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. Author manuscript; available in PMC: 2026 May 26.
Published before final editing as: J Posit Psychol. 2025 May 26:10.1080/17439760.2025.2500565. doi: 10.1080/17439760.2025.2500565

Purpose in life and loneliness among African American women: an application of latent growth mixture modeling

Eunbea Kim 1, Carolyn Cutrona 2, Daniel Russell 1
PMCID: PMC12333313  NIHMSID: NIHMS2085730  PMID: 40857457

Abstract

This study investigated the extent to which purpose in life predicted African American women’s loneliness over time. Using data from 661 African American women (Mage = 44.92, SD = 8.10) across four waves of the Family and Community Health Study (FACHS), latent growth mixture modeling was employed to explore the trajectories of loneliness across approximately 10 years and whether level of purpose in life was a significant predictor of the trajectories. This study also accounted for potent influential factors such as positive and negative social support, religiosity, racial discrimination, and financial strain as covariates. Findings revealed significant between-individual differences in loneliness trajectories, with individuals reporting a higher initial level of purpose in life tending to have lower levels of loneliness over time. Thus, greater purpose in life predicted lower loneliness among African American women, highlighting the importance of these factors in their psychosocial well-being.

Keywords: Loneliness, African American, Purpose in life, Longitudinal studies

Purpose in life has been widely proposed as a significant predictor of positive health outcomes, including lower risk of chronic diseases (Boyle et al., 2010), physical disability (Cohen et al., 2016), and mental illness (Battersby & Phillips, 2016; Wood & Joseph, 2010; Steger et al., 2009; Van der Heyden et al., 2015). Purpose in life refers to a person’s subjective perception that one’s life and existence have significance, direction, and coherence (King et al., 2006; Martela & Steger, 2016; Ryff, 2013; Steger, 2012). Individuals with a higher level of purpose in life tend to pursue meaningful activities, long-term goals, and relationships in their lives (Krause, 2007; Pinquart, 2002; Stillman et al., 2009). Their active pursuit of relationships and social activities may be associated with lower levels of loneliness. Even when social resources are scarce, a higher level of purpose in life may diminish feelings of loneliness and social isolation (Krause, 2007).

Few studies have examined psychological predictors of loneliness among racial/ethnic minority populations. The current study will examine the trajectories of loneliness and whether purpose in life may serve to lessen loneliness among African American women. Systemic discrimination, personal experiences of discrimination in daily life, financial strain, and poorer health conditions have the potential to threaten African Americans’ mental health, sense of belonging, and social integration, possibly leading to loneliness (Chang, 2018; Charron-Chénier & Mueller, 2018; Clark et al., 1999; Erving & Frazier, 2021; Fisher et al., 2014; Kahn & Fazio, 2005; Nadimpalli et al., 2015). For many African American women, religiosity and spirituality form the core of their purpose in life, which may serve as an important psychological factor in lowering loneliness. The main aim of the current study is to examine African American women’s purpose in life as a predictor of psychosocial well-being, which is operationalized as a low level of loneliness (Buczak-Stec et al., 2023; Van den Eijnden et al., 2008). Furthermore, the study examines the roles of contextual factors such as racial discrimination, financial strain, health conditions, and religiosity as components that may influence purpose in life.

Purpose in Life and Psychosocial Well-being

The Psychological Well-being model by Ryff (1989) highlights the process of personal growth and self-realization as a route to psychological well-being beyond personal gratification (Deci & Ryan, 2008; Ryff, 2013). Psychosocial well-being is defined as a superordinate construct of emotional, mental and social well-being (Eiroa-Orosa, 2020; Larson, 1996). It is often operationalized using indicators such as life satisfaction or loneliness, capturing the multidimensional well-being. Extant literature suggests that psychological well-being can serve as a personal and motivational resource to relieve distress and benefit psychosocial well-being (Kim et al., 2022; Windsor et al., 2015).

Ryff proposed six dimensions of Psychological Well-being: purpose in life, autonomy, personal growth, environmental mastery, positive relationships, and self-acceptance (Ryff, 2013; Ryff & Singer, 2008). In her view, higher of all six dimensions represent psychological maturity and positive functioning, which are associated with psychological well-being. Among these dimensions, purpose in life addresses comprehensive life purpose and direction. Purpose in life serves as a motivational resource that drives individuals to invest more effort in pursuing life goals, including social relationships and meaningful activities. This study focuses specifically on purpose in life as a resource that may enhance psychosocial well-being in African American women.

Previous studies have identified ways in which purpose in life contributes to psychosocial well-being through a variety of mechanisms. It promotes social networking, resulting in an increase of sense of belongingness (Folker et al., 2021; Pinquart, 2002). Empirical studies have shown that individuals having a greater sense of purpose in life tend to be socially attractive to others, regardless of their objective physical attractiveness (Folker et al., 2021; Stillman et al., 2011). Furthermore, they tend to exhibit a positive orientation towards others, leading to the development of social connections (Stavrova & Luhmann, 2016). Recent longitudinal studies have demonstrated that purpose in life predicts a larger social network and more frequent social contacts (Kim et al., 2022; Lee & Martin, 2023). These, in turn, serve as resources for enhancing psychosocial well-being.

Loneliness and Purpose in Life

Loneliness is the distressing feeling that arises when individuals perceive that their social needs are not being met by their actual social relationships (Perlman & Peplau, 1981; Russell et al., 1980). It encompasses emotional, mental and social dimensions of well-being, with higher levels of loneliness often accompanied by difficulties in these domains (Cacioppo et al., 2006). As discussed earlier, a greater sense of purpose in life is associated with social skills and attractiveness to others, contributing to better social connections (Stavrova & Luhmann, 2016; Stillman et al., 2011), thus lowering the probability of loneliness. Purpose in life may also alleviate loneliness by facilitating emotion regulation abilities and coping skills in dealing with negative affect that arises from interpersonal situations (Folker et al., 2021; Kashdan & Goodman, 2023; Krause, 2007). Purpose in life fosters a positive outlook on the world and others (Ho et al., 2010). This positive perspective may influence the process of how individuals interpret their interpersonal situations or interactions, thereby resulting in lower levels of loneliness.

Recent studies have documented the association between purpose in life and lower levels of loneliness. In a study of men over age 60, Neville et al. (2018) found that a low level of purpose in life was associated with higher levels of loneliness. Kang and colleagues (2021) recruited adults online to investigate the buffering effect of purpose in life against loneliness during the COVID-19 pandemic. They found that individuals with stronger purpose in life were less likely to experience loneliness and more likely to engage in COVID-preventive behaviors. A study by Kim et al. (2022) tested the effect of change in purpose in life on health and well-being outcomes (e.g., all-cause mortality, number of chronic conditions, health behaviors, psychological well-being and distress, and social factors, including loneliness) using three waves of Health and Retirement Study data (2006/2008, 2010/2012, and 2014/2016). Results showed that an increase in purpose in life was associated with lower loneliness as well as other positive outcomes. A study by Hill and colleagues (2023) employing a cross-sectional method with Swiss adults also indicated a negative relationship between a sense of purpose and loneliness. In a meta-analysis based on thirty-six cohorts from ten studies, Sutin and her colleagues (2022) examined the cross-sectional and longitudinal relationship between purpose in life and loneliness and showed that greater purpose in life is associated with lower levels of loneliness and fewer new incidents of loneliness.

Although previous studies have shown a significant negative association between purpose in life and loneliness, more research is needed to employ longitudinal methods to further clarify the effects of purpose in life on loneliness. Moreover, studies have rarely addressed the role of purpose in life in levels of loneliness among people of color.

African American Women: Purpose in Life predicting Loneliness

In the U.S., systemic racial/ethnic discrimination contributes to the disadvantages faced by African Americans, some of which are related to social isolation and loneliness (e.g., low levels of income, poorer health conditions, unsafe neighborhoods, degraded living environments, and low accessibility to community resources; Cacioppo & Cacioppo, 2014; Lubben et al., 2015; Redwood et al., 2010; Ross & Mirowsky, 2001; Schulz et al., 2002; Williams & Collins, 2001). This can be explained with the concept of collective loneliness - feeling disconnected from one’s valued social identities and social networks, and being prevented from connecting with others in the community (Cacioppo et al., 2015; Weiss, 1973) as outlined in the loneliness model by Hawkley and colleagues (2005; 2012).

Researchers have speculated about the mechanisms through which racial discrimination impacts loneliness. First, experiences of daily racial discrimination amplify feelings of loneliness. Daily discrimination threatens one’s security and belongingness within the community, possibly leading to loneliness (Lee & Bierman, 2019; Lee & Turney, 2012; Lim et al., 2018; Majeno et al., 2018; Sutin et al., 2015). For African Americans, daily discrimination is a significant stressor, causing depressive symptoms, and potentially, greater social isolation and loneliness (Chang, 2018; Clark et al., 1999; Nadimpalli et al., 2015). African American women face additional challenges from gender-based discrimination (Chang, 2018). Moreover, financial strain, which is prevalent among African Americans, further limits social networking opportunities, thereby intensifying loneliness (Cheung et al., 2019; Hawkley et al., 2020; Hutten et al., 2021; Kahn & Fazio, 2005; Loibl et al., 2021).

Countering the experiences that increase loneliness, African American women have a unique culture of religiosity and spirituality from which they may derive a strong sense of purpose in life (Chatters et al., 2014; Ko et al., 2016). Compared to other demographic groups, African American women are more actively involved in religious communities and activities and report higher levels of both religiosity and spirituality as well as higher perceived social support (Chatters et al., 2014; Mattis, 2002; Taylor et al., 2005; Taylor et al., 2007). Religious beliefs may endow African American women with a strong sense of purpose in their lives (Ko et al., 2016; Krause, 2008). Purpose in life has significant connections to physical and psychological well-being among African Americans (Mattis, 2002; Paranjape & Kaslow, 2010; Watlington & Murphy, 2006). However, little research has examined the effect of purpose in life on the specific outcome of loneliness among African American women.

The purpose of the current study is to identify between-individual differences in loneliness trajectories, followed by examining the extent to which purpose in life among African American women is associated with their level and change in loneliness over time. Considering the features of the target population, we control for known risk factors for loneliness, including racial discrimination and financial strain, and resulting limited social network ties. Further, since religious faith has been considered an important value among African American women, we will include religiosity as a covariate in testing whether change in loneliness is explained by purpose in life. We expect that women with higher purpose in life will have a lower level of loneliness even when controlling for those covariates. To test the predictions, we employ latent growth mixture modeling. This method allows one to identify groups based on individual differences in initial loneliness and trajectories of loneliness over time and to assess how purpose in life and control variables predict membership in the groups defined by differing levels of loneliness over time.

Hypothesis: A higher initial level of purpose in life will predict lower levels of loneliness over time.

Methods

Participants

This study involved 661 African American women from four waves (Wave 4 through 7) of the Family and Community Health Study (FACHS; Cutrona et al., 2000; 2003). The FACHS is a large-scale longitudinal study of African American families residing in rural and suburban Georgia and Iowa. All procedures were approved by the Iowa State University and University of Georgia Institutional Review Boards. Written informed consent was obtained from all participants.

For sample selection, the FACHS used 1990 census data to identify block group areas (BGAs) with 10% or higher African American families in both Georgia and Iowa. At each wave of interviews, caregivers and children who participated were reimbursed $100 and $70, respectively. The FACHS successfully recruited 61% to 68% of eligible families (Cutrona et al., 2000).

In evaluating the representativeness and variability of the sample, the FACHS team compared the included census tracts with excluded census tracts in each state based on average family income from the 1990 census data. While there was no significant difference in income between included and excluded census tracts in Iowa, the average family income of included census tracts from Georgia was lower than excluded census tracts. This indicates that high-income census tracts in Georgia were underrepresented (i.e., $45,000 or more in 1990 income). However, since the sample includes a large number of both lower- and middle-class individuals from Georgia, the FACHS team concluded that the sample is a representative set of neighborhoods from these two states (Murry et al., 2008; Stewart et al., 2007).

In the current study, we included the 661 African American women who provided complete data on the key variables, which required participation from Wave 4 (W4; 2005 – 2006) to 7 (W7; 2015 – 2017) of the study. The reason we selected these waves is that the measure of purpose in life was only available at W4, and loneliness was not measured after Wave 7.

Measures

Purpose in Life.

To assess participants’ level of purpose in life, we used the seven-item Purpose in Life subscale from the 42-item version of Ryff’s Psychological Well Being Scale (1989; 1995). The items assess the clarity of individuals’ goals in life and a sense of directedness or meaningfulness using a 6-point Likert scale (1 = strongly disagree to 6 = strongly agree). The scale is scored by computing the average response after the five negatively-worded items are reverse coded (e.g., “My daily activities often seem trivial and unimportant to me.”). Higher scores indicate greater purpose in life. The Cronbach’s alpha coefficient for the current study was .69 at Wave 4. Validity of the scale among African American participants was evidenced by significant correlations with measures of psychological distress, including symptoms of depression and anxiety (r = −.35, p <.001; The Mini Mood and Anxiety Symptoms Questionnaire; Clark & Watson, 1995), and optimism (r = .49, p <.001; The Life Orientation Test; Scheier & Carver, 1985).

Loneliness.

This study employed six items from the UCLA Loneliness Scale Version 3 by Russell (1996). This scale assesses the subjective feeling of a lack of social relationships or of high-quality relationships. Using a frequency scale (1 = never to 4 = always), three of the six items indicate how often participants feel a lack of social relationships (e.g., How often do you feel left out?), and the other three items indicate the frequency of positive social experiences (e.g., “How often do you feel close to people?”). After reverse coding the 3 items describing positive social experiences, the scale is scored by computing the average score. Higher scores indicate greater loneliness. Cronbach’s alpha coefficients ranged from .78 to .85 across the four waves of assessments.

Control variables.

Religiosity.

To test the influence of religiosity, investigators administered three different measures addressing religiosity: Organizational Religiosity, Non-Organizational Religiosity, and Subjective Religiosity. The first five items assessing organizational religiosity were developed by Simons et al. (1995) and ask about the frequency of organizational activities related to religion in the past month (e.g. “How often in the past months did you attend church services?”) using a 5-point response format (1 = Never to 5 = Daily). Non-organizational religiosity and subjective religiosity include items from a multidimensional measure of religious involvement by Levin and colleagues (1995). Non-organizational religiosity includes four items that indicate the frequency of non-organizational religious activities (e.g., “How often do you pray?”) These items use a 3-point response format (1= Often to 3 = Never). Subjective Religiosity includes three items representing individuals’ perceptions of their level of religiosity (e.g., “How religious would you say you are?”) and uses a 4-point Likert scale (1= very religious to 4 =Not at all religious). To ensure consistency across the three measures, we reverse-coded negatively-worded items within the Nonorganizational Religiosity and Subjective Religiosity scales. The averages of the responses for each scale were calculated, then each scale was standardized and averaged to generate a composite religiosity variable. Higher scores indicate a greater level of religiosity for each component (α = .79).

Positive and negative support.

Considering the strong influence of perceived social support on loneliness (Russell, 1996; Zhang & Dong, 2022), we incorporated measures of perceived positive and negative social support as control variables in our analyses. These measures were created based on a study conducted by Cohen & Hoberman (1983). The positive social support measure consists of six items that assess the extent to which individuals perceive receiving positive support from their closest relative and best friend (e.g., “How much does your best friend make you feel appreciated, loved or cared for?”). The negative social support measure includes four items asking about negative interactions with the respondent’s closest relative and friend (e.g., “How much conflict, tension, or disagreement do you feel there is between you and your best friend?”). The two measures have a 3-point Likert response scale (1 = a lot to 3 = not at all). Responses to all items were reversed and averaged, so higher scores indicate higher positive social support (α = .81) and higher negative social support (α = .70), respectively.

The current study also included other control variables such as age, marital status, self-reported health, physical functioning, and depression. Many studies have indicated that these variables are related to level of loneliness (Cacioppo et al., 2002; Hawkley & Cacioppo, 2010). In addition, racial discrimination experiences and financial strain were included as control variables because they have been shown to be related to loneliness among African Americans (Chang, 2018; Cheung et al., 2019; Clark et al., 1999; Hawkley et al., 2020; Hutten et al., 2021; Kahn & Fazio, 2005; Loibl et al., 2021; Nadimpalli et al., 2015).

Procedure

Before starting data collection, the FACHS research team employed African American women from neighborhoods similar to those included in the sample to review interview questions. They provided suggestions for the modification of items that might come off as culturally insensitive, intrusive, or unclear. Data were collected through in-person administration of questionnaires. Participants were interviewed by African American interviewers at their homes or other places near their homes.

Analysis Plan

Before conducting the main analysis on the relationship between purpose in life and loneliness, we conducted preliminary descriptive analyses (means, standard deviations, and correlations among variables. For the main analysis, we used latent growth mixture modeling (LGMM) with four waves of loneliness data to answer our research questions: (1) Does an initially higher level of purpose in life predict lower loneliness? and (2) Does the initial level of purpose in life predict change in loneliness across time? The LGMM approach provides information about between-individual differences in intraindividual change by identifying sub-populations (i.e., classes) based on the heterogeneity in the trajectories of the construct of interest for each participant (i.e., loneliness in the current study; Jung & Wickrama, 2008). For each class, the model estimates parameters for the initial level (intercept) and trajectories (slope) of the variable of interest for each class. Then, the model provides coefficients that allow estimation of the probability that the participants who have higher or lower levels of predictors belong to each class. In this study, given the four waves of observations of loneliness, we included a linear slope term to reflect change in loneliness (see Figure 1).

Figure 1. Conceptual model of the Latent Growth Mixture Analysis.

Figure 1.

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Notes. This study employed 4 waves (W4 – W7).

To observe between-individual differences in loneliness across time, we estimated three unconditional models including two, three, and four classes based on the trajectories of loneliness using M-plus version 8.7 software. Given the increased amount of computing time of the bootstrap likelihood ratio test (BLRT; Jung & Wickrama, 2008; Nylund et al., 2007), the model with the optimal number of classes was selected based on Bayesian Information Criterion (BIC) and likelihood ratio test p value (LMR-LRT) first. Then, we requested the BLRT and the total count in a class to determine the number of classes.

Next, we investigated the heterogeneity of loneliness across classes and the predictive effect of purpose in life on the initial level and the change of loneliness across the classes. Control variables were added to the selected model, including marital status, income, and health-related variables (e.g., self-reported health, physical function, and depressive symptoms) that were significantly related to the level of loneliness. We also included risk factors for loneliness such as racial discrimination, positive and negative support from social networks, and financial strain. Finally, we added the composite religiosity measure to control its role in the effect of purpose in life on loneliness. Before running these analyses, all predictors were centered. To determine the predictive effects of purpose in life and control variables on class membership based on the trajectories of loneliness, we examined the odds ratios for a logistic regression using the R lavaan package.

Results

Descriptive Statistics and Bivariate Correlations

Table 1 shows descriptive statistics for the sample at Wave 4 (n = 661). The average age of participants was 44.92 years (SD = 8.10); their age ranged from 20 to 89 years old. About half of the participants did not have a college degree (53.1%) and reported less than $35,000 in annual household income (55.8%).

Table 1.

Descriptive Statistics

Variables n (%) M (SD) Range
Loneliness
  W 4 1.57 (.57) 1 – 4
  W 5 1.57 (.53) 1 – 4
  W 6 1.51 (.55) 1 – 4
  W 7 1.60 (.65) 1 – 4
Purpose in Life 5.15 (.81) 1 – 6
Covariates
  Positive Social Support at W 4 2.68 (.37) 1 – 3
  Negative Social Support at W 4 1.33 (.39) 1 – 3
  Religiosity at W 4 .00 (.76)
  Racial discrimination at W4 1.85 (.69) 1 – 5
  Financial Strain at W4 2.22 (.70) 1 – 4
  Income (under $35,000) at W4 346 (55.80)
  Health at W4 3.24 (.97) 1 – 5
  Functional Status at W4 2.63 (.60) 1 – 3
  Depression at W4 1.27 (.37) 1 – 3
Demographic information
  Age at W4 44.92 (8.10) 20 – 89
  No college degree at W4 351 (53.1)
  Not married at W4 441 (66.72)
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Notes. Religiosity variable is a composite variable of three standardized variables related to religiosity.

We conducted bivariate correlation analyses on the relationships among the study variables (see Table 2). Purpose in life was negatively associated with loneliness across all four waves. In addition, positive and negative social support, self-reported heath, physical functioning, depressive symptoms, and financial strain were significantly associated with loneliness across the four waves.

Table 2.

Bivariate Correlations between Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. LONE 4
2. LONE 5 .43**
3. LONE 6 .40** .48**
4. LONE 7 .41** .46** .46**
5. PL −.43** −.29** −.24** −.27**
6. POSP −.34** −.25** −.27** −.21** .26**
7. NESP .29** .20** .21** .18** −.24** −.30**
8. RELI −.09* −.13** −.07 −.15** .14** .16** −.03
9. Racism .16** .07 .12** .03 .03 .05 .08* .06
10. FStrain .25** .17** .23** .15** −.27** −.15** .19** −.14** −.01
11. Income −.11** −.06 0 −.10 .12** .06 −.06 .06 .14** −.22**
12. Health −.18** −.19** −.19** −.18** .25** .09* −.15** .11** −.04 −.24** .17**
13. Function −.13** −.12** −.12** −.18** .16** .08* −.12** .07 −.05 −.16** .12** .48**
14. DEPR .35** .23** .31** .26** −.35** −.12** .21** −.13** .10* .22** −.05 −.27** −.21**
15. Age −.06 −.02 .06 .02 −.02 .10* −.04 .16** .08* .05 −.02 −.09* −.21** −.07
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Note.

**

indicates p<.01.

*

indicates p <.05. LONE – Loneliness, PL = Purpose in Life, POSP = Positive social support, NESP = Negative social support, RELI = Religiosity, Racism = racial discrimination, FStrain = Financial strain, Health = Self-reported health, Function = Physical functioning, DEPR = Depressive symptoms, MARRI = Martial status.

Class Membership Based on the Trajectories of Loneliness

To classify individuals into classes based on loneliness trajectories over time, we estimated models with different number of classes (see Table 3). The first three models included two, three, and four classes, respectively, using the full sample (n = 661). Among the models, the model with three classes was supported by significant LMR-LRT and BLRT values, although BIC became smaller with an increasing number of latent classes. However, the third class did not have enough participants (i.e., n = 5; less than 1% of the sample) to adequately represent the trajectory of the class, and they had extremely high levels of loneliness across the waves (see Table 4). Thus, we identified them as outliers and decided to exclude these participants in the main analysis.

Table 3.

Indicators of Model Fit and Distribution of Respondents among Models

Full samples (n = 661) Samples without 5 subjects (n = 656)
Model fit index 2 Classes 3 Classes 4 Classes 2 Classes 3 Classes 4 Classes
Log Likelihood −1586.14 −1560.41 −1539.66 −1520.85 −1500.06 −1477.17
AIC 3196.27 3150.82 3115.33 3065.70 3030.13 2990.34
BIC 3250.20 3218.23 3196.21 3119.54 3097.42 3071.09
Adjusted BIC 3212.10 3170.60 3139.06 3081.44 3049.80 3013.94
Entropy .78 .87 .81 .80 .76 .77
LMR-LRT p .12 <.01 .51 <.001 .51 .07
BLRT p <.001 <.001 <.001 <.001 <.001 <.001
Distribution of Respondents
 Class 1 (%) 496 (75.04) 191 (28.90) 119 (18.00) 465 (70.88) 88 (13.41) 401 (61.13)
 Class 2 (%) 165 (24.96) 465 (70.35) 449 (67.93) 191 (29.12) 449 (68.45) 112 (17.07)
 Class 3 (%) 5 (.75) 5 (.75) 119 (18.14) 82 (12.50)
 Class 4 (%) 88 (13.31) 61 (9.30)
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Note. AIC = Akaike’s Information Criterion; BIC = Bayesian Information Criterion; LMR-LRT = Lo, Mendell, & Rubin likelihood ratio test, BLRT = Bootstrap likelihood ratio test.

Table 4.

Information of Key Variables for 2 Classes Model (n = 656)

Lower Class n = 465 (70.88 %) Higher Class n = 191 (29.12 %) difference test Outliers n = 5
Parameters β (SE) β (SE) β (SE)
  Intercept 1.29 (.02) *** 2.17 (.04) *** −.91 (.02) ***
  Slope .04 (.02) ** −.13 (.04) *** .18 (.02) ***
Loneliness M (SD) M (SD) T-test (df) M (SD)
  W 4 1.28 (.30) 2.24 (.37) − 34.37 (654) *** 3.43 (.40)
  W 5 1.37 (.40) 2.02 (.47) − 17.45 (595) *** 3.25 (.65)
  W 6 1.32 (.41) 1.89 (.59) −13.57 (579) *** 3.33 (.60)
  W 7 1.45 (.57) 1.89 (.68) −6.41 (363) *** 3.42 (.59)
Purpose in life 5.20 (.70) 4.56 (.89) 10.25 (653) *** 3.58 (.78)
Positive social support 2.75 (.33) 2.53 (.38) 7.38 (652) *** .16 (.42)
Negative social support 1.26 (.35) 1.47 (.42) − 6.40 (652) *** 1.98 (.65)
Religiosity .04 (.76) −.08 (.74) 1.89 (654) −.45 (.58)
Racial discrimination 1.79 (.68) 1.98 (.69) − 3.31 (654) *** 2.83 (.54)
Financial strain 2.12 (.67) 2.45 (.70) − 5.60 (653) *** 2.85 (.45)
Age 45.11 (8.23) 44.45 (7.86) .71 (654) 44.00 (4.20)
Income (< $35,000) 7.74 (10.81) 5.43 (3.70) 2.789 (613) ** 1.00 (.00)
Self-reported health 3.36 (.93) 2.98 (1.00) 4.85 (654) *** 2.00 (.71)
Physical function 2.68 (.58) 2.52 (.63) 3.06 (654) ** 2.00 (.71)
Depressive symptoms 1.18 (.29) 1.45 (.45) − 8.98 (653) *** 2.120 (.39)
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Notes.

***

indicates p<.001.

**

indicates p <.01. Purpose in life variable was computed with the averaged scores of the seven items in this table.

In the class membership analysis without the five outlier subjects (n = 656), the model with two classes had the best model fit considering the significance of LMR-LRT and BLRT, and the total count of subjects in each class. In the two-class model, 70.88 % of the participants were in Class 1, which was characterized by a lower initial level and slightly increasing levels of loneliness over time. The remaining 29.12 % of the participants were placed into Class 2, which represents a higher initial level and slightly decreasing levels of loneliness over time. Table 4 presents the sample means of key predictors and control variables for each class. Class 1 showed significantly lower levels of loneliness at all time points compared to Class 2. Figure 2 shows the levels of loneliness for Class 1 and Class 2 over time. Participants in Class 2 reported lower levels of purpose in life, positive social support, religiosity, income, self-reported health, and physical functioning than those in Class 1. They also reported higher levels of negative support, racial discrimination, financial strain, and depressive symptoms. Regarding marital status, within Class 1 35.05 % of participants were married, and 29.84 % of were married in Class 2.

Figure 2. Trajectories of Loneliness in 2 Classes over Time.

Figure 2.

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Model Results: Predicting Loneliness Trajectories

We conducted LGMM to examine whether level of purpose in life predicted level of loneliness as well as the rate of change over time. Table 6 indicates the odds ratio of class membership compared with those in the lower loneliness class (the reference group). After controlling for the other predictor variables, participants with a higher level of purpose in life were less likely to be in the higher loneliness class (OR = .68, CI = 1.51 − 3.64) relative to the lower loneliness class.

Table 6.

Coefficients and odd ratios from logistic regression predicting class membership in loneliness trajectory (n = 656)

β (SE) p OR [95% CI]
Odds ratios of class membership (vs. low loneliness)
  Purpose in Life −.38 (.15) .01 .68 [1.51, 3.64]
  Positive Social Support −.47 (.13) <.001 .62 [.50, .91]
  Negative Social Support .24 (.12) .04 1.27 [1.01, 1.61]
  Religiosity −.22 (.14) .13 .81 [.61, 1.07]
  Racial discrimination .35 (.11) <.001 1.43 [1.16, 1.76]
  Financial strain .18 (.11) .11 1.20 [.96, 1.50]
  Married status .09 (.06) .15 1.10 [.97, 1.24]
  Income .07 (.11) .68 1.08 [.89, 1.38]
  Self-reported health −.30 (.12) .01 .74 [.58, .94]
  Physical functioning .04 (.12) .71 1.04 [.83, 1.31]
  Depressive symptoms .71 (.16) <.001 2.04 [1.51, 2.83]
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Note. Purpose in life is a latent variable

Additionally, in the full model, participants reporting a higher level of positive social support were less likely to be in the higher loneliness class (OR = .62, CI = .50 − .91) relative to the lower loneliness class. For negative social support, those with a higher level of negative social support were more likely to be in the higher loneliness class (OR = 1.27, CI = 1.01 − 1.61). In addition, participants who experienced a higher level of racial discrimination were more likely to be in the higher loneliness class (OR = 1.43, CI = 1.16 − 1.76). Those with a lower level of self-reported health and higher level of depressive symptoms tended to be in the higher loneliness class (self-reported health: OR = .74, CI = .58 − .94; depressive symptoms: OR = 2.04, CI = 1.51 – 2.83). However, contrary to expectations, financial strain and religiosity were not significant factors predicting the levels and trajectories of loneliness.

Discussion

This study was designed to investigate the association between purpose in life and loneliness among African American women over time. We conducted an LGMM on longitudinal loneliness data for African American women from the FACHS study. Our analyses identified two classes based on the trajectory of loneliness over time and examined whether initial level of purpose in life predicted the probability of belonging to the two different classes. In addition, we included other potential factors (positive and negative social support, religiosity, racial discrimination, and financial strain) in the model as control variables and tested if they were related to the level and trajectory of loneliness, as represented by class membership.

Turning first to loneliness trajectories, although the group of individuals having lower initial loneliness reported a small increase in loneliness over time, their levels of loneliness were consistently lower across waves than the other group, which began with higher levels of loneliness (see Figure 2). Furthermore, the magnitude of slope values for both groups were relatively small (see Table 4).

Consistent with previous studies in majority populations, our findings revealed that African American women who had a higher initial level of purpose in life reported lower levels of loneliness over time. The role of purpose in life as a motivational resource helps explain this relationship. First, purpose in life motivates individuals to invest more effort and time in social activities, thereby reducing feelings of loneliness. A higher level of purpose in life may motivate them to actively pursue their goals and desires with a positive outlook, leading to optimistic attitudes and positive attitudes towards others. These attitudes and behaviors render individuals more socially attractive, which increases the opportunities for receiving social support and developing larger social networks (Folker et al., 2021; Stavrova & Luhmann, 2016; Stillman et al., 2011). This enhanced social well-being may thus contribute to lower levels of loneliness.

Existing research highlights a high level of religiosity among African American women and its significant impact on social support and social connections (Mattis, 2002; Rote et al., 2013; Taylor et al., 2005; Taylor et al., 2007). Based on this, we expected that greater religiosity might be associated with lower levels of loneliness over time. Although the religiosity composite measure and loneliness were weakly correlated, religiosity was not a significant predictor of the initial level and trajectory of loneliness. To evaluate the possibility of the differential impact of the three aspects of religiosity (i.e., organizational, non-organizational, and subjective religiosity) on loneliness, we conducted the LGMM analyses separately for each aspect of religiosity and found that none of the specific aspects of religiosity predicted class membership. Thus, although previous studies have indicated that religiosity is an important protective factor against African Americans’ mental health problems such as depressive symptoms and anxiety disorder (Chatters et al., 2018; Himle et al., 2012), these findings suggest that religiosity’s effect on social well-being, particularly loneliness, may not be as pronounced. In our analysis, we controlled for positive and negative social support. It is possible that religiosity is only indirectly associated with the trajectory of loneliness, through positive and negative social support (Rote et al., 2013). Thus, social resources, such as perceived positive social support from close networks, may diminish the impact of religiosity on loneliness. Future research should explore these nuances to better understand the role of religiosity in loneliness among African American women.

We underline the significance of racial discrimination as a factor influencing loneliness within the context of African American women’s experiences. Those who reported a lower level of racial discrimination were likely to experience lower levels of loneliness over time. This is consistent with previous studies on the association between discrimination and loneliness among marginalized groups (Chang, 2018; Lee & Bierman, 2019; Lim et al., 2018; Nadimpalli et al., 2015). Experiences of racial discrimination undoubtedly lead to feelings of social exclusion from the community (Lee & Bierman, 2019; Seawell et al., 2012), ultimately contributing to higher levels of loneliness. The significant influence of discrimination experiences, when controlling for influential variables such as social support and depressive symptoms, underscores that racial discrimination experiences are an important, independent risk factor for loneliness over time among African American women.

Limitations

This study had several limitations. First, the sample was recruited from only two states, Georgia and Iowa; therefore, the results may not generalize to other parts of the country. Second, the current study could not consider the potential effect of change in purpose in life over time on loneliness. We suggest that future studies replicate these findings with more representative samples including additional variables. Moreover, we did not estimate the non-linear trajectory of loneliness over time although the different levels of purpose in life may predict non-linear changes of loneliness with age. Finally, the current study’s analytic approach did not address the mechanisms by which purpose in life predicts level of loneliness. Future research on these topics using longitudinal methods needs to consider these issues.

Conclusion

Despite these limitations, this study makes a valuable contribution to the current literature and to the design of possible future interventions. First, the findings advance Ryff’s Psychosocial Well-being Model by demonstrating the effects of purpose in life on loneliness. Using a longitudinal design, this study also provides insight into changes in loneliness as predicted by purpose in life. Furthermore, this study expands upon prior findings regarding predictors of psychosocial well-being with an underrepresented populations. The study examined the experiences and characteristics of African American women (i.e., racial discrimination, financial strain, and religiosity) and add to our knowledge of predictors of psychosocial well-being among racial minority groups. The findings encourage further investigation into the potential benefits of purpose in life for the psychosocial well-being of stigmatized groups.

Given the substantial impact of loneliness on both mental and physical health, it is important for intervention programs to consider the importance of purpose in life as a means to reduce loneliness. Intervention programs that underline goal setting, personal growth, and fostering purpose in life could be effective in mitigating feeling of loneliness and enhancing psychosocial well-being. Furthermore, among African American women, addressing racial discrimination is important for tailoring intervention programs when treating loneliness and related mental health issues in African American women.

In conclusion, this study suggests that developing a stronger sense of purpose in life may be beneficial in preventing and/or overcoming feelings of loneliness among marginalized populations who face significant societal challenges. This insight may be useful in the development of interventions to prevent or alleviate loneliness in underrepresented populations.

Supplementary Material

Appendix

Table 5.

Confirmatory Factor Analysis for of 7-item Purpose in Life scale

Loadings (SE) Items Loadings (SE)
Factor 1 .99 (.07) *** I tend to focus on the present, because the future nearly always brings me problems. .51 (.04) ***
I don’t have a good sense of what it is I’m trying to accomplish in life. .60 (.04) ***
Factor 2 .84 (.06) *** My daily activities often seem trivial and unimportant to me. .54 (.04) ***
I used to set goals for myself, but that now seems like a waste of time. .64 (.04) ***
I sometimes feel as if I’ve done all there is to do in life. .48 (.04) ***
Factor 3 .74 (.06) *** I enjoy making plans for the future and working to make them a reality. .55 (.05) ***
I am an active person in carrying out the plans I set for myself. .58 (.05) ***
Fit Statistics
  χ2 (df) 20.48 (11) *
  CFI .98
  TLI .97
  RMSEA .04
  SRMR .02
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Note. Loadings are standardized values.

***

indicates p<.001.

**

indicates p <.01.

*

indicates p <.01. χ2 = Chi-square. CFI = Comparative Fit Index. TLI = Tucker-Lewis Index. RMSEA = Root Mean Square Error of Approximation. SRMR = Standardized Root Mean Square Residual.

Acknowledgement

This research uses data from the FACHS, a project designed by Drs. Ron Simons, Frederick Gibbons, and Carolyn Cutrona, and funded by grants from the National Institute of Mental Health (MH62666, MH62668, MH62669), and the National Institute on Drug Abuse (DA021898).

Footnotes

This work has not been published elsewhere, nor it is currently under consideration for publication elsewhere.

Generative AI

This manuscript was prepared with the assistance of Chat GPT-4o (February 2024 version) for language refinement to improve clarity and fluency in writing.

Declaration of Interest Statement

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

We have no known conflict of interest to disclose.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Eunbea Kim, upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix
NIHMS2085730-supplement-Appendix.docx (15KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Eunbea Kim, upon reasonable request.

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