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. Author manuscript; available in PMC: 2021 Dec 8.
Published in final edited form as: J Posit Psychol. 2020 Jul 7;16(5):651–659. doi: 10.1080/17439760.2020.1791946

Loved and lost or never loved at all? Lifelong marital histories and their links with subjective well-being

Mariah F Purol 1, Victor N Keller 1, Jeewon Oh 1, William J Chopik 1, Richard E Lucas 1
PMCID: PMC8654342  NIHMSID: NIHMS1610844  PMID: 34887936

Abstract

Marriage has been linked to higher well-being. However, previous research has generally examined marital status at one point in time or over a relatively short window of time. In order to determine if different marital histories have unique impacts on well-being in later life, we conducted a marital sequence analysis of 7,532 participants from the Panel Study of Income Dynamics (54.2% women; Mage = 66.68, SD = 8.50; 68.7% White/Caucasian). Three different marital sequence types emerged: a “consistently-married” group (79%), a “consistently-single” group (8%), and a “varied histories” group (13%), in which individuals had moved in and out of various relationships throughout life. The consistently-married group was slightly higher in well-being at the end of life than the consistently-single and varied histories groups; the latter two groups did not differ in their well-being. The results are discussed in the context of why marriage is linked to well-being across the lifespan.

Keywords: sequence analysis, lifespan approach, marital history, subjective well-being, life satisfaction

The preponderance of evidence suggests that married people are happier than single people (e.g., Perelli-Harris, Hoherz, Lappegard, & Evans, 2019). However, much of the existing research on this topic only examines the marital status of an individual at one particular time point and relates this single measurement to their happiness. Yet, people often move in and out of relationships throughout their lives. It is not yet known whether these transitions are associated with differences in well-being. For example, are people who experience many marital transitions (multiple marriages and divorces) as happy as those who have stayed single their whole lives? In the current study, we examined the marital histories of 7,532 individuals, followed from ages 18 to 60, and determine whether these histories were differentially associated with life satisfaction in older adulthood.

The Link Between Marriage and Well-Being

Prior research consistently shows that married people report higher well-being than those who are single, divorced, or widowed (Waite & Gallagher, 2001). Indeed, being married is associated with several emotional, social, and health-related benefits across the lifespan (Haring-Hidore, Stock, Okun, & Witter, 1985). For example, married individuals report less anxiety, depression, and mental distress (Ross, Mirowsky, & Goldstein, 2009), are healthier and live longer (Schone & Weinick, 1998), and adjust well to the onset of chronic illness (Revenson, 1994). Given these consistent associations, researchers have turned their attention to identifying their causes.

There are many reasons why marriage might be associated with greater well-being. Although the most obvious explanations point to the benefits of the companionship that comes with marriage, alternative explanations for this association exist. Johnson and Wu (2002), for instance, suggested that the association between marriage and subjective well-being may actually reflect a different process—namely, that divorced individuals report lower well-being than those who have not divorced, and they may be the ones primarily driving the difference between groups of different marital status. Specifically, Johnson and Wu proposed that the stress of the divorce and its consequences (e.g., social isolation, shared childcare, economic hardship) can cause divorced individuals to experience more distress than those who are married. Divorced individuals may also have poorer coping skills and less social support, which likely amplifies the intensity of these stressors (Avison, 1999). Altogether, the presence of these stressors likely lowers well-being.

Lucas and colleagues (2003, 2005) likewise suggested that these differences in happiness may not reflect a causal relationship between marriage and happiness, but, instead, stable individual differences. They argued that the association could be a result of selection effects: those who end up getting married may have been happier than those who never do—even before they got married. In examining how individuals adapt to changes in marital status, they found evidence of this effect, such that happier people were more likely to enter into and maintain a marriage than their less happy counterparts (who either did not marry or eventually divorced). This suggests that there may be pre-existing differences in happiness between those who remain married, singles, and those who eventually divorce.

It is also possible that the apparent effect of marriage is not due to marriage per se, but to the existence of any close, interpersonal relationship (e.g., high quality friendships; DePaulo & Morris, 2005). Singles and those in relationships can report similar levels of well-being (Greitemeyer, 2009), perhaps because of positive relationships with their family or friends (DePaulo & Morris, 2005). However, regardless of the reasons why, married people generally report higher well-being than single, divorced, and widowed individuals in cross-sectional comparisons.

Marital Transitions Across Life

Although the association between marital status and subjective well-being is robust, there are still questions left unanswered. Importantly, many of the aforementioned studies merely take a snap-shot of married and non-married people at one point in time. There have been comparatively fewer longitudinal studies investigating how marital transitions impact subjective well-being. Further, studies examining the entire marital history of an individual are rare. Those studies that have been conducted suggest that the effects of marital status on well-being can be quite complicated (e.g., Lucas, 2005; Lucas & Clark, 2006; Lucas & Dyrenforth, 2005).

For example, divorce is typically perceived as a negative event, which might lead to the expectation that divorced individuals will report lower subjective well-being than those who are married. However, Lucas (2005) described levels of life satisfaction among participants who were going through a divorce; this research showed that levels of life satisfaction did not track changes in official marital status in a simple way. People who got divorced experienced declines in life satisfaction even before the divorce officially occurred (i.e., when their marital status was still “married”). In addition, the divorce itself was associated with an increase in well-being relative to lowest points at the end of the marriage, though satisfaction levels were still lower than they were at the beginning of or prior to the marriage. Further complicating these patterns, individuals who remarry experience a greater recovery in life satisfaction after their divorce (albeit they do not quite return to their baseline levels of life satisfaction).

Additionally, multiple marriage transitions impact life satisfaction in intricate ways. Luhmann and Eid (2009) found that, for those who had undergone repeated divorces, life satisfaction was higher at their second divorce than their first, and, for those who had undergone more than one marriage, life satisfaction was similar across marriage events. Taken as a whole, these results suggest that having an appreciation for the fact that marital histories can be quite nuanced may provide additional insights into the association between marital status and well-being.

Other observations suggest that the differences in well-being between those who are married versus unmarried may not be as stark as some researchers have suggested (DePaulo, 2007). Specifically, research comparing married individuals and lifelong singles points to a similarly complex pattern of associations between marital status and well-being. For example, the association between marital status and well-being varies considerably across contexts, including differences in the size of the association across countries (Lucas & Dyrenforth, 2005). There is also evidence to suggest that the effects of marital status on well-being might even be decreasing in recent cohorts, as people report being more satisfied with being single (Boger & Huxhold, 2018). In sum, although cross-sectional studies suggest a robust association between marital status and subjective well-being, the nuance of the association requires a more sophisticated design. Examining an individual’s entire marital history gives a more holistic picture of the various transitions they go through over time.

The body of longitudinal research on marital transitions suggests an intricate relationship with subjective well-being that has not yet been effectively captured. In order to precisely determine how relationships impact happiness, longitudinal research needs to account for the many marital transitions across the lifespan with individuals’ entire marital history. Marital histories provide a year-by-year account of an individual’s relationship status, including transitional periods. By getting a more holistic view of how individuals move through relationships throughout life, we are better able to draw conclusions about these relationships’ impact on well-being and happiness.

The Current Study

Although the relationship between marital status and life satisfaction has been widely studied, past research has relied on a limited scope of individuals’ relationship histories, failing to account for all of the relationship transitions a person may have undergone in a lifetime. At the same time, it is possible that groups of individuals can be identified based on whether or not they experience similar transitions across their lifespans. The current study examines differences in life satisfaction among those with different marital histories. In order to do this, we employed sequence analysis to thematically and empirically group individuals who experienced similar marital transitions. Doing so provides valuable descriptive data for how common different relationship transition patterns are. For example, there could be a relatively homogenous group of people who have been married to one person most of their adult life. Others could have experienced varied marital histories consisting of multiple marriages and periods of being single. In a descriptive approach, the sequence analysis allowed us to compare the marital histories of 7,532 participants followed from ages 18 to 60 to determine which patterns were most common. We also examined how people in these sequences differed from each other in well-being in late life in order to provide a more comprehensive understanding of how marital status is associated with life satisfaction.

Method

Participants

Participants were 7,532 respondents (54.2% women; Mage = 66.68, SD = 8.50) from the Panel Study of Income Dynamics (PSID). The PSID is a longitudinal study that has followed a nationally representative sample of U.S. individuals and their families since 1968. Children of original study participants (and their children) are subsequently included in data collection as the survey progresses. Participants were 68.7% White/Caucasian, 28.9% Black/African American, 1.7% Spanish American/Puerto Rican/Mexican/Cuban, and .7% Other. On average, participants had 13.17 years (SD = 2.82) of education.

We examined marital histories for participants from ages 18 to 60, collected both prospectively and retrospectively. Well-being information was available for a subset of the participants (N = 3,530; 54% women; Mage = 66.23, SD = 8.01), as well-being was not assessed until 2009. Participants in this subsample were 75.2% White/Caucasian, 22.9% Black/African American, 1.4% Spanish American/Puerto Rican/Mexican/Cuban, and .6% Other. Average level of education for the subsample was 13.26 years (SD = 2.80).

Measures

Martial histories.

Assessment of marital history data in the PSID began in 1985. Participants in the 1985 wave were asked to give a retrospective account of their entire marital history. In all successive waves, participants provided annual marital history updates, providing information for their current/most recent marriage in regard to the previous calendar year. Data and transitions after age 60 were sparse, so this age was used as a cut-off to maximize our sample size.

Life satisfaction1.

Life satisfaction was measured with a single item, “Please think about your life as a whole. How satisfied are you with it?” Responses ranged from 1 (completely satisfied) to 5 (not at all satisfied). The scale was reverse-coded so that higher levels indicated greater life satisfaction. Single-item measures of life satisfaction have comparable validity to longer form measures (Cheung & Lucas, 2014; Lucas & Donnellan, 2012). Life satisfaction was assessed at five time points (i.e., 2009, 2011, 2013, 2015, 2017). Because we were interested in participants’ life satisfaction at the end of the marital sequence, we used the life satisfaction assessment most recently available after age 60. Thus, participants in all analyses had full marital history from age 18 to age 60.

Results

Sequence Analysis

Individual marriage histories were organized into sequences of years. Each year, a person could be in one of 6 states: never married (single), married, divorced, separated, widowed, or deceased. All sequences started at age 18 and ended at age 60. To determine whether there are common patterns of marital histories, we performed two analyses: one, a sequence analysis, which assessed the similarities between the marriage histories, and, two, a cluster analysis, to determine which clusters of marriage histories best summarize the data.

The sequences were then compared to each other using optimal matching (Abbott & Forrest, 1986). This procedure determines the similarity of two sequences by counting the fewest amount of insertions, deletions, and substitutions that are necessary to transform one sequence into the other.2 For example, a four-year sequence of one individual (i.e., married married married married) might require one substitution to be transformed into the sequence of another individual (i.e., married married married divorced). The more insertions, deletions, or substitutions necessary to transform one sequence into another, the more dissimilar they are. The optimal matching procedure was performed on all possible pairs of sequences resulting in a matrix with a row and column for every sequence and cells with the dissimilarity scores for any two sequences.

The cluster analysis recommended by Studer (2013) was then performed on the dissimilarities to determine how the sequences could be best clustered. This analysis, Partitioning Around Medoids (PAM) initialized with Hierarchical Clustering (HC), is explained in detail by Studer (2013). The hierarchical clustering used the “Ward” algorithm. Essentially, this analysis groups similar sequences into a predetermined number of clusters, then optimizes the clusters by swapping sequences until the overall dissimilarity between the sequences within a cluster is as low as possible. PAM initialized with HC was done for solutions of 2 to 20 clusters.

To determine which number of clusters best represents the data, a set of clustering-quality measures recommended by Studer (2013) were calculated. The measures used were the Point Biserial Correlation (PBC), Hubert’s Gamma (HG), Hubert’s C (HC), and the Average Silhouette Width (ASW). The PBC and HG have a similar interpretation: they are indices of how well the clustering can reproduce the dissimilarity matrix. If a clustering solution reproduces the dissimilarity matrix well, then it is considered to be a good clustering solution. HC is a measure of the difference between the solution obtained and the best solution theoretically possible (for this measure, lower numbers indicate higher clustering quality). Finally, ASW indicates whether the within-cluster similarity is greater than the between-cluster similarity.

The measures of clustering quality were highly correlated, and, as can be seen in Figure 1, they indicated that 3 clusters were the optimal solution for this data set of marriage sequences. As seen in Figure 2, the resulting clusters were (1) people who spent most of their adult lives married (“consistently-married;” 79.30% of the sample), (2) people who spent most of their adult lives single (“consistently-single;” 7.86% of the sample), and (3) people who transitioned throughout various relationship states (“varied-histories;” 12.84% of the sample).

Figure 1.

Figure 1.

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Clustering quality for 2 to 20 clusters

Note. Quality indicators are standardized.

Figure 2.

Figure 2.

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Index plots of the three clusters of marital histories

The individuals in these groups were not homogenous in their relationship histories. It is important to note that cluster membership is assigned by the length of time spent in each state, and not an individual’s exclusive association with this state. For example, while those in the consistently-married cluster are characterized by their long marriages, the group includes those who have been widowed or divorced as well. Similarly, those in consistently-single cluster are characterized by long periods of singlehood, although some eventually married, divorced, or became widowed.

Figure 3 shows the relative distribution of marital states for each cluster from ages 18 to 60 (i.e., chronograms). Consistent with our interpretation of the sequence graphs (Figure 2), over time, a large proportion of people became married (in Cluster 1), a large proportion of people stayed single (in Cluster 2), and the proportion of single and married individuals was larger earlier in life, followed by a larger proportion of divorces (in Cluster 3).

Figure 3.

Figure 3.

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Chronograms (relative frequencies of states) of the three clusters of marital histories

Linear Regression

We then compared the life satisfaction of individuals from the different clusters. To do so in the context of linear regression, one must pick a reference group against which to compare group differences in life satisfaction (i.e., a dummy coded approach). We chose the consistently-married group because they had the largest number of participants available. Thus, the effects of consistently-single and varied-histories groups were estimates of the differences between these groups and the consistently-married group. We controlled for gender and education, as these factors are known to predict life satisfaction in different ways (Chipperfield & Havens, 2001; Fernández-Ballesteros, Zamarrón, & Ruiz, 2001). Specifically, men are more likely to get remarried than women after a divorce (Bradbury & Karney, 2010), and those with lower levels of education often have shorter-lasting marriages (Wang, 2015). Thus, we included gender and education as control variables.

The results of this analysis are presented in Table 1. Compared to the consistently-married group, individuals in the consistently-single group and varied-histories group reported lower life satisfaction. People with higher levels of education also reported higher life satisfaction, which is consistent with prior research (Melin, Fugl-Meyer, & Fugl-Meyer, 2003). In a follow-up analysis, we varied the reference group such that the comparison was against the varied-histories group (see Table 2). Reproducing the effect above, people in the consistently-married group reported higher levels of happiness relative to the varied-history group. Importantly, the consistently-single and varied-history groups did not significantly differ in their levels of life satisfaction.

Table 1.

Regression predicting subjective well-being from cluster membership

b SE β t p LB UB
Intercept 3.807 0.158 24.106 < .001 3.497 4.117
Consistently-Single −0.184 0.058 −0.054 −3.153 .002 −0.299 −0.070
Varied History −0.301 0.043 −0.120 −7.010 < .001 −0.385 −0.217
Age −0.001 0.002 −0.006 −0.368 .713 −0.004 0.003
Gender −0.018 0.028 −0.011 −0.628 .530 −0.074 0.038
Education 0.016 0.005 0.053 3.080 .002 0.006 0.026
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Note. Consistently-married cluster served as the reference group. Gender: 1=men, 2=women. LB: Lower bound of 95% confidence interval; UB: Upper bound of 95% confidence interval.

Table 2.

Regression predicting subjective well-being from cluster membership

b SE β t p LB UB
Intercept 3.506 0.160 21.960 < .001 3.193 3.819
Consistently-single 0.117 0.069 0.034 1.704 .088 −0.018 0.251
Consistently-married 0.301 0.043 0.141 7.010 < .001 0.217 0.385
Age −0.001 0.002 −0.006 −0.368 .713 −0.004 0.003
Gender −0.018 0.028 −0.011 −0.628 .530 −0.074 0.038
Education 0.016 0.005 0.053 3.080 .002 0.006 0.026
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Note. Varied History cluster served as the reference group. Gender: 1=men, 2=women. LB: Lower bound of 95% confidence interval; UB: Upper bound of 95% confidence interval.

Individuals in the consistently-married group reported higher level of life satisfaction than individuals either of the other groups. To contextualize these effects, the differences were relatively small in magnitude (βs > .05). This suggests that there are not dramatic differences between people with dramatically different marital histories.

Discussion

The current study used sequence analysis to examine differences in life satisfaction of individuals with different marital histories. This method allowed us to determine common marital transition patterns and get a more holistic picture of the association between relationship history and life satisfaction. We found that individuals generally fall into one of three marital history groups: consistently-married (married for most of their adult life), consistently-single (single for most of their adult life), or varied-histories (demonstrated varied patterns of relationships throughout their adult life). The large majority of participants fell into the consistently-married group. When examining life satisfaction, we found that the consistently-married individuals had higher life satisfaction compared to consistently-single and varied-history individuals, but the consistently-single individuals and varied-histories groups did not differ in their life satisfaction.

The Link between Marital Histories and Well-being

Our analyses showed that, when using a life history approach, greater time spent married was positively associated with well-being towards the end of life. Those who had spent much of their adult lives single and those who experienced many transitions were similar in their levels of well-being. These findings are consistent with previous research in two ways. First, they are consistent in direction: marriage was associated with higher life satisfaction compared to divorce/singlehood. This is consistent with a large body of literature showing higher levels of life satisfaction among married individuals. Second, these associations are consistent in magnitude: the differences in life satisfaction between these groups, although significant, were relatively small. This, too, is consistent with previous research (Boger & Huxhold, 2019; Lucas & Dyrenforth, 2005).

While the reports of greater life satisfaction of married individuals are relatively unsurprising, the lack of difference between the consistently-single and varied-histories groups was unanticipated. As previously discussed, those who remain married and those who eventually get divorced have been shown to differ in life satisfaction before the marriage event (Lucas, 2005). The similarity in life satisfaction in older adulthood between the consistently-single and varied-history groups could likewise be attributable to similarity in life satisfaction earlier in life of singles and individuals who eventually divorce.

Other possibilities exist as well, although, at the moment, they are a bit speculative. For example, individuals who eventually get divorced may indeed be happier than single people earlier in life (e.g., before or when they are married). However, given the persistent declines in well-being that accompany divorce, consistently-single and varied-history individuals may end up looking relatively similar with respect to well-being. The negative feelings that can accompany divorce, such as feelings of failure, emotional instability, depression, or guilt (Thomas & Ryan, 2008) may prompt an enduring decrease in well-being. In other words, decreased well-being may be a lasting response to divorce, which causes divorced individuals’ well-being to match that of singles.

Yet another explanation for this similarity is that consistently–single and varied-history individuals may both struggle with social isolation, albeit from different sources. Spielmann and colleagues (2013) posited that some single people may experience a “fear or being single”, fueled, in part, by a sense of loneliness or apprehension about being “alone forever.” These authors also suggested that those who are very fearful of being single may “settle” in a relationship—sometimes choosing partners that are less responsive or attractive, or pine for previously unsuccessful or problematic relationships from their past (Spielmann, MacDonald, Joel, & Impett, 2016; Spielmann et al., 2013). Social isolation might also be salient for those who experience divorce (Johnson & Wu, 2002). In addition to losing a spouse, those who are divorced may also lose ties with family or adult children (de Jong Gierveld, van Tilburg, & Dykstra, 2006). Loneliness caused by this loss of connections may worsen with age, as well (de Jong Gierveld, van Tilburg, & Dykstra, 2006). For both of these groups (singles and those divorced), social isolation or loneliness maybe a common factor in decreasing well-being. However, future research is needed in order to definitively determine the sources of life satisfaction within these separate groups.

Limitations and Future Directions

Our study examined complete marital histories from a large sample of individuals, it used an innovative data reduction technique (e.g., sequence analysis) to make sense of marital transitions, and it linked variation in these transitions to well-being in older adulthood. Yet, despite these strengths, some limitations do exist. Unlike previous studies (Lucas, 2005; Luhmann & Eid, 2009), we did not have measures of life satisfaction before and after each marital transition. Rather, our approach allowed us to examine the culmination of all marriage events across the entirety of a person’s life. We viewed these approaches as related but not entirely overlapping. One approach allows researchers to examine short-term pre- and post-marital changes in life satisfaction in a relatively isolated time window. Our approach allowed us to characterize the marital transitions of a lifespan and examined whether or not people were happy at the end of their histories. Our approach leaves open the possibility that the differences observed later in life might at least partially reflect differences that may be observed earlier in life. Nevertheless, including more dense measures of life satisfaction would have allowed us to parse apart the impact of each event and re-occurrences of events (e.g., Luhmann & Eid, 2009).

Another limitation is that we examined an isolated life event (e.g., marital status) at each age from 18 to 60. However, these changes in marital status occur in the context of other life events that may have an impact of life satisfaction (e.g., parenthood, unemployment, disability). Indeed, many of these life events have implications for marriage as well. Unemployment causes a great deal of stress which may have negative effects on an individual’s marriage (Kraft, 2001; Lester, 1996). Married, divorced, and single life all differ in important ways depending on whether these people are also caring for children (Gringlas & Weinraub, 1995; Rankin & Maneker,1985). Thus, the patterns of changes in marital status that we observed may result from other important events—events that are causally linked with the different levels of well-being that we identified across groups. Fortunately, there are a number of ways in which sequence analysis can be adapted to account for the life transitions of different events that occur simultaneously (e.g., multichannel sequence analysis; Gauthier, Widmer, Bucher, & Notredame, 2010; Roux, Grimaud, & Leray, 2018). Future research should examine whether there are additional descriptive patterns of life transitions when other, related lifespan transitions are considered.

Conclusion

The current research found that, in general, people fall into one of three types of marital histories: consistently-married, consistently-single, or varied-histories. Life satisfaction at end of life differed depending on membership in these groups, with consistently-married individuals being slightly happier than consistently-single or varied-histories individuals, who did not differ from each other in life satisfaction. There are a variety of explanations for these results: marriage may indeed lead to higher life satisfaction, those who are consistently-single or have varied histories might experience similar social isolation or additional stress, or these groups may differ from each other before these transitions even begin. Additional research is needed in order to determine precisely why these groups are similar or different, the implications of these differences, and will help further elucidate the relationship between marital history and life satisfaction.

Acknowledgments

This study was not preregistered.

Research reported in this publication was supported by the National Institute of Aging of the National Institute of Health under Award Numbers P01AG029409, R01-AG051142, and 2R03AG054705-01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Portions of this research were presented at the 2020 SPSP (New Orleans, LA) Conference.

Footnotes

1

Worth noting, it wasn’t until 2009 that PSID started assessing life satisfaction, so plenty of participants were much older than age 60 (i.e., those born prior to 1949). As a result, the age at which this first assessment took place varies considerably. To account for this variability, we controlled for age at assessment in all analyses.

2

The costs for insertions, deletions, and substitutions were all set to 1.

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