Dyadic profiles of personality among older couples: Associations with marital quality

Abstract

Understanding dyadic personality configurations and their associations with marital quality helps identify couples who are at high risk of marital strain. However, current research on personality similarity among spouses usually confounds couples with similarly positive and similarly negative personalities. This study aimed to (1) provide a clearer classification of dyadic personality profiles among older couples, (2) examine the associations between these profiles and both partners’ marital quality, and (3) explore gender differences in these associations. Data came from 3,178 older couples drawn from the 2010/2012 waves of the Health and Retirement Study. Latent profile analysis was used to identify dyadic personality profiles based on spouses’ standardized Big Five personality scores. Multilevel models examined associations between dyadic personality profiles and each partner’s marital quality, testing for gender differences as well. Six dyadic personality profiles were identified, including two opposite profiles (52%; positive wife–negative husband and positive husband–negative wife), two similar profiles (40%; similarly positive and similarly negative), and two extreme profiles (8%; extremely negative husband and extremely negative wife). Couples in the similarly positive profile reported the best marital quality, whereas couples in the similarly negative profile and the two extreme profiles reported the worst marital quality. The associations between profiles characterized by negative traits and marital quality were more pronounced among wives than husbands. This study advances the understanding of personality similarity and its consequences, suggesting heterogeneous subgroups of dyadic personalities among older couples and providing evidence of gender differences in the implications of personality similarity for relationship quality.

Keywords

Dyadic data analysis gender differences marriage personality

High marital quality is widely documented to benefit adults’ well-being and physical health (Margelisch et al., 2017; Proulx et al., 2007; Robles et al., 2014). In later life, when social networks shrink (Lang & Carstensen, 2002), people may rely more on spouses for interactions and mutual support than when they were younger. Thus, high marital quality may play an even more important role in older couples’ health and well-being. Although unhappy couples are more likely to be dissolved at early stages of marriages, couples in long-term marriages do not necessarily enjoy good marital quality (Kamp Dush & Taylor, 2012). A great number of older couples expressed substantive marital conflicts, indifference, aversion, and, consequently, high levels of loneliness and dissatisfaction toward their marriages (Duba et al., 2012; Hsieh & Hawkley, 2018). These older couples may hold stricter ideologies about gender roles and life-long marriages than their younger counterparts, and their marital outcomes may be affected by different factors than younger couples. Evidence based on older couples has indicated that individuals’ personality traits as well as couples’ personality compatibility continue to shape marital appraisals in later life (van Scheppingen et al., 2019; Wang et al., 2018). However, it is not clear yet how different personality configurations among older couples are related to their marital quality. To help identify older couples who may be at higher risk of experiencing poor marital quality and its concomitant harms to health and well-being, this study aims to depict a clear picture of personality configurations among older couples by identifying distinct profiles of dyadic personality and examine their implications for marital quality in later life. Furthermore, the existing literature suggests potential gender differences in the associations of personality with marital outcomes among older couples (Iveniuk et al., 2014; Wang et al., 2018). Therefore, we plan to examine whether profile membership has different implications for husbands’ and wives’ perceptions of marital quality.

Couples’ personality and marital quality

Marital quality is best understood in a dyadic setting; as suggested by interdependence theory, individuals’ perceptions of their shared relationships are largely affected by whether their needs (e.g., belongingness, care, agreement) are gratified by the interaction with other persons (Rusbult & Van Lange, 2008). Marital quality may thus be shaped by both one’s own and one’s partner’s characteristics and behaviors. Similarly, the vulnerability–stress–adaptation (VSA) model (Karney & Bradbury, 1995) suggests that personalities are relatively stable traits that couples bring into their marriages. Partners’ personalities are linked to ways of interactions, affect the couples’ ability to deal with stressful circumstances, and ultimately exert influence on marital outcomes. We connect and extend these theoretical frameworks by exploring how spouses’ different personality traits map onto couples and examining the extent to which dyadic personality configurations are linked with marital outcomes.

Researchers have explored the implications of couples’ personality traits for marital outcomes using a variety of approaches. One commonly employed approach focuses on individual and/or partners’ trait levels, which measures trait positivity, mostly using the “Big Five” personality traits (i.e., openness to experience, conscientiousness, extroversion, agreeableness, and neuroticism). Studies based on samples across different life stages generally suggest that trait positivity—defined as being low on neuroticism and high on the other four personality traits—is associated with both one’s own and one’s spouse’s better marital quality (e.g., Bouchard et al., 1999; Donnellan et al., 2004; Iveniuk et al., 2014). It appears in the literature, however, that the associations were more consistent for neuroticism and agreeableness, while the associations were less replicable for openness, conscientiousness, and extroversion across samples.

As research on couples’ personality traits has developed, studies have increasingly examined personality distance/similarity between spouses. Previous research has typically approached spouses’ personality similarity using the following measures: (1) distance between spouses, commonly measured by the absolute difference between spouses on a particular trait (e.g., Gattis et al., 2004; Kilmann & Vendemia, 2013); (2) pattern similarity, obtained by taking the correlations between husbands’ and wives’ array of personality traits (e.g., Dyrenforth et al., 2010; Luo et al., 2008; Wang et al., 2018); and (3) response surface analysis (RSA). Instead of mathematically operating personality scores, the RSA plots all possible combinations of couples’ personality traits and the outcome variable in a three-dimensional space and allows examination of quadratic effects (Barranti et al., 2017; van Scheppingen et al., 2019; Weidmann et al., 2017). Findings across these studies have been mixed. Some evidence suggests that similarity on certain personality traits or personality patterns was associated with better marital quality (Caspi & Herbener, 1990; Kilmann & Vendemia, 2013; Luo & Klohnen, 2005; Nemechek & Olson, 1999). However, other studies did not find significant associations between personality similarity and marital quality, after adjusting for individuals’ absolute personality levels (Barelds, 2005; Dyrenforth et al., 2010; Gattis et al., 2004; Weidmann et al., 2017). Studies comparing different measures of personality similarity among older couples suggested that the distance between two spouses’ personalities played a more important role than pattern similarity or RSA in explaining marital quality (van Scheppingen et al., 2019; Wang et al., 2018). The inconsistency in findings from these studies may be in part due to the weaknesses of each approach in measuring personality similarities. Distance between spouses and profile similarity fail to address heterogeneity within study samples fully, which may lead to inaccurate conclusions. For example, a focus on dis/similarity cannot distinguish between couples who are similarly high or similarly low on certain traits, nor can it determine which spouse is higher/lower on particular personality traits. Although RSA is a more advanced method to distinguish these scenarios, it can be applied only to isolated personality traits.

Personality profiles

To understand the importance of personality compatibility for older adults’ marriages, it is important to employ a typology approach, which identifies subgroups of couples with distinct dyadic personality profiles within a study sample. This approach has previously been used to identify individual personality profiles, with the most-replicated personality types including resilient, undercontroller, and overcontroller (i.e., RUO types; Donnellan & Robins, 2010; Specht et al., 2014). Resilients were characterized as low on neuroticism and relatively high on the rest traits. Undercontrollers were marked as low on agreeableness and consciousness, whereas overcontrollers were typically high on neuroticism and low on extroversion or openness. The RUO types set a ground for further personality type research but were not always replicated, possibly due to various personality measures or sample differences. For instance, Isler et al. (2017) identified one additional personality type among New Zealand middle-aged adults, brittle, indicating persons with undesirable scores on all personality traits. Other researchers identified five personality types among individuals from Finland and North America (Daljeet et al., 2017; Kinnunen et al., 2012; Zhang et al., 2015).

Researchers have applied the typology approach in a dyadic setting to capture couples’ configurations of characteristics such as couples’ desires for closeness with a partner (Czikmantori et al., 2018), successful aging characteristics (Ko et al., 2007), and self-reported masculine and feminine attributes (Wood et al., 2015). We also found one study that specifically focused on older couples’ personality profiles; Kulik (2006) identified three dyadic personality profiles among older Israeli couples—homogeneous-adaptive, homogeneous-maladaptive, and heterogeneous-complementary—based on three traits (i.e., self-esteem, anxiety, and tolerance for ambiguity). Higher life satisfaction was found among the homogeneous-adaptive group, represented by couples who both had high self-esteem, high tolerance for ambiguity, and low anxiety levels. Identifying distinct combinations of spouses’ personality traits can provide useful information about the pattern similarity, trait positivity, and distance between spouses more efficiently. Yet to date, no research has explored the typologies of couples’ personality configurations among U.S. older couples nor using the “Big Five” personality traits.

Gender differences in the associations between personality and marital quality

A meta-analysis concluded that there was no significant gender difference when clinical samples were removed (Jackson et al., 2014). However, recent studies using dyadic data from older husbands and their wives found that—at least among current cohorts of older U.S. couples—husbands consistently report superior marital quality compared to their wives (Carr et al., 2014; Stokes, 2017). Despite the inconsistency in such gender differences, men and women may also derive different marital outcomes from their own and their partner’s personalities. According to social role theory (Eagly & Wood, 2012), individuals are socialized to hold different beliefs toward men and women. Men are often encouraged to be more agentic (e.g., assertive, masterful, and dominant), whereas women are socialized to be more communal (e.g., friendly, concerned with others, and emotionally expressive). As a result of these differences, men and women may value different personality traits of their partners. For example, empirical research among older couples found that spouses’ level of agreeableness was more important to husbands’ perception of spousal strain (Wang et al., 2018), whereas husbands’ level of extroversion was more predictive of wives’ reported marital conflicts (Iveniuk et al., 2014).

In addition, gendered socialization patterns lead to a greater emphasis on close relationships among women than among men (Eagly & Wood, 2012). Studies have also suggested that women have greater awareness of emotions and show stronger emotional reactions than men (e.g., dissatisfaction or happiness), in response to a partner’s personality traits (DiStefano & Motl, 2009; Nemechek & Olson, 1999). With regard to the effects of personality dis/similarity, the limited empirical findings have been mixed. Luo and Klohnen (2005) indicated that greater dissimilarity undermined newlywed wives’ marital happiness but not their husbands’ happiness. However, other studies did not find gender differences in the effect of overall personality similarity on marital outcomes among middle-aged and older couples (Dyrenforth et al., 2010; Wang et al., 2018). Given limited understanding of gender differences in personality effects, this study aims to further explore gender differences in the implications of couples’ dyadic personality configurations for marital quality among older couples.

The current study

This study extends prior research by classifying dyadic personality profiles based on the “Big Five” personality traits of U.S. older couples and further explores the implications of these personality profiles for individuals’ own and their partner’s perceived marital quality. We anticipate distinct dyadic profiles that manifest different combinations of three major dimensions: (1) trait positivity, (2) distance between spouses, and (3) pattern similarity. Further, drawing from the VSA model and the empirical literature, we expect to find the highest levels of marital quality (i.e., highest spousal support and lowest spousal strain) among profiles that present high trait positivity, little distance, and high similarity. Lastly, we anticipate gender differences in the association between profile membership and marital quality, such that effects will be stronger for wives than for husbands.

Method

Data and sample

Dyadic data were drawn from couples who participated in the 2010/2012 waves of the Health and Retirement Study (HRS), a longitudinal study of aging in the United States. HRS began in 1992 and surveys Americans over the age 50 and their spouses (regardless of age) every 2 years (Servais, 2010). In 2010, a new cohort, the mid-baby boomers (born 1954–1959), was added into the HRS, offering a representative sample of adult cohorts aged 50 or older. Variables of interest for this study (e.g., marital quality, personality traits) were part of the Leave-Behind Questionnaire (LBQ), which is administered to a random 50% subsample of living HRS participants at alternating waves, beginning in 2006 (Smith et al., 2017). Therefore, we pooled data from the 2010 and 2012 waves to include the mid-baby boomers’ cohort, and we merged data from these two waves to analyze both 50% subsamples simultaneously. We restricted the sample to couples where both spouses were 50 or older. The final analytic sample included 6,356 respondents from 3,178 heterosexual couples who responded to the LBQ in either 2010 or 2012.

Measures

Personality traits

The HRS employed 26 items from the Midlife Development Inventory personality scales (Lachman & Weaver, 1997) and 5 items from the International Personality Item Pool (http://ipip.ori.org/) to assess the “Big Five” personality traits: (a) neuroticism (moody, worrying, nervous, and calm; α = .71), (b) extroversion (outgoing, friendly, lively, active, and talkative; α = .75), (c) openness to experience (creative, imaginative, intelligent, curious, broad-minded, sophisticated, and adventurous; α = .80), (d) agreeableness (helpful, warm, caring, softhearted, and sympathetic; α = .79), and (e) conscientiousness (reckless, organized, responsible, hardworking, self-disciplined, careless, impulsive, cautious, thorough, and thrifty; α = .73). Respondents were asked to indicate how well each item described them on a 4-point scale (1 = a lot, 2 = some, 3 = a little, and 4 = not at all), and all items (except reckless, calm, careless, impulsive) were reverse-coded. Scores were calculated by taking the average of the items assessing each personality trait and were set to missing if more than half of the items were missing within each trait scale. Less than 1.5% of cases had missing values on any personality traits.

Marital quality

Marital quality was measured using a two-dimensional scale concerning each spouse’s perceptions of (a) spousal support and (b) spousal strain (Smith et al., 2017). Spousal support was measured using 3 items concerning the extent to which a spouse “understands you,” “is reliable,” and “opens up to you.” Spousal strain was measured using 4 items concerning how often a spouse “make[s] too many demands on you,” “criticize[s] you,” “let[s] you down when you are counting on him/her,” and “get[s] on your nerves.” Responses ranged from 1 (not at all) to 4 (a lot). Average scores were obtained for spousal support (α = .82) and spousal strain (α = .78). Scores were set to missing if fewer than half of the scale items had valid responses. Only 21 and 25 cases had missing values for spousal support and spousal strain, respectively. Both measures of marital quality showed substantial within-couple correlations (intraclass correlation = .33 for spousal support and .35 for spousal strain).

Covariates

The literature highlighted several factors that were associated with marital quality in later life, including individual demographics (Anderson et al., 2010; Bulanda, 2011; Corra et al., 2009), health conditions (Yorgason et al., 2008), and couple characteristics (Bulanda, 2011; VanLaningham et al., 2001). Therefore, we controlled for these variables in our analyses. Demographics included age (in years), education (in years), employment status (1 = working for pay, 0 = no), race (Hispanic, non-Hispanic White, non-Hispanic African American, and non-Hispanic Other race), and first marriage (1 = yes, 0 = no). For health characteristics, we included self-rated health and number of chronic conditions. Self-rated health was coded on a 5-point scale (1 = poor to 5 = excellent). The number of chronic conditions was the count of total chronic conditions respondents ever had; the conditions included high blood pressure, diabetes, cancer (excluding skin cancer), lung disease, heart condition, stroke, emotional/psychiatric problems, and arthritis. Regarding couple characteristics, we included household income (in U.S. dollars, transformed by natural log), length of current marriage (in years), total number of children or stepchildren, and whether couples were coresiding with minor child(ren) under age 18 (1 = yes, 0 = no). Finally, we included a dichotomous variable to indicate which 50% random subsample respondents were taken from (1 = 2010, 0 = 2012); no significant differences in personality traits or marital quality were found between respondents from the two waves.

Analytic strategy

We examined descriptive characteristics for husbands and wives in the study sample, including individual and couple characteristics. Paired t and McNemar’s tests were performed to examine differences between husbands and wives. We then used latent profile analysis (LPA) to identify dyadic profiles of personality traits. LPA classifies subjects into meaningful profiles based on given indicators using maximum likelihood techniques. We applied LPA to the standardized personality trait scores of both spouses (a total of 10 indicators), as standardized scores reflect how distinctive the persons are from the average males and females in the sample (Wood & Furr, 2016). Separate means and standard deviations for husbands and wives were used to calculate standardized personality scores. We selected the optimal model for latent profiles of personality based on several criteria, including Akaike’s information criterion (AIC), adjusted Bayesian information criterion (BIC), Lo–Mendell–Rubin adjusted likelihood ratio test (LMR LRT), and entropy, which measure the separateness among different profiles.

After deciding the optimal number of profiles, we put relevant labels to the derived profiles based on the conditional levels of spouses’ personality traits. To better describe the dyadic constellations of personality traits, we calculated the scores on distance between spouses and pattern similarity for each profile. Distance between spouses was measured by taking the average score of absolute difference (AAD) between spouses in five personality traits. Pattern similarity was measured by taking the Pearson correlation score (PCS) between husband and wife’s array of trait scores (McCrae, 2008).

Next, we compared individual and couple characteristics by the derived dyadic profiles of personality by performing one-way analysis of variance (ANOVA) with Tukey’s honestly significant difference (HSD) post-hoc tests and χ² tests.

Finally, we estimated multilevel models (i.e., two-level models; spouses nested within couples) to predict each spouse’s perceived marital quality (i.e., spousal support and strain) using dyadic personality profile membership and covariates. We performed tests of variance inflation factors to identify multicollinearity bias among covariates and excluded individuals’ age from our analyses because there was a multicollinearity issue between age and length of marriage. We also included interactions between gender and dyadic personality profile membership to examine gender differences in the associations between dyadic personality profile membership and marital quality. Data preparation and descriptive statistics were performed in SPSS 25.0. LPA and multilevel models were performed using Mplus 7.4 (Asparouhov & Muthén, 2008); 726 of 6,356 participants (11%) had missing values on selected variables, which were addressed using full information maximum likelihood.

Results

A descriptive summary of the study sample is presented in Table 1. The mean ages were 69 years for husbands (SD = 9.71, range = 50–96) and 66 years for wives (SD = 9.36, range = 50–93). Couples were married for an average of 37 years (SD = 16.10, range = 0.1–70). Wives were younger, healthier, more likely to be in their first marriages, and less likely to be working than husbands. We also found substantial differences in ratings of personality and marital quality between older husbands and wives. Wives were higher on neuroticism, extroversion, agreeableness, and conscientiousness, but had no difference on the level of openness than husbands. Wives reported worse marital quality (i.e., lower spousal support and higher spousal strain) than husbands.

Table 1.

Descriptive characteristics of the study sample.

	Husband (N = 3,178)		Wife (N = 3,178)		Paired t or McNemar’s test
Variables	M	SD	M	SD	Paired t or McNemar’s test
Individual characteristics
Age	68.66	9.71	65.84	9.36	32.74***
Years of education	13.32	2.89	13.25	2.48	1.51
Working for pay, %	40.23		36.98		11.20***
Race, %
Hispanic	6.11		6.64		2.29
White	82.14		81.93		0.15
African American	9.29		9.13		0.64
Other races	2.46		2.30		0.67
First marriage, %	65.93		67.43		4.66*
Self-rated health^a	3.27	1.02	3.39	1.01	−5.02***
Number of chronic conditions	2.22	1.49	2.03	1.40	6.07***
Couple characteristics^b
Household income^c	79.59	89.00	—	—	—
Years of marriage	37.24	16.10	—	—	—
Number of children	3.23	1.95	—	—	—
Coresiding with minor children, %	4.46		—		—
Personality traits^d
Neuroticism	1.93	0.59	2.07	0.60	−9.65***
Extroversion	3.13	0.56	3.25	0.55	−9.03***
Agreeableness	3.37	0.51	3.65	0.42	−25.09***
Conscientiousness	3.21	0.40	3.34	0.38	−13.00***
Openness	2.93	0.54	2.95	0.54	−1.38
Marital quality^d
Spousal support	3.64	0.50	3.44	0.64	16.68***
Spousal strain	1.87	0.63	1.96	0.68	−7.05***

Note. Number of cases may vary across variables due to missing values.

^a1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent.

^b Same for wives.

^c In US$1,000.

^d Items were rated 1 = not at all, 2 = a little, 3 = some, 4 = a lot.

*p < .05; **p < .01; ***p < .001.

Dyadic personality profiles

We compared the model fit indices (i.e., AIC, adjusted BIC, LMR LRT) and entropy across LPA models with different numbers of profiles (see Supplementary Table 1), which suggested that the 6-profile model was the best possible solution. Figure 1 illustrates the average standardized personality traits (z-scores) for couples in each profile and the estimated proportions of each profile in the sample (raw scores of personality traits for couples in different profiles are provided in Supplementary Table 2). Figure 1 also shows AAD and PCS for each profile. As expected, the profiles show different combinations of trait positivity, distance between spouses, and pattern similarity. It should be noted that we named the derived profiles as positive/negative only to show a socially desirable trend in trait positivity, not to judge personal values. Further, our labels for profiles were based on sample mean levels, not clinical cutoff scores.

Two profiles, positive wife–negative husband (33%) and negative wife–positive husband (19%), included couples with “opposite” patterns (PCS = −.01 and −.14, respectively) but average distance between spouses (with AAD = 0.55 and 0.83, respectively). The next two profiles, similarly positive (25%) and similarly negative (15%), fit the pattern of “similar” personalities (PCS = .34 and .31, respectively). Although wives in both “similar” profiles showed slightly higher levels of neuroticism and lower levels on the rest traits than husbands, the distances between spouses were minimal (AAD = 0.12 and 0.14, respectively). These two profiles manifested distinct directions of personality positivity. The last two profiles were described as “extreme spouse” profiles, extremely negative husband (5%) and extremely negative wife (3%), where one spouse scored extremely low on extroversion, agreeableness, conscientiousness, and openness. These two extreme profiles were less typical (together constituting 8% of the total sample), which showed “opposite” patterns (PCS = −.15 and −.04, respectively) and the largest distance between spouse among all profiles (AAD = 1.47 and 1.69, respectively).

Individual and couple characteristics for each of the profiles are depicted in Table 2. Results from one-way ANOVA and χ² tests suggested substantial differences regarding socioeconomic status (SES; i.e., education level and household income), working status, and health among profiles. Similarly positive couples fare the best in terms of education, income, health, and marital quality, and similarly negative and “extreme spouse” couples fare the worst, whereas couples in “opposite” profiles fall in the middle.

Table 2.

Sample characteristics by dyadic personality profiles.

Variables	“Opposite” profiles				“Similar” profiles				“Extreme spouse” profiles				Post-hoc group difference^a
	Profile 1 (33%)		Profile 2 (19%)		Profile 3 (25%)		Profile 4 (15%)		Profile 5 (5%)		Profile 6 (3%)
	Positive wife and negative husband		Negative wife and positive husband		Similarly positive		Similarly negative		Extremely negative husband		Extremely negative wife
	M	SD	M	SD	M	SD	M	SD	M	SD	M	SD
Individual characteristics
Age (H)	68.86	9.90	68.27	9.37	68.19	9.36	68.96	9.77	69.02	10.71	70.69	10.04
Age (W)	66.08	9.43	65.86	9.11	65.23	9.01	66.13	9.67	65.65	9.85	66.99	10.42
Years of education (H)	13.47	2.86	13.32	2.87	13.80	2.70	12.50	3.05	12.66	2.89	12.86	3.13	1, 2, 3>4, 5, 6
Years of education (W)	13.47	2.41	13.19	2.34	13.60	2.40	12.50	2.74	13.16	2.31	12.15	2.78	1, 2, 3, 5>4, 6
Working for pay (H), %	38.72		43.07		46.93		33.62		32.94		28.74		3>1, 2, 5, 6>4
Working for pay (W), %	36.32		33.50		42.27		34.61		38.82		31.03		3>1, 2, 4, 5, 6
Race (H), %
Hispanic	6.13		6.12		4.48		8.49		6.47		6.90
White	84.06		82.98		82.48		78.56		79.41		74.71
African American	7.55		9.09		10.10		10.19		10.59		17.24
Other races	2.26		1.82		2.94		2.76		3.53		1.15
Race (W), %
Hispanic	6.70		5.61		5.49		9.13		6.47		10.34
White	83.49		83.83		81.48		78.77		80.59		73.56
African American	7.74		8.58		10.22		9.77		10.59		13.79
Other races	2.08		1.98		2.81		2.34		2.35		2.30
First marriage (H), %	64.91		68.71		62.53		69.53		65.48		71.26
First marriage (W), %	65.13		73.72		64.19		69.57		65.88		72.41		2>1, 3, 4, 5, 6
Self-rated health^b (H)	3.22	0.99	3.43	0.99	3.62	0.94	2.85	1.02	2.79	1.03	3.05	1.06	3>2>1, 6>4, 5
Self-rated health^b (W)	3.53	0.93	3.26	0.96	3.63	0.98	3.00	1.09	3.21	1.01	2.84	1.12	1, 3>2, 5>4, 6
# of chronic conditions (H)	2.24	1.53	2.17	1.47	2.05	1.42	2.33	1.47	2.58	1.54	2.55	1.65	6>3
# of chronic conditions (W)	1.94	1.39	2.22	1.40	1.81	1.30	2.33	1.49	1.95	1.26	2.36	1.58	2, 4, 6>1, 3
Couple characteristics
Household income^c	79.28	79.43	77.43	84.68	93.57	104.16	65.23	73.43	68.62	82.69	71.86	140.09	1, 2, 3>4, 5, 6
Years of marriage	36.81	16.47	38.23	15.23	36.16	16.00	38.98	15.76	36.03	16.68	38.36	18.11
Number of children	3.27	1.98	3.10	1.86	3.25	1.88	3.20	2.01	3.42	2.22	3.40	1.89
Living with minor children, %	3.81		5.26		5.26		3.55		6.17		1.19
Marital quality^d
Spousal support (H)	3.64	0.47	3.68	0.45	3.78	0.42	3.49	0.56	3.34	0.64	3.48	0.63	3>1, 2>4, 6>5
Spousal strain (H)	1.87	0.60	1.86	0.61	1.73	0.62	2.01	0.65	2.07	0.69	2.04	0.71	4, 5, 6>1, 2>3
Spousal support (W)	3.52	0.59	3.41	0.60	3.62	0.56	3.22	0.69	3.14	0.78	3.07	0.72	3>1, 2>4, 5, 6
Spousal strain (W)	1.93	0.66	1.99	0.63	1.81	0.66	2.14	0.73	2.20	0.73	2.17	0.72	4, 5, 6>1, 2>3

Note. Couple N = 3,178. Number of cases may vary across variables due to missing values. H = husband; W = wife.

^a Based on one-way ANOVA with Tukey’s HSD post-hoc tests or χ² tests (p < .008); numbers indicate profiles.

^b1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent.

^c In US$1,000.

^d Items were rated: 1 = not at all, 2 = a little, 3 = some, 4 = a lot.

Dyadic personality profiles and marital quality

Results from multilevel models predicting marital quality (i.e., spousal support and spousal strain) are provided in Table 3. We first estimated the association between dyadic personality profile membership and marital quality (Models 1, 3, 5, 7, 9, and 11; alternating which profile served as the reference category), controlling for covariates (results for covariates are provided in Supplementary Table 3). To control for familywise error rates in multiple comparisons, we used the Bonferroni approach by adjusting the significance level to p < .008 (i.e., p < .05/6). Couples in the similarly positive profile reported higher spousal support and lower spousal strain than couples in “opposite” profiles (i.e., positive wife–negative husband and negative wife–positive husband), both of whose marital quality was significantly higher than couples in similarly negative, extremely negative husbands, and extremely negative wives profiles. No differences in marital quality were found between positive wife–negative husband and negative wife–positive husband profiles or between extremely negative husbands and extremely negative wives profiles. Couples in similarly negative profile experienced higher spousal support but similar levels of spousal strain compared with those in “extreme spouse” profiles.

Table 3.

Multilevel models for marital quality with different personality profiles as reference groups.

Variables	B	SE	B	SE	B	SE	B	SE	B	SE	B	SE
Spousal support
Couple personality profiles	Model 1		Model 3		Model 5		Model 7		Model 9		Model 11
Profile 1: Positive wife and negative husband	—	—	0.03	0.02	−0.12**	0.02	0.20**	0.03	0.32**	0.05	0.28**	0.05
Profile 2: Negative wife and positive husband	−0.04	0.03	—	—	−0.15**	0.02	0.17**	0.03	0.29**	0.05	0.25**	0.05
Profile 3: Similarly positive	0.16**	0.03	0.15**	0.02	—	—	0.32**	0.03	0.44**	0.05	0.40**	0.05
Profile 4: Similarly negative	−0.22**	0.03	−0.17**	0.03	−0.32**	0.03	—	—	0.12	0.05	0.08	0.06
Profile 5: Extremely negative husband	−0.23**	0.03	−0.29**	0.05	−0.44**	0.05	−0.12	0.05	—	—	−0.04	0.07
Profile 6: Extremely negative wife	−0.14**	0.03	−0.25**	0.05	−0.40**	0.05	−0.08	0.06	0.04	0.07	—	—
Interactions with gender	Model 2		Model 4		Model 6		Model 8		Model 10		Model 12
Female × Profile 1	—	—	0.14**	0.03	0.03	0.03	0.14**	0.04	0.08	0.07	0.27	0.11
Female × Profile 2	−0.14**	0.03	—	—	−0.11*	0.03	0.01	0.04	−0.06	0.07	0.14	0.11
Female × Profile 3	−0.03	0.03	0.11*	0.03	—	—	0.11*	0.04	0.05	0.07	0.24	0.11
Female × Profile 4	−0.14**	0.04	−0.01	0.04	−0.11*	0.04	—	—	−0.06	0.07	0.13	0.11
Female × Profile 5	−0.08	0.07	0.06	0.07	−0.05	0.07	0.06	0.07	—	—	0.19	0.13
Female × Profile 6	−0.27	0.11	−0.14	0.11	−0.24	0.11	−0.13	0.11	−0.19	0.13	—	—
Spousal strain
Couple personality profiles	Model 1		Model 3		Model 5		Model 7		Model 9		Model 11
Profile 1: Positive wife and negative husband	—	—	−0.02	0.03	0.12**	0.02	−0.15**	0.03	−0.21**	0.05	−0.18*	0.06
Profile 2: Negative wife and positive husband	0.02	0.03	—	—	0.14**	0.03	−0.13**	0.03	−0.19**	0.05	−0.15	0.06
Profile 3: Similarly positive	−0.12**	0.02	−0.14**	0.03	—	—	−0.27**	0.03	−0.33**	0.05	−0.29**	0.06
Profile 4: Similarly negative	0.15**	0.03	0.13**	0.03	0.27**	0.03	—	—	−0.07	0.05	−0.03	0.06
Profile 5: Extremely negative husband	0.21**	0.05	0.19**	0.05	0.33**	0.05	0.07	0.05	—	—	0.04	0.07
Profile 6: Extremely negative wife	0.18*	0.06	0.15	0.06	0.29**	0.06	0.03	0.06	−0.04	0.07	—	—
Interactions with gender	Model 2		Model 4		Model 6		Model 8		Model 10		Model 12
Female × Profile 1	—	—	−0.06	0.04	−0.01	0.03	−0.05	0.03	−0.05	0.05	−0.04	0.07
Female × Profile 2	0.06	0.04	—	—	0.05	0.04	0.00	0.03	−0.01	0.04	0.00	0.06
Female × Profile 3	0.01	0.03	−0.05	0.04	—	—	−0.03	0.03	−0.03	0.04	−0.02	0.06
Female × Profile 4	0.07	0.04	0.01	0.05	0.05	0.04	—	—	0.00	0.04	0.01	0.05
Female × Profile 5	0.07	0.07	0.01	0.07	0.06	0.07	0.00	0.02	—	—	0.01	0.04
Female × Profile 6	0.05	0.10	−0.01	0.10	0.04	0.10	0.00	0.02	0.00	0.03	—	—

Note. Couple N = 3,178. Models are fully adjusted for individual (gender, years of education, working for pay, race, whether in first marriage, self-rated health, and number of chronic conditions) and couple covariates (household income, length of marriage, number of children, having a coresiding child under the age 18, and Wave 2010). Models with interaction terms are adjusted for couple personality profiles.

*p < .008; **p < .001.

To examine gender differences, we added interaction terms between profile membership and gender into the models (Models 2, 4, 6, 8, 10, and 12). Compared to couples in positive wife–negative husband and similarly positive profiles (Profiles 1 and 3), the negative associations of being in positive husband–negative wife and similarly negative profiles with perceived spousal support were more pronounced among wives. The interaction for extremely negative wives profile was marginally significant (p = .01 and .03 comparing to Profiles 1 and 3, respectively), possible due to fewer observations in the profile and larger standard errors. In contrast, the association between profile membership and spousal strain did not differ between husbands and wives. Predicted spousal support for husbands and wives in different dyadic personality profiles is illustrated in Figure 2. As a sensitivity test, analyses were also run using predicted profile probabilities as focal predictors rather than predicted profile assignments (available from authors upon request). Results were very similar to those presented here, with only minor changes to significance.

Discussion

This study identified six dyadic personality profiles in a U.S. sample of older and predominantly long-married couples and examined whether profile membership accounted for individual and/or gender differences in perceived marital quality. This study contributes to the literature by addressing heterogeneity in the measures of personality similarity and advancing personality theories by considering three dimensions of personality similarity (i.e., trait positivity, distance between spouses, and pattern similarity) simultaneously when examining implications for marital outcomes.

Our findings using a typology approach revealed diverse dyadic personality configurations among relatively long-married couples, with 40% being similar (either positive or negative) and 60% being opposite or dissimilar on trait levels or overall personality patterns. This finding contradicts Kulik (2006), which suggested that a majority of Israeli older couples shared similar personalities. Factors explaining this difference are unclear; possible explanations may include different sampling methods, different measures of personality used in each study, or cultural differences in mate selection preferences and couples’ tolerance levels toward personality differences between spouses. Comparing to the individual personality profiles (RUO types; Donnellan & Robins, 2010), we found that our dyadic profiles included resilients (husbands in Profiles 2 and 3 and wives in Profiles 1 and 3), overcontrollers (husbands in Profile 1 and wives in Profile 2), but not undercontrollers. Rather, husbands and wives in the similarly negative profile and “extreme spouse” profiles were more similar to the brittle personality type as identified by Isler and colleagues (2017).

As hypothesized, the six dyadic personality profiles identified in this study manifested different magnitudes of trait positivity, distance between spouses, and pattern similarity. Our findings indicated that marital quality was associated with the personality configurations of both spouses, which supported interdependence theory and the VSA model. Our findings further expanded the theories by suggesting that personality (dis)similarity is a multidimensional concept with all dimensions contributing to individuals’ marital quality. Couples who scored high on trait positivity, pattern similarity, and low on distance (e.g., similarly positive profile) experienced the highest levels of marital quality. Partners in the positive wife–negative husband and positive husband–negative wife profiles reported lower marital quality than those in the similarly positive profile, possibly due to low similarity on personality patterns; however, they reported higher marital quality than partners in “extreme spouse” profiles, which may be explained by their higher trait positivity and smaller distance. Thus, small differences between spouses’ personalities may not be an obstacle in couples’ daily interactions, nor a hindrance to positive marital outcomes. Similarly positive and similarly negative profiles only differed by trait positivity. By comparing these profiles, we conclude that couples sharing negative personality traits suffer comparatively poor marital quality. Some previous studies concluded that pattern similarity had minimal contribution to couples’ marital quality (Barelds, 2005; Dyrenforth et al., 2010; Wang et al., 2018); this may be due to the confounding of similarly positive and similarly negative couples, as we found significant differences between these two types.

Another contribution of this study is that we identified which spouse was higher/lower on personality traits (positive wife–negative husband vs. positive husband–negative wife; extremely negative husbands vs. extremely negative wives). No significant differences on perceived marital quality were found between these contrasting profiles, suggesting that among couples with similar personality distance, average ratings of marital quality were not sensitive to which spouse scored higher. We further examined gender differences in the associations of dyadic personality profiles with marital quality. Gender interactions indicated that wives with undesirable personality traits tend to perceive even lower marital quality than those with positive personalities, suggesting that wives’ perception of marital quality is largely affected by their own personality levels. Moreover, comparing couples in similarly positive and similarly negative profiles, it appears that when husbands and wives have similarly negative personalities, wives tend to report even lower marital quality. Our findings align with social role theory and cohere with previous findings that women are more communal and have greater awareness of and sensitivity to emotions (Eagly & Wood, 2012; Nemechek & Olson, 1999).

Limitations

There are several limitations to this study. First, it is based on a cross-sectional design, preventing an assessment of causality. Our findings revealed substantial differences among dyadic personality profiles in terms of SES, health conditions, and marital outcomes, which may suggest cumulative advantages for couples with similar and positive personality traits. Future studies may employ longitudinal designs to examine trajectories of marital quality and other interested outcomes within each dyadic personality profile. Second, our measures of personality and marital quality are based on subjective evaluations, and objective measures of marital quality, such as observations of couples’ interactions, may yield different results. Also, it should be noted that people with more positive personalities may tend to report better marital quality. However, we examined bivariate correlations among personality traits and marital quality measures and found low correlations among these variables. Third, our description of trait positivity, distance between spouses, and pattern similarity were at the profile level, which limited our ability to describe couple differences at trait level. We acknowledge that some personality traits may be more important than others in explaining marital quality; further studies may address this limitation by duplicating this study on selected personality traits. Finally, a self-selectivity issue may exist in our sample, as couples who remain married into later life may have different personalities than those who divorce or never marry; future research is needed to determine whether the implications of dyadic personality profiles for marital quality vary by relationship duration, as well as age. Also, our findings from contemporary U.S. older couples may not be generalized to couples from other cultures, nor to future cohorts of older couples, given that gender roles and marital expectations are shifting for newer and future cohorts of older adults.

Conclusion

Research has long established the importance of personality for individual well-being, as well as for relational outcomes such as marital quality (Mund et al., 2016; Weidmann et al., 2016). However, previous study designs have focused on couples’ individual personality traits, their personality similarity, and the distance between their personality traits in isolation. The present study used a couple-centered approach to identify dyadic personality profiles among a sample of married U.S. older adults. We found that being similarly positive on personality traits was associated with better marital quality in later life. Being opposite but close to average undermined marital quality somewhat, but not as much as both spouses’ being negative or one spouse having extremely negative traits. Our findings highlight the heterogeneity in personality configurations even among older and long-married couples and suggest that one or another partner exhibiting undesirable personality traits is a risk factor for experiencing poor marital quality and its consequences, particularly among older married women.

Supplemental Material

JSPR-19-383_Dyadic_Pers_Profile_Supplementary_Tables - Dyadic profiles of personality among older couples: Associations with marital quality

JSPR-19-383_Dyadic_Pers_Profile_Supplementary_Tables for Dyadic profiles of personality among older couples: Associations with marital quality by Shuangshuang Wang, Kyungmin Kim and Jeffrey E. Stokes in Journal of Social and Personal Relationships

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Shuangshuang Wang

Kyungmin Kim

Jeffrey E. Stokes

Open research statement

This research was not pre-registered. The data and materials used in the research are available and can be obtained at .

Supplemental Material

Supplemental material for this article is available online.

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