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. Author manuscript; available in PMC: 2012 Oct 31.
Published in final edited form as: Am J Fam Ther. 2012 Mar 1;40(2):126–140. doi: 10.1080/01926187.2011.586619

Relationship Stability through Lenses of Complexity

Gunnur Karakurt 1
PMCID: PMC3485395  NIHMSID: NIHMS411930  PMID: 23124824

Abstract

Research on relationship stability usually considers the effect of couple outcomes and individual differences on relationship stability in isolation from each other. These separate bodies of research often lead to inconsistent results. In order to better understand relationship stability and explain inconsistencies in the literature, it is important to investigate more complex models that integrate couple outcomes and behaviors with individual differences. Motivated by these considerations, we examined the complex interplay between personal characteristics, couple interactions, and relationship stability. In particular, we investigated the relationships among investment model, big five personality traits, attachment dimensions, relationship factors and relationship stability. Participants of this study included 162 individuals (Female N=117) who are currently in a relationship from a large Midwestern university campus. Analyses were conducted using Structural Equation Modeling. Examination of the structural path parameters indicated that attachment had significant direct effect on personality, relationship factors, and relationship stability. Personality also had a significant direct effect on relationship stability. Finally personality had no direct effect on relationship factors and relationship factors had no direct effect on relationship stability. These results suggest that the effect of personality on relationship stability is direct, rather than being mediated by relationship factors.

Sometimes it is surprising that some promising relationships end while others do not. What makes a romantic relationship enduring? For many, a romantic relationship starts as a source of satisfaction and fulfillment, but finishes as a source of frustration and anguish. Due to current high divorce and separation rates, understanding the processes that lead to couple disruption becomes increasingly important. The need for increased understanding of these processes becomes more salient when the effects of separation and divorce on individuals, their families, and society at large are considered (Ambert, 2005).

How does the positive atmosphere of relationships change into disappointment? Although it is commonly believed that individuals who have highly satisfying relationships would be more likely to stay in a relationship (Simpson, 1987), the reasons that individuals remain in relationships cannot be sufficiently explained by relationship satisfaction alone. Indeed, recent research findings indicate that there are multifaceted factors associated with relationship stability.

The broad range of factors that are found to be associated with relationship stability include individual characteristics or predispositions of partners in a relationship, such as personality characteristics, individual differences, preferences for activities, attitudes, and needs. Often personality is a factor that has been widely studied (Cooper & Sheldon, 2002). A meta analysis conducted by Karney and Bradbury (1995) found that agreeableness and conscientiousness were positively associated with relationship stability for both males and females. Furthermore, neuroticism was found to be negatively associated with relationship stability (Kelly & Conley, 1987). Neuroticism and decreased marital satisfaction were also found to be mediated by marital interactions that are high in hostility and low in warmth for both genders (Donnellan, Conger, & Bryant, 2004). Similarly, Lazarides, Belanger and Sabourin (2010) found that men's neuroticism moderates the relationship between women's problem solving and relationship stability.

Research on individual differences in attachment patterns tested the hypothesis that individuals who have secure attachment patterns would be more likely to have stable relationships. Kirkpatrick and Davis (1994) found that secure attachment was indeed linked with stable relationships. A longitudinal study conducted by Druemmler and Kobak (2001) also demonstrated that as attachment security increases over time, relationships become more stable. On the other hand, Kirkpatrick and Davis (1994) reported that avoidant and secure men had equally stable relationships at one year follow-up but not at three year follow-up.

Couple interactions, i.e., relational factors, have also been widely used to understand the separation process (Gottman & Levenson, 1992). These relational factors include various states of relationship such as love and commitment, which are considered to evolve out of the relationship of partners, and dyadic interaction patterns such as closeness, conflict, and complementarities, which draw from the degree of match between individual characteristics. The most widely studied relationship factors include relationship satisfaction, commitment, closeness, intimacy, love, trust, conflict, and social support. While many of these factors were often found to be significantly associated with relationship stability, studies also produced mixed results on this association, especially when various factors were controlled. The following discussion briefly reviews these findings.

Relationship satisfaction

Some studies failed to show the influence of relationship satisfaction on relationship stability (Cate & Lloyd, 1992), while some others showed this link (Drigotas & Rusbult, 1992).

Commitment

Voluntary continuance in a relationship was reliably linked with strong commitment (Drigotas & Rusbult, 1992). Commitment was also found to be associated with a variety of purported relationship maintenance behaviors, such as tendencies of driving away from alternatives, willingness to sacrifice, and accommodating poor behavior (Rusbult, Verette, Whitney, Slovik, & Lipkus, 1991).

Closeness, intimacy, and love

Past research indicated love as a significant factor in maintaining the relationship (Attridge, Berscheid, & Simpson, 1995). However, when other factors, such as alternatives, relationship length, and sexual intimacy, were controlled, love was found not to be a significant predictor of stability (Felmlee et. al., 1990). Intimacy and closeness were found to be positively associated with premarital stability as well (Rusbult et al., 1991).

Trust

Trust was also found to be associated with commitment and relationship stability (Wieselquist, Rusbult, Foster, & Agnew, 1999). Here, trust involves predictability, or belief that the partner's behavior is consistent or belief that the partner can be counted on to be honest, reliable, and faith or conviction that the partner is motivated to be responsive and caring,

Conflict

Past research regarding the association of conflict with relationship stability yielded contradictory results. According to Felmlee et al. (1990), level of conflict was not significantly related to relationship stability. On the other hand, Murray, Holmes, and Griffin (1996) found that more stable relationships were more likely to have lower level of conflicts when positive illusion about the relationship was controlled. Similarly, Gottman and Levenson (1992) demonstrated that negative interactions and negative communication styles, or defensiveness contributed to the risk factors for relationship instability.

Social support

Couples are embedded in social networks that influence them in a variety of ways. For example, a supportive social network is expected to help couples stay together and protect them from breaking up. Social context was found to have a relatively large and multifaceted effect on the nature of the interaction between partners and relationship stability (Felmlee, 1995). In particular, existence and approval of social networks can have a positive impact on relationship stability (Felmlee et al., 1990).

Although understanding the factors that lead to relationship dissolution is valuable, an equally essential topic is to understand the processes that result in different relationship outcomes. While there exist vast amount of research on identifying factors that have a role in this process, it is also important to understand the interplay among these multiple factors. These complex interactions can help explain the conflicting results past research has generated. Motivated by these considerations, this study aimed to understand how personal, relational, and larger social factors influence the process of relationship stability/dissolution. For this purpose, a conceptual model that includes these factors was developed, which is presented in Figure 1. This model included five latent variables, representing investment, attachment security, personality, relationship functioning, and relationship stability. Each of the former four latent variables were hypothesized to have a direct effect on relationship stability. The indicators of these variables and the hypothesized direct relationships were described as follows:

Figure 1.

Figure 1

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Proposed Model

(1) It is expected that the investment latent variable, with three indicators, cost of leaving, investment to the relationship and availability of alternatives, would have a significant direct effect on relationship stability. (2) It is expected that attachment security latent variable, with two indicators, anxiety and avoidance dimensions of attachment, would have a significant direct effect on relationship stability. (3) It is expected that personality latent variable, with five indicators, extraversion, openness, agreeableness, conscientiousness, and neuroticism would have a significant direct effect on relationship stability. (4) It is expected that relational functioning latent variable, with indicators satisfaction, dependency, trust, commitment, affective responsiveness, conflict, social approval, and empathy would have a significant direct effect on relationship stability.

Method

Participants

Participants of this study included 162 individuals who are currently in a relationship. 72% of the participants were composed of females (N=117). The average age of participants was 20.75 (S.D =1.35). All participants were of age between 18 and 26 years. 82% of the participants were Caucasian, 8% were African American, 4 % were Latina, and 3% were Asian American, 3% reported other ethnicities. All participants were involved in a dating relationship ranging from 1 month to 8 years in duration. The average relationship length was 19.72 months (S.D. = 19.72). Participants were recruited through announcements in classes and subsequent e-mail communication with a link to the online survey.

Procedure

Before collecting the data, couples were asked to sign consent forms. After completing the consent form, they completed the online questionnaires measuring relationship satisfaction, closeness, commitment, communication, conflict resolution, social support network, attachment patterns, and personality trait measures. Completing the questionnaires took approximately 40 minutes. After completing the questionnaire, participants were completely debriefed about the objectives of the study. Participants also received extra class credit for their participation.

Measures

Big Five Inventory (John & Srivastava, 1999)

The BFI is a self-report inventory developed to assess the five big personality dimensions; agreeableness, openness, consciousness, neuroticism, and extraversion. It has 44 items each rated on a five point rating scale ranging from 1 (disagree strongly) to 5 (agree strongly). These items consist of short phrases with relatively accessible vocabulary. Reliabilities for the BFI subscales range from .75 to .90. For the current study, alpha reliability was .86 for extraversion,.83 for conscientiousness,.81 for agreeableness,.75 for openness, and.73 for emotional stability/neuroticism.

Experiences in Close Relationship (ECR)s

The ECR was designed to measure attachment dimensions of romantic relationships. The ECR has 36 items and each item is rated on a seven-point Likert scale. Higher scores in anxiety dimension suggest more anxiety about rejection by others and feelings of personal unworthiness with respect to interpersonal relationships. On the other hand, higher scores in avoidance dimension suggest more interpersonal distrust and avoidance of closeness with others. In the original study, items related to the avoidance dimension had .94 alpha and anxiety dimension had .91 alpha reliability (Brennan, Clark & Shaver, 1998). For the current study, alpha was .88 for anxiety and .89 for avoidance dimension of attachment.

Reasons for Commitment

Surra et al. (1997) used this instrument to measure 12 different reasons for commitment. These reasons measure dyadic, social network and circumstantial reasons for commitment. In the current study, principal component analysis was performed to examine the factor structure of the reasons for commitment measure. Assessing the scree plot, communality scores and initial analysis indicated that a one-factor solution was appropriate. Consequently, the solution was forced to a single factor, which yielded one principal component that represents commitment (eigenvalue=3.47). This subscale had nine items, all of which loaded on to this component. Three items that were not loaded significantly to this factor were removed from the analysis. Alpha coefficient for this factor was .73.

Dyadic Adjustment Scale (DAS)

The DAS was designed to measure global satisfaction and quality of couple relationship (Spanier, 1976). It has 32 self-report items and four subscales; dyadic consensus, dyadic cohesion, dyadic satisfaction, and affectional expression. Participants use a five- to seven-point Likert scale ranging from Always agree to Always disagree or All the time to Never. Reliability of the subscales ranged from .73 to .94 while the overall reliability of the scale is .96 (Spanier, 1976). Evidence of criterion-related validity came from Spanier (1976), in which the DAS successfully distinguished satisfied versus discordant marital couples. For the current study, alpha was .95 for dyadic consensus, .74 for dyadic cohesion,.79 for dyadic satisfaction, and.48 for affectional expression. Due to low reliability of the affectional expression subscale, this subscale was not included in the analysis.

Interpersonal Reactivity Inventory (IRI)

The IRI is composed of an empathic concern and a perspective taking subscale (Davis, 1983). The subscales contain a total of 28 items. Empathy scale measures the degree to which the respondent is concerned about others, is able to take the perspectives of others, and becomes emotionally related to others. Participants use a five-point Likert scale ranging from not characteristic to very characteristic. For the present study, internal consistency was .87 for empathy overall score.

Interpersonal relationship scale

This scale consists of six subscales including a total of 27 items (Hupka & Bachelor, 1979). For the purposes of this study, trust and dependency subscales were used. Trust subscale is composed of three items and alpha reliability was .82 for the current study. Dependency subscale is also composed of three items and alpha reliability was.79 for the current study. Participants used a 7-point scale to indicate their extent of agreement with each item from strongly agree to strongly disagree.

Social context

This scale measures the approval of one's social context of their current relationship. It includes four questions: “To what extent do you think your family disapproves/approves of this relationship?”, “To what extent do you think your friends disapprove/approve of this relationship?”, “To what extent do you think your partner's family disapproves/approves of this relationship?“, “To what extent to you think your partner's friends disapprove/approve of this relationship?”. For these items, alpha reliability was .85 in this study.

Investment model

Investment model factors were measured using a scale proposed by Agnew, Loving, Drigotas (2001) This scale measures the cost of leaving the relationship, investment in the relationship, and alternatives to the relationship. In order to measure the alternatives to the relationship, three items were used including, “All things are considered, how attractive are the people other than your partner with whom you could become involved?”. In order to measure the cost of leaving the relationship, two items were used including “Have you put things into your relationship that you would in some sense lose if the relationship were to end (e.g. time spent together, secrets disclosed to one another)?” In order to measure investment to the relationship, one item was used: “How much have you got invested in your relationship – things that you put into it, that are tied to activities that are connected to it, etc?”.

Stability

In order to measure relationship stability, the current relationship length was used together with participants' expected length of their relationship.

Results

Data for this study was collected by non-experimental, correlational design. Structural Equation Modeling (SEM) was utilized to analyze the proposed model, using AMOS 17. Descriptive statistics for attachment variables indicated that that overall, participants reported higher levels of attachment avoidance (M=5.62, SD =.90) than attachment anxiety (M =2.47, SD=.96). Examination of personality traits indicated that agreeableness (M=3.93. SD=.58) and the lowest level was for neuroticism (M =3.35, SD=.60) The relationship satisfaction scores showed that the participants were satisfied with their relationship with mean of 5.24 (SD=1.16).

Correlations among various measures were in the expected direction. For example, relationship expectancy length was negatively correlated with investment to relationship (r=−.49, p < .001), cost of leaving the relationship (r=−.43, p < .001), and attachment avoidance (r=−.52, p < .001). On the other hand, it was positively correlated with relationship satisfaction (r=.48, p < .001), dependency (r=.39, p < .001), trust (r=.24, p < .05), and approval of social context (r=.25, p < .05). Similarly, relationship actual length was negatively correlated with investment to relationship (r=−.40, p < .001), cost of leaving the relationship (r=−.35, p < .001), and attachment avoidance (r=−.37, p < .001), while it was positively correlated with relationship satisfaction (r=.17, p < .05), dependency (r=.29, p < .05), trust (r=.29, p < .001), and approval of social context (r=.30, p < .001) (Descriptive statistics and correlation table will be available upon request).

Testing the Hypotheses via SEM

The hypothesized structural model was tested and the goodness of fit indices were compared to several nested models to make sure that the measurement assumptions were met. These nested models were (i) independence (null) model, (ii) uncorrelated factors model, and (iii) fully saturated structured model were tested. Testing of these models also provided necessary criteria to evaluate the hypothesized model by identifying the possible lack of fit within a model. Results of analyses of the nested models are presented in Table 1. Findings indicated that null and uncorrelated factors models produced a very poor fit to the data, while the saturated model produced an excellent fit to the data. These findings suggested that there was a relationship among the latent variables and therefore gave the permission to test the hypothesized and alternative models. The fourth tested model was the proposed model, which is shown in Figure 1. This model estimated the direct and indirect effects of attachment anxiety and avoidance on jealousy. Maximum likelihood estimation of the proposed model produced a significant chi-square, χ2(185, N=162)=671.47, p<.01. The χ2:df ratio was 3.63. Examination of the hypothesized model showed that the hypothesized model provided a poor fit to the data, as indicated by low values of the AGFI=.45 and CFI=.42 indices, together with an acceptable value of RMSEA=.13.

Table 1.

Summary of fit indices for nested models on relationship stability

Model χ 2 AGFI RMSEA CFI
Null model 1173.76(253) .00 .15 .00
Uncorrelated factors model 738.73(209)* .30 .13 .43
Saturated model 0(0) 1 1
Hypothesized model 671.47(185)* .45 .13 .42
Alternative model 149(64)* .91 .07 .90
X2 Change
Uncorrelated- Saturated 738.73(209)*
Hypothesized - Alternative 522.47(121)*
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*

p<.05

In this study, an alternative model was also tested to understand the nature of interactions among factors that might have potential effect on relationship stability. The results provided in Table 1 indicated a significant χ2 difference between the first proposed model and the first alternate model. Results revealed that the alternative model provided significantly better fit as compared to the hypothesized model. Maximum likelihood estimation of the proposed model produced a significant chi-square, χ2(88, N=162)=149, p<.01. The χ2:df ratio was 1.69 (Singer & Willet, 2003). Examination of the alternative model showed that the hypothesized model provided a good fit to the data, as indicated by high values of the AGFI=.91 and CFI=.90 indices, together with an low value of RMSEA=.07. As can be seen in the Figure 2, the structural path parameters showed that attachment had significant direct effect on personality (β=−.67. p<.001), relationship factors (β =−.67, p<.001), and relationship stability (β =−78, p<.001), as expected. Personality also had a significant direct effect on relationship stability (β =−.50, p<.001). Results also indicated that structural path parameters showed no significant direct effect of personality on relationship factors, or relationship factors on relationship stability. This model explained 60% of the total variance in relationship stability.

Figure 2.

Figure 2

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Final Model

Discussion

The aim of this study was to understand the interplay among various factors that have a potential influence on relationship stability. For this purpose, we examined the interplay among personality, investment to the relationship, attachment dimensions, relationship functioning factors, and relationship stability.

According to attachment theory, the main function of attachment behavior is to maintain proximity to caregivers during infancy. Through repeated interactions, infants learn what to expect and they adjust their behaviors in terms of their expectations and beliefs. These expectations about the availability and responsiveness of attachment figures form the basis of mental representations (Bretherton & Munholland, 1999). These mental representations are thought to influence one's emotional life by affecting their perception of self and others (Bretherton & Munholland, 1999). Attachment theory asserts that relationship stability and satisfaction are largely based on satisfaction of attachment needs for comfort, care and sexual gratification. Furthermore, stability of a romantic realtionship depends on the condition that each spouse trusts that their partner can meet those needs. Secure attachment contributes to relationship stability as partners come to rely on each other and derive security from their relationships. Results of the current study indicated that attachment latent variable is central in explaining relationship stability. Attachment has not only a significant direct path to relationship stability but also has an indirect effect through intervening with personality traits.

In the current study, personality factors were also found to have a significant direct path to relationship stability. As expected, certain personality traits including high aggreeableness, high concienciousness, high emotional stability, high extraversion, and high openness had a significant direct effect on relationship stability. While past research focused on the importance of neuroticism (i.e., lack of emotional stability) as an important risk factor for relationship stability, these results demonstrate that all big five personality traits (i.e., well-adjusted personality profiles), play a significant role in relationship stability. Past research also observed that well adjusted personality profiles with low negative emotionality, high positive emotionality and constraint tend to have relatively happy relationships (Robins, Caspi, & Moffitt, 2002).

Results indicated that investment model was not significantly linked to relationship stability when all else was controlled in the model. One of the widely accepted explanations to relationship stability comes from the interdependence theory, which premises that, at least in part, individuals maintain the relationship due to the benefits of their interaction (Blau, 1967). Although predictions of interdependence theory were not found to be directly related to relationship stability, cost of leaving the relationship provided partial support in explaining relationship stability. For this reason, rather than using the investment latent variable, cost of leaving the relationship was included in the relationship functioning latent variable in the final alternative model. Indeed, cost of leaving loaded significantly on this latent variable in the final model.

Another aim of the current study was to explore the role of relationship functioning factors in relationship stability. The relationship functioning factors latent variable was composed of greater levels of relationship satisfaction, high social approval, high trust, high dependency and low levels of conflict, cost of leaving the relationship (included in the alternative model), and reasons for commitment. In the current study, it was expected that this latent variable would have significant direct path to relationship stability, however, SEM results failed to indicate this link. This might be due to small sample size or the shared variance that was accounted by other variables. Furthermore, the results of this study revealed that empathy and closeness were not significantly related to other variables. Therefore, these variables were excluded from the analysis. While closeness and empathy were found to be related to expectations of relationship length in previous studies (Kelly & Conley, 1987), it is possible that these variables may not be as crucial in predicting relationship stability when other variables are taken into account. Overall, findings regarding relationship functioning factors did not provide a good support for predictions regarding relationship stability. In other words, as compared to relationship functioning factors, personality and attachment latent variables were better at predicting expected relationship length. However, it is important to note that, relationship functioning factors latent variable was found to be significantly affected by the attachment and personality latent variables and its presence was vital for the model fit.

Findings of this study should be carefully considered in the light of its limitations. Limitations of this study included its focus on dating relationships of heterosexual college students. The dynamics of relationships in later years and among same sex couples could have different effects on relationship stability. Furthermore, the sample of the study was mostly composed of white, middle class, female undergraduates. Small sample size was another limitation of this study. Past longitudinal research relationship stability among African American and European American couples indicated that both race and education are critical to the risk of relationship instability (Orbuch, Veroff, Hassan, & Horrocks, 2002). Orbuch et al, (2002) also found that interactional processes regarding relationship instability depend on the context of race and gender. In future research, it is important to explore race and gender, and their interaction in relationship stability.

Clinical implications

The current study contributed to the understanding of relationship dynamics that lead to relationship stability/instability processes through orchestration of personality, attachment and relationship functioning factors. Findings of this study can be particularly beneficial in the conceptualization of cases whose presenting problem is relationship instability by drawing attention to multidimensional perspectives that incorporate various factors in combination. To this end, family therapists could facilitate stability by promoting attachment security and restructuring how clients are feeling about themselves (whether they feel they are worthy of love) and others (whether others will be supportive in times of need), while taking into account personality traits and relationship dynamics (such as conflict, dependency, cost of leaving relationship) as risk factors.

Therapy approaches utilizing attachment theory, such as Emotionally Focused Therapy, can be instrumental in developing awareness and facilitating change in a couple's relationship instability. In the first stages of EFT, identifying sources of distress and reflecting on the ways in which emotional experiences contribute to interactional cycle can be helpful in facilitating change. In the following stages of therapy, it can also be useful to facilitate open disclosure with enactments, and ask clients to take risks to express their vulnerable feelings, fears and their maladaptive beliefs about themselves and their partner. In conclusion, clinicians working with instable couples can aid their clients by identifying individuals and couples who are at risk by assessing their personality traits, exploring their attachment positions and restructuring their interactions by helping these couples to work together in developing security in their relationship while improving their relationship functioning by developing better communication skills, and social support network to develop a more satisfying and mutually valued relationship.

Acknowledgement

I would like to thank Mary Step (CWRU) for reading and commenting on the manuscript.

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