities that could
prevent loneliness (Pinquart & Sorensen, 2001). Living alone, versus living
with others, was also considered because it is an important risk factor for
loneliness in older adults (e.g., De Jong Gierveld, 1987; Havens, Hall,
Sylvestre, & Jivan, 2004). Finally, health was included in the models because
it consistently correlates with loneliness (e.g., Wenger et al., 1996), although
the association may become weaker at older ages (Dykstra, Van Tilburg,
& De Jong Gierveld, 2005; but see Hawkley & Cacioppo, 2007). Recent
research shows that loneliness may directly influence health through its
negative effect on immune response (Pressman et al., 2005; Hawkley &
Cacioppo, 2003), although the nature of the relationship between health
and loneliness is likely reciprocal (e.g., Fees, Martin, & Poon, 1999).
Aging in Manitoba (Canada) study. Participants were community-dwelling
individuals who took part in the Aging in Manitoba (AIM) longitudinal
studies that have been ongoing for over 35 years. The initial 1971 survey was
conducted by the Manitoba Provincial Department of Health and Social
Development in order to identify the needs of older persons (Mossey,
Havens, Roos, & Shapiro, 1981). A random sample of Manitobans (65+
years) was drawn from the Manitoba Health population registry, and strat-
ified by place of residence (i.e., community or personal care home) and
region. A total of 4,803 individuals participated in the initial 1971 AIM
study. Using similar techniques, two additional cross-sectional samples of
older individuals (60+ years) were selected in 1976 (N = 1,302) and again in
1983 (N = 2,877). Since then, there have been several follow-up data collec-
tion waves (in 1990, 1996, 2001, 2005, and 2006) in which sociodemographic,
health, and psychosocial information has been obtained via face-to-face
Newall et al.: Loneliness among older adults 277
Social Participation Loneliness
Causal Beliefs
Theoretical model
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interviews in the participants’ homes. A loneliness scale was included in the
interview schedule for the first time in 1996. Our study complements other
Aging in Manitoba research studies on the social and health factors related
to loneliness and social isolation in older Manitobans (e.g., Havens et al.,
Obtaining representative samples of older Manitobans was important to
the objectives of the AIM study. The 1971 sample was representative in
terms of age and gender of the Manitoba population aged 65 and over
(Mossey et al., 1981). Furthermore, the AIM 1990 sample was comparable
to the older Manitoba population in 1990 in terms of gender and marital
status (Chipperfield, Havens, & Doig, 1997) and generally representative in
terms of age, although the oldest age category (90+ years) was over repre-
sented and the youngest age category (70–74 years) was underrepresented
(Chipperfield et al., 1997).
Sample selection. The present study’s cross-sectional sample (N = 1243)
was taken from the AIM 1996 sample of 1,868 individuals to include only
those respondents who: (1) were community-dwelling (i.e., not living in a
personal care home); and (2) had complete data for loneliness and causal
beliefs (the main variables of interest). Participants living in a personal care
home (n = 255) and/or who had incomplete data (n = 370) were excluded,
leaving a final sample of 1,243 participants. Data were incomplete due to
proxy respondents completing the interview who did not answer the subjec-
tive questions (n = 132), participants being interviewed over the phone and
given a shorter interview (n = 6) and due to participants refusing to answer
the questions or providing invalid (e.g., don’t know) responses (n = 232).
Some of these latter participants with missing data had required consider-
able help from a proxy respondent to complete the interview (n = 77).
The longitudinal (i.e., AIM 1996–2001) sample included individuals (N =
688) from the 1996 cross-sectional sample who: (1) also participated in the
2001 study; and (2) had completed the AIM 2001 loneliness scale. Out of the
1,243 individuals from the 1996 cross-sectional data, 798 also participated in
AIM 2001. The reasons for non-participation in AIM 2001 included death,
re-location to another province, hospitalization or being too ill, and refusal.
Of the individuals who had completed both surveys, 110 people had incom-
plete 2001 data, leaving a total of 688 individuals in the longitudinal sample.
Data were incomplete due to proxy respondents completing the interview
(n = 106), and participants being interviewed over the phone and given a
shorter interview (n = 4). Not surprisingly, given the reasons for attrition,
those individuals in the longitudinal sample (N = 688), compared to those
who participated in 1996 only (N = 555), were younger (M ages = 78.9 vs.
82.3 yrs, t(1241) = 11.6, p < .01) and were healthier in 1996, as indicated by
a smaller number of health conditions (M = 3.6 vs. 4.3, t(1241) = 4.9, p < .01).
Table 1 describes the measures used for the cross-sectional and longitudi-
nal samples. Note that, although all the variables were measured at Time 1
in 1996, loneliness was also measured at Time 2 in 2001.
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Sociodemographics. Sociodemographic variables included age (in years),
and gender. Education level, or years they had completed in school, was
included to account for differences in socioeconomic status.A living arrange-
ments variable differentiated between people who lived alone and those who
lived with one or more persons. Living arrangements closely corresponded
with marital status. In the cross-sectional sample, 96% (575/598) of married
individuals lived with one or more individuals. Of the not married (widowed,
n = 534; separated/divorced, n = 28; or single, n = 83), only 16% (101/645)
lived with others.
Newall et al.: Loneliness among older adults 279
Description of study measures for the cross-sectional and longitudinal samples
# of
Measures Anchors items MSDRange
Age (yrs) 1 80.45 5.43 72–98
(78.93) (4.68) (72–95)
Gender 1 = men 1 1.57 .50
2 = women (1.59) (.49)
Education (yrs) 1 9.23 3.15 0–27
(9.42) (2.95) (0–21)
Independence (IADL) 0 = needs help 12 9.70 1.97 2–12
1 = yes, can do (10.20) (1.55) (2–12)
General perceived health 1 = poor 1 2.42 .73 1–4
4 = excellent (2.34) (.69) (1–4)
Health conditions 0 = no 21 3.94 2.55 0–17
1 = yes (3.62) (2.43) (0–13)
Social participation 0 = no 14 5.05 2.11 0–13
1 = yes (5.35) (2.09) (0–13)
Living arrangements 1 = lives alone
2 = lives with 1 or 1 1.54 .50
2 = more persons (1.56) (.50)
Causal belief – effort 1 = total disagreement 1 4.90 1.90 1–7
7 = total agreement (5.04) (1.82) (1–7)
Causal belief – context 1 = total disagreement 1 3.13 2.10 1–7
7 = total agreement (3.08) (2.10) (1–7)
Causal belief – luck 1 = total disagreement 1 3.74 2.05 1–7
7 = total agreement (3.72) (2.07) (1–7)
Loneliness (1996) 0 = no 11 2.76 2.58 0–11
1 = yes
Loneliness (2001) 0 = no 11
1 = yes (2.68) (2.79) (0–11)
Notes.Numbers shown in brackets represent the longitudinal sample values.All measures taken
in 1996, except loneliness assessed in 1996 and 2001.
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Functional status. Participants’ functional status (independence) was meas-
ured by asking whether or not they were independently capable of per-
forming 12 specific instrumental activities of daily living (IADL; e.g.,
light housework, laundry, and food preparation). Based on similar IADL
measures (e.g., Lawton & Brody, 1969), a composite score was created by
summing the items so that higher scores reflected greater independence
(cross-sectional sample: alpha = .75, skewness = 1.13, kurtosis = 1.43).
Health status. Two measures were used to create a health status latent
construct. First, individuals’ self-rated health was assessed by asking them
to rate their health compared to other people who were their own age. This
measure has been shown to predict objective health status (Bailis, Segall,
& Chipperfield, 2003), mortality (Menec, Chipperfield, & Perry, 1999), and
health care use (Mossey et al., 1981). Responses ranged on a 5-point scale
(1 = bad; 2 = poor; 3 =fair; 4 =good; 5 = excellent). This measure was subse-
quently reverse coded and the small number of “bad” responses were re-
categorized to “poor”, resulting in a 4-point scale ranging from 1 = excellent
to 4 = poor.
As a second measure of health, individuals were asked whether they
currently had, or were still feeling, the after-effects of 21 specific health
problems (e.g., heart and circulation problems, arthritis). These items were
summed to create a composite measure, with higher scores indicating
poorer health (cross-sectional sample: alpha = .61, skewness = .84, kurtosis
= .95). Because the two health measures both theoretically tap into people’s
health, and because of their strong empirical association (r = .44), we spec-
ified the two observed health measures as a latent measure in the structural
equation models.
Social participation. In the present study, respondents’ social activity parti-
cipation was measured by tallying the number of social activities they had
participated in during the past week. In particular, respondents were asked
whether they had performed any of 14 activities (e.g., visiting family, visiting
friends, playing sports or games, doing church-related activities, doing com-
munity volunteer work). Affirmative responses were summed to create a
measure of social activity participation (cross-sectional sample: alpha = .56,
skewness = .39, kurtosis = -.03).
Causal beliefs for affiliation. Participants’ causal beliefs for affiliation were
assessed using three items from the Multidimensional Multiattributional
Causality (MMC) scales (Lefcourt, Von Baeyer, Ware, & Cox, 1979). The
original MMC scales were intended to measure causal beliefs (attributions
for outcomes) in the affiliation and achievement domains and items were
classified as: internal/unstable items (Effort subscale); external/stable items
(Context subscale); external/unstable items (Luck subscale); internal/stable
items (Ability subscale). Note that the original MMC scales included items
that varied in only two dimensions (internal/external and stable/unstable) as
identified in Weiner’s early work (Weiner et al., 1971). Thus, the scales did
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not originally include items to tap into the third causal dimension of con-
trollability (i.e., whether events are perceived to be controllable or uncon-
trollable) that was introduced much later by Weiner (1985). Nonetheless, it
was possible to classify the items based on the controllability dimension.
Due to restricted space in the interview schedule, only one item from each
of the four affiliation subscales was included in the 1996 interview schedule,
and only three of those four items were selected for the present study. Note
that we excluded this fourth item that corresponded to the ability subscale
because it was ambiguous in terms of whether people perceive social skills
to be innate (internal/stable/uncontrollable) or modifiable (internal/unstable/
controllable). Weiner (1983) discussed this potential pitfall of researchers
erroneously concluding ability attributions to be innate. Specifically, for the
purposes of the present study, respondents were asked whether they agreed
with statements assessing: (1) Effort: “Do you believe that loneliness comes
from not trying to be friendly?” (internal/unstable/controllable); (2) Context:
“Over your life have you found that no matter what you do, some people
just don’t like you?” (external/stable/uncontrollable); and (3) Luck: “Over
your life, have you found that making friends has largely been a matter of
having the right breaks?” (external/unstable/uncontrollable).
Loneliness. Loneliness was measured in 1996 and again in 2001 using De
Jong Gierveld and Kamphuis’s (1985) 11-item loneliness scale. Five of the
scale’s items measure feelings of belonging (e.g., “I can call on my friends
whenever I need them”), and six items measure emotional loneliness or
missing relationships (e.g., “I miss having a really close friend). Based on
participant agreement with the statements (no; more or less; and yes), a
composite measure was created by summing item scores such that higher
scores reflected greater loneliness. The items were also dichotomized (0 =
no, 1 = more or less; yes), as is typically done with this scale, so that the
potential range was 0–11 (e.g., Dykstra et al., 2005). For both the cross-
sectional and the longitudinal samples, respectively, the scale had good reli-
ability (AIM 1996 (N = 1,243): alpha = .77, skewness = .99, kurtosis = .43;
AIM 2001 (N = 688): alpha = .83, skewness = .99, kurtosis = .20). If scores
of 3 and above reflect loneliness (Lauder, Mummery, Jones, & Caperchione,
2006), then 45% of the 1996 sample could be considered as being lonely.
In addition to the De Jong Gierveld and Kamphuis scale, participants
were categorized based on their self-reported loneliness in 1996 (1 = not
lonely; 2 = moderately lonely; 3 = severely lonely; 4 = extremely lonely;
M = 1.35; SD = .53). Past studies suggest that such 1-item measures may
underreport loneliness (Perlman, 2004; Ernst & Cacioppo, 1999); neverthe-
less, it is of value to obtain a sense of how many participants categorize
themselves as being lonely. In 1996, 1% of participants described them-
selves as “severely” or “extremely” lonely, 32% as “moderately” lonely, and
67% as “not lonely”.
Analytical approach. Structural equation modelling using AMOS (Arbuckle,
1995) tested the hypothesized cross-sectional and longitudinal models.
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Coefficients were estimated using the maximum likelihood method and
the goodness of fit was assessed by Chi-square, the comparative fit index
(CFI), the adjusted goodness of fit index (AGFI), and the root mean square
error of approximation (RMSEA). Both the CFI and AGFI indices range
from 0 to 1, with values close to 1 indicating good fit (Byrne, 2001), and,
more specifically, values at or above .95 recommended for the CFI (Hu
& Bentler, 1999). RMSEA values at or below .06 indicate good fit (Hu &
Bentler, 1999).
Bivariate correlations for the variables are presented in Table 2. Focusing
on the 1996 cross-sectional sample, as expected, being older was associated
with being female, living alone, having less education, less functional capa-
bility, poorer health, and participating in fewer social activities. As past
research suggests, a number of age-related variables, rather than simply age,
relates to loneliness (e.g., Jylha, 2004). For this reason we were interested
in examining age in the context of other social and health variables in the
structural equation models. Also, as expected, greater social activity parti-
cipation was correlated with participants’ functional capability and health
(in particular their perceived health) and with loneliness.
Structural equation modelling results
Cross-sectional results. Indices suggested good fit for the cross-sectional
model, χ
= 48.47, p < .001, CFI = .97, AGFI = .95, and RMSEA = .06. Table
3 shows all the estimated coefficients between the variables. Although not
provided in Table 3, the estimated coefficients between all of the demo-
graphic, health, and causal belief variables were specified in the models
(Figure 1). In general, the results corresponded to theoretical expectations.
Focusing first on social participation, significant predictors included
younger age, higher education level, greater functional independence, and
greater endorsement of the effort causal belief. Turning to loneliness, signi-
ficant predictors included lower education level, poorer health, living alone,
as well as less endorsement of the effort belief, and greater endorsement of
the context and luck beliefs. Although the magnitude of the relationships
between the causal beliefs and loneliness could be considered small, these
magnitudes exceed most of the effects of the other established demo-
graphic and health predictors in the model.
In addition, as expected, greater social participation was associated with
less loneliness, even after controlling for the demographic, health, and
causal belief variables. This is important because we can also examine how
variables may affect loneliness indirectly through social participation. That
is, age and functional status, while not having a direct effect on loneliness,
do appear to indirectly relate to loneliness through greater social participa-
tion. Moreover, education level and endorsement of the effort causal belief
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Newall et al.: Loneliness among older adults 283
Correlations between study variables for the cross-sectional (N = 1,243) and longitudinal (N = 688) samples
Variables 123456789101112
1. Age –
2. Gender .07*
3. Education –.17** .07*
(–.16**) (.09*)
4. Independence (IADL) –.37** –.18** .05
(–.26**) (–.26**) (–.02)
5. General perceived health .01 .07* –.09** –.33**
(.02) (.05) (–.09*) (–.32**)
6. Health conditions .11** .07* –.03 –.34** .44**
(.08*) (.07) (–.03) (–.34**) (–.46**)
7. Social participation –.14** –.04 .11** .24** –.19** –.07*
(–.06) (–.06) (.12**) (.20**) (–.14**) (–.06)
8. Living arrangements –.27** –.35** .01 .09** –.01 –.06* .01
(–.27**) (–.40**) (.03) (.11**) (–.04) (–.09*) (.04)
9. Causal belief – effort –.04 –.03 –.05 .09** –.07* –.07* .13** .08**
(.03) (–.02) (–.06) (.03) (–.08*) (–.07) (.12**) (.06)
10. Causal belief – context –.05 –.12** –.04 –.02 .03 .07* –.03 –.03 –.03
(–.03) (–.08*) (–.09*) (–.03) (.03) (.07) (–.07) (–.06) (–.04)
11. Causal belief – luck .08* –.05 –.08* –.05 .04 .10** –.03 .03 .13** .13**
(.06) (–.02) (–.08*) (–.08**) (.06) (.10*) (–.02) (–.01) (.11**) (.22**)
12. Loneliness 1996/ .06* .00 –.11** –.17** .19** .25** –.17** –.12** –.22** .18** .14**
12. (Loneliness 2001) (.07*) (.07) (–.04) (–.11**) (.09*) (.16**) (–.18**) (–.12**) (–.13**) (.05) (.11**)
Notes. Correlations without parentheses are from the cross-sectional sample and correlations in parentheses are from the longitudinal sample. All variables are from 1996 except
Loneliness measured in 1996 and 2001. *p .05; **p .01.
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are both associated indirectly, through social participation, and directly to
In summary, for the cross-sectional results, causal beliefs predicted lone-
liness in the expected directions even after controlling for the sociodemo-
graphic, social, and health variables. Moreover, the internal/controllable
belief was directly associated with loneliness, as well as indirectly associated
with loneliness through its association with greater social participation.
Longitudinal results. The longitudinal version of the model in Figure 1 was
tested only with those participants who completed both the 1996 and 2001
data collections. Moreover, all of the predictor variables, including social
participation, were from the 1996 data collection whereas the loneliness
variable was from the 2001 data collection.
Indices suggested good fit for the longitudinal model, χ
= 15.58, p = .08,
CFI = .99, AGFI = .97, and RMSEA = .03. Note that although they are not
shown in Table 4, for theoretical reasons the estimated coefficients between
all of the demographic, health, and causal belief variables were specified in
the models (Figure 1). Similar to the cross-sectional sample results, greater
education level, greater functional independence, and greater endorsement
of the effort causal belief in 1996 Time 1 were associated with greater social
participation in 1996 (Table 4). Unlike the cross-sectional results, age was
not associated with social participation. Note that because greater social
participation is, in turn, associated with less loneliness five years later in 2001,
this means that education level, functional status, and effort beliefs can be
seen as having an indirect effect on loneliness through social participation.
Similar to the cross-sectional results, better health, greater endorsement
of the effort causal belief, and less endorsement of the luck causal belief in
1996 Time 1 were directly related to less loneliness in 2001 (Table 4). Unlike
284 Journal of Social and Personal Relationships 26(2–3)
SEM coefficients for the cross-sectional model (N = 1,243)
Endogenous variables
Exogenous variables 1996 Social participation 1996 Loneliness 1996
Age –.06+ –.02
Gender (1 = men; 2 = women) –.01 –.05+
Education level .09** –.08**
Independence (IADL) .17** .02
Poorer health –.08+ .28**
Living arrangements –.04 –.10**
Causal belief-effort .11** –.19**
Causal belief-context –.02 .13**
Causal belief-luck –.01 .11**
Social participation –.09**
.09 .20
+p = .06–.08; *p .05; **p .01.
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the cross-sectional results, endorsement of the causal belief of context and
education level in 1996 were not directly associated with loneliness in 2001,
and living with others in 1996 was associated with loneliness in 2001.
In summary, the longitudinal results replicated to a great extent the cross-
sectional results. This is not surprising given the strong association between
the 1996 and 2001 loneliness ratings (r = .46). As expected, results showed
that strongly endorsing the statement that loneliness stems from not being
friendly (effort belief) was related to less loneliness at both measurement
points separated by five years (Table 3 and 4). Similarly, strongly endorsing
the uncontrollable/external causal belief of luck was related to greater lone-
liness at both measurement points. However, no significant association was
found between the external/uncontrollable causal belief of context and lone-
liness in 2001. Thus, this belief was only related to loneliness in the imme-
diate context, and not over the 5-year time span of the longitudinal study.
This study examines the experience of loneliness among a representative
sample of older adults. Consistent with past research, suggesting that 20–40%
percent of older adults report occasional or moderate loneliness (Pinquart
& Sorensen, 2001; Wenger & Burholt, 2004), our study found that, based on
the single-item self-reported loneliness measure, about 33% of participants
described themselves as “severely” or “moderately” lonely.This implies that
for many people loneliness is a common experience in later life, underscoring
the need to understand how it evolves and how to mitigate its effects.
Our study provides some insight into the development and maintenance
of loneliness. In particular, we showed that beliefs that new relationships
Newall et al.: Loneliness among older adults 285
SEM path coefficients for the longitudinal model (N = 688)
Endogenous variables
Exogenous variables 1996 Social participation 1996 Loneliness 2001
Age .00 .03
Gender (1 = men; 2 = women) –.02 .02
Education level .11** .00
Independence (IADL) .18** .02
Poorer health –.03 .14*
Living arrangements .00 –.08*
Causal belief-effort .11** –.10**
Causal belief- context –.05 .00
Causal belief-luck .01 .10**
Social participation –.15**
.07 .09
+p = .06–.08; *p .05; **p .01.
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are forged through internal/controllable causes, like effort, are associated
with greater social participation and less loneliness, whereas beliefs that
making friends is due to the context or to luck are associated with greater
loneliness. Moreover, the predictive value of endorsing these causal beliefs
about affiliation was demonstrated both immediately and over five years
even after accounting for the effects of other sociodemographic and health
Causal beliefs, social participation, and loneliness
The association found between endorsing internal/controllable affiliative
causal beliefs (i.e., effort) and being less lonely suggests that these types
of beliefs are, in fact, protecting people from loneliness. Moreover, effort
beliefs related to less loneliness both directly and indirectly through greater
social participation. This evidence is consistent with attribution theory that
posits that the explanations individuals give for events can profoundly
influence their expectations, emotions, and behaviours. Perceiving that a
social failure, such as loneliness, stems from a controllable reason, for
example, might at least provide individuals with a sense of control. Such a
sense could lead to subsequent efforts to ameliorate their social situation
and to bring into line their actual versus desired social networks.
In contrast, endorsing external/uncontrollable beliefs was related to
greater loneliness which may imply that uncontrollable attributions for
friendship erode feelings of control for individuals about creating or main-
taining relationships. This may lead people to have low motivation and
effort in the affiliative domain which, in turn, leads to greater loneliness.
In this way, an uncontrollable attribution, such as luck, could foster a
“passive”, rather than an “active”, approach to creating and maintaining
friends. Of note, our results suggested that strongly endorsing the context
or luck beliefs was not related to social participation. Thus, the results
suggest that social participation is not part of the mechanism through which
external/uncontrollable causal beliefs are associated with greater loneliness.
In addition, our findings are not consistent with the speculation that
internal/controllable causal beliefs result in self-blame thereby fostering
greater loneliness. Nor do they support the thinking that loneliness may be
avoided by attributing loneliness to external/uncontrollable forces. These
issues could be addressed more directly, however, in research focusing on
individuals’ feelings of self-blame surrounding their loneliness and their
perceptions of control. For example, given theory which emphasizes the
adaptiveness of a correspondence between control perceptions or control
strategies and the objective potential of control in a given situation (e.g.,
Heckhausen & Schulz, 1998), it may be that our results would have been
different had we examined beliefs among people living in institutions
offering less objective potential for control over social relationships. That
is, it is possible that if a person has little objective potential to control their
social relations, then making external/uncontrollable attributions for lone-
liness may be more adaptive than making internal/controllable attributions.
Based on the potential to change individuals’ causal beliefs through inter-
ventions, these findings have important implications. For example, enabling
286 Journal of Social and Personal Relationships 26(2–3)
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people to become aware of how their perceptions affect their behaviours
and emotions might help them to deal more effectively with their loneliness.
Moreover, increasing practitioners’ awareness of these connections may
help them to provide guidance for their patients. At the same time, it is
important to note that we do not mean to suggest that individuals are solely
responsible for their loneliness.As acknowledged earlier, to understand and
to address the complex phenomenon that is loneliness requires multiple
perspectives. Indeed, our results point to the importance of adults being
given resources and opportunities for social activities and interaction as
they grow older. What we are suggesting is that designing interventions to
address loneliness among older adults may be facilitated by considering the
role that general causal beliefs play in loneliness.
Demographic, health, and loneliness
The present study replicated past research in finding a health–loneliness
relationship (e.g., Fees et al., 1999; Russell, 1996). Our results showed that,
even after accounting for other variables such as gender and education,
poorer health was associated with greater loneliness. This study also repli-
cated past research (e.g., Jylha, 2004) in showing that no direct relationship
between age and loneliness exists, after controlling for other age-related
variables. In the cross-sectional analyses, that increasing age predicted less
social participation suggests one possible intervening variable between age
and loneliness. Participants’ level of functionality or independence was also
indirectly related to loneliness through social participation. That is, parti-
cipants who were more independent participated in more social activities,
which, in turn, related to less loneliness.
Strengths, limitations, and conclusions
There were several strengths of the study, including the opportunity to
longitudinally examine loneliness among a large, representative sample of
older adults that included those who were aged 85 years and older. Access
to longitudinal data enabled us to consider the replicability of results estab-
lished in cross-sectional analyses. We have greater confidence in the gener-
alizability of these results because, unlike past studies using convenience
samples, our sample was initially selected to reflect the population of older
adults in the province of Manitoba, Canada.
Some limitations of the study should also be noted. In particular, we had
available only single items to assess different types of affiliative causal
beliefs. A preferable method would have been to have participants offer
reasons for their lack of friends and greater loneliness and subsequently to
have them rate these causes on the dimensions of internality, controllabil-
ity, and stability. Another limitation is that research questions relating to
causality cannot be definitively answered in this study; it is only possible to
determine how concepts vary in relation to others and not strictly whether
one variable caused changes in another variable. For example, although it
was argued that causal beliefs predicted social participation, it is also
Newall et al.: Loneliness among older adults 287
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possible that the reverse holds true. Although the present study does not
preclude this possible interpretation, the study findings are consistent with
attribution theory and with the Controllability Attributional Model, which
propose that cognitions influence motivation and behaviours (Anderson &
Riger, 1991; Weiner, 1985).
To conclude, our findings provide insights into how the causal dimensions
of internality/externality and controllability relate to loneliness for older
adults. In addition to replicating past research, which shows a relation be-
tween greater loneliness and uncontrollable causal attributions (Anderson
& Riger, 1991), our research provides a more nuanced picture of the
relative importance of causal beliefs for social participation and loneliness.
In particular, the findings point to the value of focusing not only on how
older adults’ health and social activities relates to loneliness, but also on
the potentially modifiable beliefs people hold, how these can be changed,
and the effects these changes will likely have on people’s loneliness in the
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... Cognitive aspects such as causal attributions of loneliness, perceived control, self-efficacy and expected feelings of loneliness in old age have consistently been shown to be associated with feelings of loneliness among the general and older population (Cohen-Mansfield & Parpura-Gill, 2007;Fry & Debats, 2002Newall, 2010;Newall et al., 2009Newall et al., , 2014Nieboer et al., 2020;Peplau et al., 1982;Perlman & Peplau, 1998;Pikhartova et al., 2016). Cacioppo and Hawkley (2009) suggest that the persistence or chronification of this feeling is caused by a vicious cycle powered by feelings of loneliness, cognitive biases and negative social interactions. ...
... Cacioppo and Hawkley (2009) suggest that the persistence or chronification of this feeling is caused by a vicious cycle powered by feelings of loneliness, cognitive biases and negative social interactions. Newall et al. (2009Newall et al. ( , 2014 confirm that cognitive and behavioural aspects, such as causal attributions and social participation, are predictors of loneliness in old age. They also found that internal, controllable attributions (regarding personal effort) had a direct and indirect relationship with loneliness mediated by social participation. ...
... However, most reviews report their effectiveness to be very low (Cattan et al., 2005;Dickens et al., 2011;Gardiner et al., 2018;Siette et al., 2017). These findings have prompted a shift in the way loneliness is addressed from a therapeutic model to a preventive one focused on modifiable psychosocial predictors of loneliness (Bolton, 2012;Holt-Lunstad, 2017;de Jong Gierveld & Fokkema, 2015;Lorente, 2017;Newall et al., 2009Newall et al., , 2014Nieboer et al., 2020). ...
Full-text available
This study aims to analyse the effectiveness of a psychosocial intervention programme in preventing loneliness and increasing self-efficacy and social participation among older women living alone in Spain. A sequential, nested experimental design was used, with a primary quantitative quasi-experimental pre-post study with a control group (CG) and a secondary qualitative study. A total of 48 women participated, and 34 of them (the experimental group, EG) received a 25-hr psychosocial support intervention delivered by volunteers who focused on three aspects: conversation, attribution retraining and behavioural activation. The other women remained on the waiting list (CG = 14). The women (EG and CG) were all interviewed before and after the intervention and a follow-up of the EG was performed at 6 months (N = 25). Semi-structured interviews were used to collect sociodemographic and health-related data, as well as data on satisfaction with the programme and its benefits. Validated instruments were used to collect data on the dependent variables (self-efficacy in ageing, subjective social participation and loneliness). Quantitative and qualitative methods were used for data analysis. Compared to the pre-test results, women in the EG improved their self-efficacy in ageing (t-test: −2.246; p: 0.031; d: 0.30), and, more specifically, their self-efficacy in managing socio-emotional problems such as loneliness (t-test: −1.995; p: 0.054; d: 0.25) and in managing their health (t-test: −2.450; p: 0.020; d: 0.47). No significant differences were observed between the follow-up and the post-test results, meaning that the changes identified after the intervention remained stable. Four additional categories of benefits were identified in the participants' discourses. In conclusion, the intervention appears to be beneficial and effective in improving self-efficacy, which is a well-established predictor of loneliness in old age, in a group of vulnerable individuals. Further studies on this type of community intervention are necessary to prevent loneliness among older people.
... Instead, they may find themselves reliant on others and may feel indebted for any help that non-spousal others provide. Interventions that increase perceived control over social aspects of life have been shown to alleviate loneliness (Newall et al., 2009;Schulz, 1976) and suggest that increasing older adults' control over other aspects of life may also have benefits. Additional research is needed to understand whether providing greater autonomy and control over the management of health limitations may alleviate risk for loneliness. ...
Full-text available
This report examines the social connectedness of older adults in the United States. Social connectedness is here defined as satisfaction and contentment with one’s social life. Its opposite, loneliness, is defined as dissatisfaction with the quantity or quality of one’s social relationships. Social isolation, or being alone, is related to loneliness but is not the same as feeling alone, which we call loneliness. Loneliness, in addition to making people’s lives miserable, has been associated with increased mortality and a range of adverse physiological and health outcomes that are prevalent and costly in older age. The scope of the problem is related to the prevalence of loneliness in older adults in the USA, and current estimates of prevalence are not yet available. Relatively recent data are available, however, and this report uses those data to estimate prevalence. Moreover, characteristics of lonely older adults are not well understood, and segments of the current older adult population who are at high risk for loneliness have not been identified. This report addresses these issues. Data derive from the National Social Life, Health and Aging Project (NSHAP), a panel study funded by the National Institutes of Aging (NIA) in which a population-based sample of 3,240 older adults was surveyed in 2010 when participants were 62–91 years of age. Results showed that 19% of older adults in the United States suffer from loneliness; 8% of older adults often feel lonely, and 11% feel lonely at least some of the time. Socioeconomic and demographic characteristics differ between the socially connected group and the lonely group, as do health, social engagement, social network characteristics, and positive and negative qualities of relationships. Relative to the socially connected group, the lonely group has lower household income and less wealth, is less likely to be married, and lives alone. The lonely group also has poorer self-rated health, more physical limitations in carrying out the activities of daily living, and fewer friends. They socialize, volunteer, attend religious services, and participate in organized groups less frequently than the socially connected group. In addition, the lonely group reports less support and greater strain in their relationships with family and friends. Data about the older population in general can be used to identify which individuals may be at particular risk for loneliness. In this nationally representative sample of older adults, the risk of loneliness is higher for those who do not have a spouse or partner, socialize less frequently, have fewer friends, or experience greater strain in family relationships. Having information on these four aspects of older adults’ lives significantly increases the likelihood that individuals can be identified as either lonely or socially connected. The ability to predict which individuals are lonely or socially connected can be further improved by considering married and unmarried older adults separately, in part because the married and unmarried groups differ in more ways than marital status. For instance, married older adults are more likely to have a higher income than those who are not married. When examined separately, 14% of married older adults and 30% of unmarried older adults fall into the lonely group. Married women are at higher risk of loneliness than married men, but unmarried women are at lower risk of loneliness than unmarried men. In addition, the subgroup most at risk of loneliness among married older adults includes those who have lower income, attend religious services relatively infrequently, and experience poor marital quality. Among unmarried older adults, the most at-risk subgroup includes those who have more physical limitations, fewer friends, and greater strain in their family relationships. It is important to note that some of the risk factors for loneliness are also consequences of loneliness. For instance, loneliness is reciprocally related to physical limitations; not only are people with physical limitations more likely to experience the onset of loneliness or increase in its frequency, but loneliness also predicts an increase in functional limitations over time (Luo, Hawkley, Waite, & Cacioppo, 2012). Similarly, strain in family relationships can lead to feelings of loneliness, but loneliness can also make a person an unpleasant interaction partner, thereby increasing strain in relationships. These issues are not addressed further in this report, but draw attention to the need for longitudinal research to untangle the temporal ordering that links loneliness and its various causes and consequences. In sum, although most older adults in the USA are socially connected and seemingly resilient to the losses that come with aging, a sizeable portion of this population feels lonely. Data identified segments of the population that may be at particularly high risk of loneliness. This information may be useful in setting directions for future research, targeting policies, and helping service agencies reduce the burden of loneliness in a growing older adult population.
... Social loneliness refers to the absence of a wider circle of friends and acquaintances that provide a sense of belonging, companionship, and being a member of a community. Correlates of social loneliness among older people may include, for instance, reduced social activities (Newall et al., 2009) and a lack of connection with ones' own neighborhood (Scharf et al., 2005). Emotional loneliness refers to the absence of an attachment figure in one's life and someone to turn to (Drennan et al., 2008;Dykstra & Fokkema, 2007). ...
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Using the European Social Survey and the UN databases, this paper investigates the differences in feelings of loneliness among different marital status groups in old age. Findings presented in this paper suggest that married elders are the least lonely group, while never-married elders come thereafter, better than widowed, divorced, and separated elders. Overall, while married individuals are the happiest and the least lonely of all groups, marriage, on average, ends up with less benign results in late stages of life. The discussion part in this paper suggests that these findings might fit with the “greedy marriage” argument that long-term never-married people develop social skills and circles over time, which allows them to be more immune to loneliness and depression than widowed, divorced, and separated elders.
... First, since the present study consists of a cross-sectional analysis, these results only represent correlational associations. However, a comparison of longitudinal results with cross-sectional results in similar research contexts indicates that they may well be very comparable (Newall et al., 2009). Nevertheless, further research should shed more light on the dynamic nature of the interactions found. ...
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Lonely students typically underperform academically. According to several studies, the COVID-19 pandemic is an important risk factor for increases in loneliness, as the contact restrictions and the switch to mainly online classes potentially burden the students. The previously familiar academic environment (campus), as well as the exchange with peers and lecturers on site, were no longer made available. In our cross-sectional study, we examine factors that could potentially counteract the development of higher education student loneliness during the COVID-19 pandemic from a social network perspective. During the semester, N = 283 students from across all institutional faculties of a German comprehensive university took part in an online survey. We surveyed their social and emotional experiences of loneliness, their self-reported digital information-sharing behavior, and their current egocentric networks. Here, we distinguished between close online contacts (i.e., mainly online exchanges) and close offline contacts (i.e., mainly in-person face-to-face exchanges). In addition, we derived the interconnectedness (i.e., the densities of the egocentric networks) and heterogeneity (operationalized with the entropy) of students' contacts. To obtain the latter, we used a novel two-step method combining t-distributed stochastic neighbor embedding (t-SNE) and cluster analysis. We explored the associations of the aforementioned predictors (i.e., information-sharing behavior, number of online and offline contacts, as well as interconnectedness and heterogeneity of the close contacts network) on social and emotional loneliness separately using two hierarchical multiple linear regression models. Our results suggest that social loneliness is strongly related to digital information-sharing behavior and the network structure of close contacts. In particular, high information-sharing behavior, high number of close contacts (whether offline or online), a highly interconnected network, and a homogeneous structure of close contacts were associated with low social loneliness. Emotional loneliness, on the other hand, was mainly related to network homogeneity, in the sense that students Frontiers in Psychology | 1 January 2022 | Volume 12 | Article 733867 Hopp et al. Social Networks and Loneliness with homogeneous close contacts networks experienced low emotional loneliness. Overall, our study highlights the central role of students' close social network on feelings of loneliness in the context of COVID-19 restrictions. Limitations and implications are discussed.
... Attribution theory proposes that people will attribute their unfortunate experience to the external environment or themselves (Kelley, 1967). Styles of individual attribution depend on internality, that is, attributing to internal or external factors (Hymel et al., 1985;Newall et al., 2009). In contrast, according to self-protection or self-serving bias theory, most people tend to attribute success to themselves and failure to the situation (i.e., denial of self-responsibility; Miller and Ross, 1975;Mezulis et al., 2004) because people are willing to protect their self-concept positively (Campbell and Sedikides, 1999), which is defined as those who have different stable and definite characters in the phenomenal field (Syngg and Combs, 1949). ...
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Previous research has mostly focused on Internet use behaviors, such as usage time of the Internet or social media after individuals experienced offline social exclusion. However, the extant literature has ignored online response behaviors, such as online review responses to social exclusion. To address this gap, drawing on self-protection and self-serving bias, we proposed three hypotheses that examine the effect of offline social exclusion on online reviews, which are verified by two studies using different simulating scenarios with 464 participants. The results show that when individuals are socially excluded offline, regardless of where the exclusion comes from (businesses or peers), they will be more likely to give negative online reviews. In addition, brand awareness moderates the effect of offline social exclusion on online reviews. Specifically, if the brand is less known, compared with social inclusion, offline social exclusion will lead individuals to give more negative online reviews; conversely, for well-known brands, no significant difference exists in the online reviews between social exclusion and inclusion.
... Autonomy does not equal independence from others but refers to feelings of volition, or being in control of one's actions (Deci & Ryan, 2008). It is thus related to other concepts such as perceived control or self-efficacy, which have been associated with loneliness in previous studies (Andrew & Meeks, 2018;Newall et al., 2009;Suanet & van Tilburg, 2019). ...
Objectives Loneliness is an important risk factor for mental and physical health over the life span. Little is known about psychosocial predictors and consequences of loneliness apart from social network characteristics. One important factor that may both prevent from, but also be affected by loneliness, is perceived autonomy. Method In the present study, we investigated the longitudinal association of loneliness and autonomy over four years among participants of the Swedish Health, Aging and Retirement Transitions in Sweden (HEARTS) study (n = 5718, age 60–66 at baseline). We used a latent curve model with structured residuals, which distinguishes within- and between-person associations and includes cross-lagged parameters. Results Higher levels of autonomy at baseline were associated with lower levels of loneliness, and increases in autonomy were associated with decreases in loneliness. When individuals felt more autonomous than usual, they also reported less loneliness. However, the cross-lagged paths were not significant, which means that autonomy did not predict loneliness over time on the within-person level, and loneliness did not predict autonomy over time. Conclusion Our findings show that higher autonomy was related to lower loneliness on different analytical levels, but the direction of effects is unclear. More research is needed to understand the development of this association over the life span.
... Older adults frequently participate in social activities and make friends with others. Interactions with others might reduce feelings of loneliness (Newall et al., 2009). Welldocumented evidence indicates that social engagement might ameliorate older adults' depression and improve their selfrated health and life satisfaction. ...
Background and purpose Social engagement is an important active aging strategy to promote older adults’ mental health. The purposes of this study were to compare social engagement in older populations around the world and explore associations with mental health outcomes. Materials and methods An international cross-sectional survey was conducted from 2017 to 2019. Data were retrieved from The International Social Survey Programme for a secondary data analysis across 30 countries. This study applied the Taxonomy of Social Activities and its six levels as operational definitions for a consistent concept of social engagement for international comparisons. Results In total, 9403 older adults with a mean age of 72.85 ± 6.40 years responded. The highest levels of older adults’ social engagement were found in Switzerland, Thailand, and New Zealand. Older adults of a higher age, with a lower educational level, who were permanently sick or disabled, who had no partner, who were widowed or whose civil partner had died, who lived alone, and who had lower self-placement in society had significantly lower social engagement than did their counterparts. In the regression model, older adults’ social engagement positively predicted general health, self-accomplishment, and life satisfaction, but negatively predicted loneliness and depression. Conclusions In aging societies worldwide, encouraging older adults' social engagement would be beneficial to promote mental health. Implications for nursing practice and health policies Community professional nurses can develop strategies of social engagement based on the needs and sociodemographic factors of older adults to improve their mental health. Developing efficient strategies and local policies by learning from successful experiences in other countries is important to promote social engagement in aging societies.
Social integration plays a key role in the quality of life of the elderly who move from a rural to an urban area (land-lost elderly), and successful urban integration is known to develop harmony and age-friendly society. Very few studies are concerned with incorporating subjective as well as objective elements into measuring social integration levels of land-lost elderly. In this study, we measured and compared social integration levels between different neighbourhoods of land-lost elderly. We found land-lost elderly in the public housing neighbourhoods, and compared to the commodity housing elderly, they have a higher level of social integration in three aspects: having positive attitudes towards urban life, having high participation rate in activities, and participating activities with neighbours. This is because the elderly living in public housing have neighbours from similar socio-demographic groups and have more social interactions. In addition, the built environments are closely related to social integration levels of the elderly in both neighbourhoods, but effects in the two neighbourhoods present a few differences; the improvement in social environments can greatly facilitate social integration of elderly living in public and commodity housing neighbourhoods, especially for improvement in social contacts with neighbours.
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BACKGROUND: Social interactions are vital for our wellbeing, particularly during times of stress. PURPOSE: We investigated the real-time effect of social interactions on changes in stress and mood using an ecological momentary assessment approach in 732 participants during COVID-19 lockdown in spring 2020 and in a subsample of these participants (n= 281) during a further lockdown in winter 2020. METHODS: Participants reported their stress and mood in a smartphone app five times per day for seven days and indicated the nature and frequency of their recent social interactions. RESULTS: Overall, social interactions and their frequency were associated with enhanced momentary mood. In person interactions, but not those that were not in person (e.g., via audio, video, or text), were linked to lower stress, especially if they were with closer others. Individuals scoring high on trait loneliness benefited least from social interactions in terms of their momentary mood, whereas those scoring high on trait depressive symptoms benefited the most. Our key findings replicated across both lockdowns. CONCLUSIONS: This study demonstrates the benefits and limits of social interactions for improving momentary mood and stress during psychologically demanding periods and highlight how clinically relevant individual differences can modulate these effects.
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In this chapter a theory of motivation and emotion developed from an attributional perspective is presented. Before undertaking this central task, it might be beneficial to review the progression of the book. In Chapter 1 it was suggested that causal attributions have been prevalent throughout history and in disparate cultures. Studies reviewed in Chapter 2 revealed a large number of causal ascriptions within motivational domains, and different ascriptions in disparate domains. Yet some attributions, particularly ability and effort in the achievement area, dominate causal thinking. To compare and contrast causes such as ability and effort, their common denominators or shared properties were identified. Three causal dimensions, examined in Chapter 3, are locus, stability, and controllability, with intentionality and globality as other possible causal properties. As documented in Chapter 4, the perceived stability of a cause influences the subjective probability of success following a previous success or failure; causes perceived as enduring increase the certainty that the prior outcome will be repeated in the future. And all the causal dimensions, as well as the outcome of an activity and specific causes, influence the emotions experienced after attainment or nonattainment of a goal. The affects linked to causal dimensions include pride (with locus), hopelessness and resignation (with stability), and anger, gratitude, guilt, pity, and shame (with controllability).
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A large-scale questionnaire study was conducted to test several aspects of different attributional models of everyday problems in living. College students completed scales assessing depression, loneliness, and shyness. In addition, they completed a questionnaire that measured attributional style on five causal dimensions (locus, stability, controllability, globality, and intentionality) for four types of situations (interpersonal success and failure, noninterpersonal success and failure. The results of a series of regression and correlation analyses led to the following major conclusions: (1) Globality, intentionality, and stability may be dropped from attributional models of depression, loneliness, and shyness with little loss of predictive power; (2) controllability is the single most important dimension in predicting a person's level of depression, loneliness, or shyness; (3) locus adds to the prediction of these symptoms only w hen assessed by failure items; and (4) attributional style predicts these ...