Charting changes in commitment

Abstract

The present study examined how turning points reported by individuals in on-again/off-again (on–off) relationships reflected relationship trajectories. Participants (N = 581) completed an online Retrospective Interview Technique asking them to report on up to 10 turning points. Participants indicated their commitment level at each turning point. Based on the variations in commitment across turning points, five trajectories emerged. Trajectory groups were compared regarding relational stability factors. Results suggest that on–off partners with a steady-low commitment trajectory reported less stability than individuals with steady-high commitment. Additionally, partners in the fluctuating trajectory, which would seemingly represent less stability, reported moderate perceptions of their relationships, faring better than the low-steady commitment group. Overall, findings add to an understanding of how to best characterize on–off relationships.

Keywords

Breakups commitment on-again/off-again relationships relational trajectories relational transitions relational stability renewals turning points

A major goal in understanding different paths or trajectories of romantic relationships is to better predict and explain the dynamics as well as the ultimate outcomes of relationships (i.e., whether couples will stay together or break up). This is especially important in on-again/off-again (on–off) relationships given that defining or measuring relational stability is more difficult (e.g., even if a couple is broken up, the relationship may not be permanently terminated). Hence, the trajectory or progression of the relationship may be more revealing about the nature of the relationship as well as the predictive of the final outcomes than overarching relational characteristics (e.g., trust, satisfaction) assessed at one time. Further, given that two thirds of adults have experienced an on–off relationship and one third are currently in one (Dailey, 2012) as well as that these relationships vary in dynamics and relational quality (Dailey, McCracken, Jin, Rossetto, & Green, in press; Dailey, Middleton, & Green, 2012; Dailey, Pfiester, Jin, Beck, & Clark, 2009), it is important to identify the developmental trajectories of these relationships to better predict which couples will achieve relative stability, permanently dissolve, and continue to cycle. Research regarding on–off relationships is moving toward this goal (Dailey, Middleton, et al., 2012) by identifying the types of on–off relationships (Dailey et al., in press) and how dimensions based on these types were associated with relational dynamics (Dailey, Jin, Brody, & McCracken, 2012).

Previous on–off research has, however, restricted analyses of turning points to breakups and renewals (e.g., Dailey et al., in press). Greater insights can be gained by assessing all turning points the partners perceive in their relationship (Baxter & Bullis, 1986; Mongeau & Teubner, 2002), and on–off relationships may encompass certain unique turning points that other relationships do not experience. Additionally, assessing on–off trajectories as well as differences in these trajectories may reveal greater nuances about the nature of these relationships and their ultimate outcomes. Hence, the current research more broadly assesses all turning points that the partners identify and how these turning points reflect different trajectories in on–off relationships.

Turning points

Turning points perspective is a process-oriented view of relationships—relationships progress and regress over time through critical events and experiences. Baxter and Bullis (1986) defined a turning point as “any event or occurrence that is associated with change in a relationship” (p. 470). Given the change or an opportunity for change, turning points are also conceptualized as transformative events (Conville, 1987) and as such are typically assessed as changes in commitment to the relationship or chance of marriage (e.g., Huston, Surra, Fitzgerald, & Cate, 1981; Koenig Kellas, Bean, Cunningham, & Cheng, 2008; Surra 1985, 1987). Given this definition, breakups and renewals are likely the significant turning points in on–off relationships (Dailey et al., in press) as transformation or change is implied in these relational transitions. Yet, even relational transitions likely involve multiple turning points (Mongeau & Teubner, 2002). Further, similar to other dating relationships, there are likely many other types of turning points that on–off partners experience such as quality time or physical separation found in Baxter and Bullis (1986) or sexual encounters and waning feelings as found in Koenig Kellas et al. (2008). Hence, assessing all turning points that on–off partners perceive throughout their relationship would provide a richer picture of on–off relationship trajectories. As such, our first research question is:

RQ1: What turning points do partners in on–off relationships report?

Relationship trajectories

A larger goal of research examining turning points is identifying the types of relationship trajectories (Huston et al., 1981; Koenig Kellas et al., 2008; Surra 1985, 1987). Based on the holistic impressions of the graphical representations of turning points over time or specific qualities of the graphs (e.g., size of increases or decreases in commitment as well as consistency of the direction of change), researchers have extracted certain trajectories. For example, Huston et al. and Surra identified four trajectories from the reports of marital partners on their dating relationships: accelerated (i.e., rapid progression to high probability of marriage), accelerated–arrested (i.e., accelerated with a point of backward progression), intermediate (i.e., moderate progression), and prolonged (i.e., slower and fluctuating path) trajectories. Surra and colleagues also identified two types of dating relationships: relationship-driven (i.e., steady and gradual increases in commitment) and event-driven (i.e., more extreme fluctuations in commitment) (Surra & Gray, 2000; Surra & Hughes, 1997). Thus, we add the following research question:

RQ2: What are the different relational trajectories of on–off relationships?

Describing the trajectories

Beyond identifying trajectories of on–off relationships, we sought to understand how different groups vary regarding a variety of dynamics associated with romantic relationships. Ultimately, this will aid in predicting the outcomes of these relationships and whether the couple reaches relative stability, permanently dissolves the relationship, or continues to cycle between being together and broken up. Because trajectories are typically based on changes in commitment or chance for a long-term relationship (e.g., marriage), they are essentially centered on relationship stability. Hence, factors associated with relational stability should reveal the greatest differences in the trajectories and provide greater understanding of the different paths on–off partners traverse.

Based on the reviews of relational stability research (e.g., Cate, Levin, & Richmond, 2002; Le, Dove, Agnew, Korn, & Mutso, 2010), the turning points literature (Baxter & Bullis, 1986), and the factors that have been associated with different types of on–off relationships (Dailey et al., in press), we chose multiple dimensions of romantic relationships to assess in relation to the identified trajectories. We focus on several general dimensions: relationship dynamics, which are perceptions, states, or feelings about the relationship (e.g., closeness, trust, love, and sexual satisfaction); communication dynamics, which are behaviors exhibited within the relationship (e.g., affection, openness, and conflict); and structural factors of the relationship (e.g., the ratio of relationship length to turning points). The specific relationship and communication dynamics selected were stronger or more consistent predictors of stability across the research (excluding commitment) or features that have emerged as significant in on–off relationships. Furthermore, we specifically highlight communication factors (compare Cate et al. and Le et al. who include these under relationship or dyadic factors) as different types of on–off relationships may vary in how they communicate but have similar feelings about the relationship (e.g., love and closeness). The specific structural characteristics included blend factors that have been salient in previous research (e.g., proportion of negative turning points; Baxter & Bullis, 1986) and features unique to on–off relationships enabling an examination of characteristics such as the proportion of turning points that are breakups and renewals and ratio of relationship length to renewals. Combined, these factors should provide a fuller understanding of how relational trajectories might vary. Importantly, theory building will differ if we find the trajectories varying primarily by relationship dynamics, for example, as compared to varying on all these dimensions.

Previous research does suggest that different groups of on–off partners vary in their reports of factors similar to those based on the perceptions of their relationship’s stability (Dailey, Middleton, et al., 2012). For example, those who perceived greater stability in their on–off relationship reported more maintenance and positive communication patterns, those who perceived their relationship as permanently terminated reported less maintenance and more negative communication, and those who anticipated more relational transitions (i.e., greater instability) reported moderate levels of these factors. Instead of grouping on–off partners based on their perceived stability, the current study will assess how different trajectories vary with regard to the factors associated with stability. We thus add the following research question:

RQ3: How do the on–off trajectories differ with regard to the factors associated with relational stability?

Triangulating types of on–off relationships

Establishing trajectories is one means of understanding how on–off relationships differ. With a similar goal, Dailey et al. (in press) extracted five types of on–off relationships from how individuals reported negotiating the transitions across their relationship. The habitual type reflected relationships in which partners tended to fall back into the relationship without much negotiation regarding the transitions. These partners renewed their relationships because it was relatively convenient and easy. For many, the on–off relationship provided companionship and comfort until one or both of the partners became interested in someone else. The mismatched type encompassed relationships in which the cyclical nature was due to the partners being mismatched regarding issues internal to their relationship (i.e., personalities and desires) or to external factors (e.g., geographic distance and different schedules). For partners in the capitalized on transitions type, relational transitions were used to test their relationship, manage relational problems, or create opportunities to improve the relationship. This category represents couples in which the on–off nature facilitated positive change in the relationship, whether it was strategic or fortuitous. Partners in the gradual separators type eventually realized that the relationship was not going to work or that they were no longer interested in continuing the relationship. Their relationships gradually came to a more defined ending, and as such, these partners reported having more closure about the ending of the relationship. In the controlling partner type, one partner tended to be more in control of the progression of the relationship or more persistent in continuing the relationship. The use of manipulation and control tactics was common.

Finding overlaps between these types and commitment trajectories may reveal stronger dimensions or types of on–off relationships. For example, a recent examination of the types as dimensions revealed that capitalizing on transitions and gradual separation facets of on–off relationships were more prominent in how they were associated with the aspects of relational quality (Dailey, Jin, et al., 2012). Hence, exploring the association between the previously established types and the trajectories may highlight factors that better distinguish on–off types. Thus, our final research question is:

RQ4: Are the on–off commitment trajectories related to the types of on–off relationships?

Method

Participants

A total of 581 participants who were currently in or had recently experienced an on–off relationship were recruited from the Amazon.com Mechanical Turk Web site.¹ Participants were eligible for participation if they were currently in or had been in an on–off relationship within the last 5 years (with “on–off” defined to participants as a relationship that had been broken up and renewed at least once) and were at least 18 years of age. The data were collected in two rounds of approximately equal numbers (n ₁ = 298; n ₂ = 283); the second round was initiated to increase the sample sizes of the trajectories identified for analyses (see below). The two samples did not significantly differ in terms of sex, age, ethnicity, current status, or relationship length. Preliminary analyses also showed that data collection round was not a significant covariate in any of the analyses and was thus excluded.

A majority of the sample was female (n = 350, 60.2%), and participants’ ages ranged from 18 to 61 years (M = 27.98, SD = 9.03). Ethnicities of the sample included: 396 (68.2%) Caucasian, 46 (7.9%) Asian or Pacific Islander, 46 (7.9%) Black or African American, 35 (6.0%) Latino or Hispanic, and 56 (9.6%) other or multiple ethnicities; two participants declined to report ethnicity. Most participants reported having a heterosexual relationship (94.5%).

Length of the relationship ranged from less than 1 to 386 months (M = 41.06, SD = 33.70, Mdn = 36). Participants reported reconciling an average of 2.44 times (SD = 2.02, range was 1–12 times), excluding 12 participants who reported 20–100 renewals. At the time of participation, 306 (52.7%) participants were currently dating their on–off partner (17 declined to report current status). About half of the sample had cohabitated with their on–off partner at some point (46.5%); and at the time of participation, half of these cohabitors (54.1%) were currently living with their on–off partner. Having cohabitated did not significantly affect the results, and thus, this factor was not included as a control variable.

Procedure

Participants completed an online questionnaire by reporting on their current or most recent on–off relationship. Given that previous research using the Retrospective Interview Technique (RIT) showed participants reported 5–10 turning points on an average (e.g., Baxter & Bullis, 1986; Koenig Kellas et al., 2008; Surra, 1985), we asked the participants to identify up to 10 turning points that occurred during their relationship. Turning points were defined to the participants as events that “change the level of commitment you have towards the relationship (i.e., the intent to maintain the relationship), could be either positive or negative events, and can, but does not have to include breakups or renewals.” We asked the participants to report all the turning points in their relationship, regardless of whether they occurred during times they were together or times they were broken up. To capture on–off turning points, we employed a modified version of the RIT (Huston et al., 1981; Koenig Kellas et al., 2008). The original RIT asked the participants to graph turning points across the course of the relationship (usually measured in months on the horizontal axis) and to specify how the turning point affected their commitment to the relationship (usually measured by a percentage of commitment from 0 to 100% on the vertical axis) (Baxter & Bullis, 1986; Koenig Kellas et al., 2008). In the current study, we modified the RIT techniques to comply with an online completion where the participants were asked, through written instructions, to describe each turning point. In attempting to make the online version as similar to the original RIT as possible, we designed the survey to be interactive in that the graph of their commitment trajectory was updated after each turning point was reported (see Figure 1 for an example). As such, participants saw how their graphs changed with each additional turning point, similar to how an interviewer would modify their trajectory during the face-to-face method of the RIT.

For each turning point, participants were asked to (1) label the turning point (e.g., major conflict, moving in together, breakup), (2) indicate their commitment at that point in the relationship on a scale of 0–100 (0 being no commitment and 100 being complete commitment ²), (3) describe why the event resulted in a change in commitment to the relationship, and (4) answer a variety of quantitative items regarding the relationship during the time period in which the turning point occurred. Participants reported between 1 and 10 turning points in their on–off relationships (M = 2.94, SD = 2.38).³ Given that on–off relationships are unique in the experience of multiple relational transitions (i.e., breakups and renewals), we calculated the proportion of turning points that were relational transitions: one third of the turning points reported were a breakup or a renewal (M = .33, SD = .35).⁴

Figure 1.

Example of turning points graph seen by participants during the survey (updated after each turning point reported).

Turning point coding

We identified types of turning points with the first sample of data. All authors read through the turning point descriptions and independently developed categories. We then met to collapse, integrate, and finalize a coding scheme. In doing so, we discussed the similarities and differences among the categories, and referenced previously established turning point categorizations (Baxter & Bullis, 1986; Koenig Kellas et al., 2008). This resulted in 16 supratypes and 36 subtypes (see Table 1). Two authors subsequently coded 20% of the data separately. Although reliability was high (κ = .73), the coders felt slight modifications to certain categories and an addition of the “realization about the relationship” category was needed. The coding manual was refined, and another 20% of the turning points were coded. Reliability was again high (κ = .74). Discrepancies were resolved through discussion, and one researcher then coded the remaining data. To ensure that bias was not present in the authors’ coding, the same 20% of the turning points were coded by an outside coder, unfamiliar with the turning points literature and blind to the study’s purposes, also yielding acceptable reliability (κ = .63). The categories of turning points are described in the Results section.

Table 1.

Turning points reported by on-again/off-again partners.

Supratypes	Frequency (%)	Subtypes
1. Get-to-know you time	38 (4.1)	First date/meeting
1. Get-to-know you time	38 (4.1)	Initial activity time
2. Relational escalation	234 (25.1)	Expressions of feelings
		Discussion of relationship
		Serious commitment
		Mutual investment
		Relational reconciliation
		Subsequent quality time
3. Physical experiences	41 (4.4)	First physical encounter
		Subsequent physical encounter
		Negative physical encounter
4. Pregnancy/having children	29 (3.1)
5. Relational de-escalation	215 (23.1)	Relational dissolution
		Discussion leading to less commitment
		Conflict
		Letting go
		Quality time
		Moving
6. External relational threats	55 (5.9)	New potential threats
6. External relational threats	55 (5.9)	Previous partner
7. Social networks	22 (2.4)	Meeting family and/or friends
7. Social networks	22 (2.4)	Network disapproval
8. Positive/helpful gestures	18 (1.9)	Instrumental support
8. Positive/helpful gestures	18 (1.9)	Social support
9. Serendipity	15 (1.6)	Circumstantial meeting
9. Serendipity	15 (1.6)	Similar vicinity
10. Special event(s)	37 (4.0)	Holiday/celebrations
		Invitation/parties
		Renewed contact/visitations
		Vacations
11. Career-related obstacles	49 (5.3)	Status
		Relocation
		Employment demands
12. Negative external behavior	21 (2.3)
13. Relational transgression	81 (8.7)	General transgression
		Infidelity
		Lying
		Violence/abuse
14. Personality characteristic	17 (1.8)
15. Realization about the relationship	38 (4.1)
16. Miscellaneous/uncodable	22 (2.4)

Trajectory coding

After the first sample of data was collected, trajectories were identified by visually assessing the graphs constructed from the commitment ratings for each turning point. To be able to assess a trajectory, only participants with three or more turning points were included (n = 123). Using a method similar to Koenig Kellas et al. (2008), the authors met to develop a coding scheme based on visual representations of the commitment trajectories.⁵ We collectively determined the criteria for grouping visually similar trajectory graphs. To begin with, we grouped a random selection of 30 graphs into categories according to the similarities in trajectory patterns. This resulted in three main patterns, two with two subtypes. Two authors then independently coded a random sample of approximately half of the participants into the five trajectory categories. Intercoder reliability was established with κ = .74. Any discrepancies were discussed until one trajectory category was agreed upon and assigned. One coder then independently coded the remaining data.

To increase the sample sizes for the analyses of RQs 3 and 4, we collected the second round of data. Visual graphs were produced including only those with three or more turning points (n = 122). The same two researchers independently coded a random sample of the second round (n = 30). Reliability was again high (κ = .91) and one of the researchers coded the remaining trajectories. Descriptions of the five trajectories are presented in the Results section.

Measures

After reporting on their turning points, participants were asked to complete measures on their general relational qualities. Specifically, for most of the measures, participants were asked to think across their relationship when completing these measures. The following scales were assessed on a Likert scale (1 = strongly disagree; 7 = strongly agree) unless otherwise noted. Means were used to create the overall scores, with higher scores indicating higher levels of each variable. See Table 2 for the overall means and SDs for the variables. All variables were normally distributed.

Table 2.

Raw means (and SDs) for stability characteristics by trajectory.

	Early dip	Later dip	Steady-high	Steady-low	Fluctuating	Total
Relationship factors
Similarity	4.93 (1.01)	5.13 (1.44)	5.03 (1.18)	4.10 (1.36)	4.64 (1.50)	4.73 (1.42)
Love	6.16 (.79)	6.02 (.97)	6.32 (.67)	4.86 (1.56)	5.68 (1.15)	5.79 (1.20)
Ambivalence	4.32 (1.44)	3.88 (1.60)	3.83 (1.78)	4.92 (1.42)	4.23 (1.57)	4.21 (1.61)
Closeness	4.20 (1.97)	5.17 (1.85)	4.88 (2.00)	2.13 (1.47)	3.15 (2.07)	3.72 (2.20)
Trust	4.52 (1.44)	4.65 (1.72)	4.31 (1.57)	3.14 (1.63)	3.50 (1.79)	3.86 (1.77)
Sexual satisfaction	5.60 (1.21)	5.17 (2.00)	5.28 (1.81)	4.40 (1.85)	5.09 (1.73)	5.05 (1.80)
Stress	4.92 (1.33)	4.49 (1.48)	4.72 (1.53)	5.05 (1.21)	5.08 (1.18)	4.89 (1.33)
Communication and interaction dynamics
Affection	5.92 (.97)	5.86 (1.02)	5.84 (.93)	4.98 (1.17)	5.39 (1.27)	5.52 (1.17)
Openness	6.17 (.82)	5.78 (1.17)	5.86 (1.29)	5.08 (1.46)	5.52 (1.42)	5.60 (1.35)
Maintenance	5.53 (.83)	5.40 (1.23)	5.70 (.97)	4.66 (1.56)	5.00 (1.33)	5.18 (1.31)
Conflict	4.63 (1.27)	4.50 (1.40)	4.68 (1.18)	4.75 (1.61)	4.59 (1.48)	4.62 (1.41)
Work on relationship	5.55 (1.11)	5.48 (1.32)	5.85 (1.07)	4.51 (1.40)	4.89 (1.37)	5.15 (1.37)
Structural factors
Proportion of positive turning points	.58 (.16)	.55 (.19)	.52 (.32)	.42 (.30)	.50 (.15)	.50 (.23)
Proportion of negative turning points	.39 (.15)	.39 (.17)	.33 (.31)	.56 (.29)	.47 (.16)	.44 (.23)
Relationship length/turning points	9.80 (6.48)	8.40 (5.90)	11.86 (7.37)	9.67 (7.21)	7.15 (5.61)	8.78 (6.51)
Relationship length/renewals	30.83 (19.53)	18.76 (16.97)	20.14 (14.90)	16.63 (17.58)	17.82 (15.95)	19.15 (16.65)
Proportion of turning points that were breakups/renewals	.48 (.24)	.39 (.22)	.39 (.26)	.33 (.24)	.36 (.20)	.37 (.22)

Relationship factors

We assessed similarity using three items that were created for the purpose of this study based on previous romantic relationship research (e.g., Surra & Longstreth, 1990). Items asked participants how similar they felt they and their partner were in: attitudes and beliefs, interests and activities, and personalities. Responses were solicited on a 7-point Likert-type scale (1 = not at all similar; 7 = very similar). Reliability (Cronbach’s α) across all items was .76. Love and ambivalence were measured using items adapted from Braiker and Kelley (1979). Seven items were in the love subscale (e.g., “To what extent have you felt that your relationship is special compared with others you had been in?”; α = .92) and five items were in the ambivalence subscale (e.g., “How much have you thought or worried about losing some of your independence by being involved with your partner?”; α = .84). Responses were solicited on a 7-point Likert-type scale (1 = not at all; 7 = a great deal), and phrasing of the items was modified to reflect the entirety of the relationship. To measure closeness, we utilized Aron, Aron, and Smollan’s (1992) one-item Inclusion of Other in Self measure to assess closeness at the time of participation. Response was solicited on seven Venn diagrams (1 = least closeness; 7 = most closeness). The trust measure includes eight items from the Dyadic Trust Scale (Larzelere & Huston, 1980) to assess the degree to which the participants felt they could rely on their partners. Example items include: “I feel that I can trust my partner completely” and “My partner is primarily interested in his/her own welfare” (reverse-scored; α = .92). Sexual satisfaction was measured using a two-item index (Sprecher, 2002). One item was a global assessment, “How sexually satisfying is the relationship to you?” on a 7-point Likert-type scale (1 = not at all; 7 = very), while the other asked “How unrewarding or rewarding are your partner’s contributions in the area of sex (e.g., meets your needs and preferences)?” on a 7-point Likert-type scale (1 = very rewarding; 7 = very rewarding). The correlation of these two items was strong (r = .84, p < .001). The measure of relational stress included six items from Dailey, Middleton, et al. (2012) that reflected three dimensions of relational stress prevalently found in on–off relationships (α = .82): relational uncertainty and ambivalence (e.g., “I have had a lot of mixed feelings about this relationship”), emotional frustration (e.g., “This relationship has made me feel like I was on an emotional roller-coaster”), and doubt or disappointment (“I have often been disappointed about things in our relationship”).

Communication factors

Affection was measured using eight items from the Affectionate Communication Index (Floyd & Morman, 1998), which assess the amount of affection participants communicated to their on–off partner through verbal expressions (e.g., saying “I love you”) and nonverbal gestures (e.g., hugging). Reliability (Cronbach’s α) across all items was .85. Openness was measured with the 10-item Self-Disclosure Index (Miller, Berg, & Archer, 1983). Responses were solicited on a 7-point Likert-type scale (1 = have not discussed at all; 7 = have discussed fully and completely). Participants were asked to rate the degree to which they had disclosed about certain areas (e.g., “my personal habits” and “what I like and dislike about myself”). Cronbach’s α across all items was .94. Maintenance (“To what extent have you tried to change your behavior to help solve certain problems between you and your partner?”; α = .83) and conflict (e.g., “How often have you and your partner argued with each other?”; α = .84) were measured through Braiker and Kelley’s (1979) five-item subscales. The relationship work measure included four items created for the purpose of this survey assessing participants’ proactive actions toward the relationship to resolve or make changes created for the purpose of this study (e.g., “We worked to resolve the issues in our relationship” and “We tried to make our relationship better between the breakup(s) and renewal(s)”; α = .85).

Structural factors

Given that a few participants noted turning points that did not change their commitment level, we calculated the proportion of positive turning points (i.e., number of turning points that increased commitment divided by the total number of turning points reported) and proportion of negative turning points (i.e., number of turning points that decreased commitment divided by the total number of turning points) separately. We also assessed the ratios of relationship length to the total number of turning points, relationship length to number of renewals, and number of breakups to number of renewals.

On–off types

To assess on–off types for RQ4, we employed the paragraphs created by Dailey et al. (in press), which reflect the characteristics described above, and asked the participants to choose which paragraph best described their relationship. We also asked the participants to rate how well the paragraph chosen reflected their relationship on a 7-point scale (1 = does not describe our relationship well; 7 = very much describes our relationship). In terms of frequencies, 35 participants (14.3%) selected the habitual paragraph, 46 participants (18.8%) selected mismatched, 54 participants (22.0%) selected capitalized on transitions, 27 participants (11.0%) selected gradual separators, and 65 participants (26.5%) selected controlling, while 18 participants (7.3%) did not select any paragraph.

Results

Types of turning points

RQ 1 asked what types of turning points partners in on–off relationships report. A total of 932 turning points were identified from the first round of data collection (summarized in Table 1). Several turning points provided further insight into the process of developing and dissolving an on–off relationship. For instance, the most frequently reported turning points were the relational escalation and relational de-escalation supratypes. This finding makes intuitive sense, given that both escalating and de-escalating transitions are defining features of on–off relationships. Yet, although many of the other supratypes (e.g., get to know your time, physical experiences, external threats, social networks, negative external behavior, and personality characteristics) overlapped to some degree with the previous turning points research (e.g., Baxter & Bullis, 1986; Koenig Kellas et al., 2008), this more diverse sample (e.g., in age) yielded additional relational experiences that can affect the developmental process of romantic relationships. The following description focuses on these more unique categories of turning points that were salient to these on–off relationships.

The pregnancy or having children supratype involved participants discussing the idea of having children or the experience of preparing for and raising child(ren). Participant reports of pregnancy or having children often coincided with increases in commitment and/or a renewal in the relationship. Because more women reported this category than men, future research may consider potential sex differences on how these turning points and on–off relationships are experienced. Conversely, a typically mitigating influence on commitment was career-related obstacles, which involved turning points surrounding or involving employment based on three distinct issues: change of status, relocation, and demands. The status subtype involved participants choosing new employment, selecting a different occupation, or becoming unemployed that impacted the relationship. The relocation subtype indicated separation caused by employment, military obligations, or college attendance, while the employment demands subtype indicated changes in the relationship due to external occupational demands placed on one or both partners (e.g., working for long hours, taking the bar exam, and graduate school stressors).

Other turning points also emerged at the onset of certain special events. The special events supratype often led participants to re-evaluate their relationships based on their inclusion or exclusion in events. Specifically, subtypes included special events, invitations/parties, vacations, and renewed contact/visitations. These occasions either created more relational strength through invitations to events or exposure to partner’s social networks. Or the rejection (e.g., refusing invitation to attend a family affair) or rebuffing (e.g., neglecting to buy a holiday present) acted as a relational barometer to show less interest or involvement in the relationship.

Additionally, circumstantial events created new opportunities to renew through serendipitous occasions and similar proximities. Similar to a circumstantial meeting by Koenig Kellas et al. (2008), the serendipity supratype classified participants’ turning points that were born out of accident or coincidence (e.g., attending a mutual friend’s party or funeral). Another opportunity for relationship renewal appeared when partners were again in similar vicinities (e.g., living in nearby proximity again, returning to college, or back from the military deployment).

Relational transgressions also emerged more explicitly in this on–off sample. Many were serious infractions of relational rules such as infidelity, lying, and less prevalently, violence or abuse. Other smaller violations fell under a general category such as being stood up for a date. As would be expected, these transgressions were associated with decreases in commitment.

On–off commitment trajectories

RQ2 asked what types of trajectories are revealed by on–off partners’ changes in commitment across their reported turning points. The five trajectories identified are presented in Figure 2. The first trajectory type, “dip” in commitment (n = 57, 23.3%), represents those relationships where one or two times in the relationship there was a decrease in commitment, often followed by a trend toward increased commitment afterward. Participants within this group also experienced relatively high, or average, levels of commitment overall. The dip in commitment group was subdivided into two subtypes, early dip and later dip. The early dip group (n = 15, 6.1%) tended to experience a downward turn in commitment at the beginning of the relationship and be more stable at the end. The later dip group (n = 42, 17.1%) showed a downward turn in commitment toward the middle or at the end of the relationship. This group as a whole may resemble the accelerated–arrested type found by Surra (1987).

Figure 2.

On-again/off-again trajectory examples: (a) early dip in commitment; (b) later dip in commitment. (c) steady-high; (d) steady-low and (e) fluctuating.

The second trajectory type, steady commitment (n = 86, 35.1%), illustrated a relatively unchanging, flat, linear progression of the on–off relationship over time. Participants in this group experienced small changes with the trend demonstrating either increases or decreases to commitment. As such, this type was divided into two subcategories; steady-high (n = 43, 17.6%) representing those trajectories with higher levels of commitment (averaging approximately 60% or higher across turning points), and steady-low (n = 43, 17.6%) represents those trajectories with moderate, low or decreasing commitment (60% or lower on average).

The final and most frequent trajectory type (n = 102, 41.6%), fluctuating commitment, showed larger, more frequent changes in the on–off relationship across time. Commitment could be either high or low at the end of the relationship and turning points were often more evenly spaced across time. Because of the often drastic rise and fall of commitment levels across the on–off relationship, on average, participants in this category tended to have moderate levels of commitment overall.

Trajectory differences and relational stability characteristics (RQ3)

RQ3 asked how different on–off trajectories differ with regard to the factors associated with relational stability. We employed analyses of covariance (ANCOVA) to compare the on–off trajectories with the relational stability factors, controlling for current status of the relationship.⁶ Pairwise comparisons using a Bonferroni adjustment assessed where the specific significant differences occurred. Table 2 includes the raw means and SDs of the characteristics by trajectory. Table 3 presents the results of the ANCOVAs, estimated marginal means, and SEs by trajectory.

Table 3.

ANCOVA results with estimated marginal means (and standard errors) for stability characteristics by trajectory.

	Early dip	Later dip	Steady-high	Steady-low	Fluctuating	F	df	Trajectory partial η ²
Relationship factors
Similarity	4.70 (.35)	4.93 (.22)	4.92 (.21)	4.27 (.23)	4.75 (.14)	1.40	4, 223	.03
Love	5.99 (.29)^a	6.06 (.17)^b	6.24 (.17)^c	4.99 (.18)^abcd	5.76 (.12)^d	6.78***	4, 222	.11
Ambivalence	4.55 (.41)	4.08 (.25)	3.94 (.25)	4.75 (.26)	4.11 (.16)	1.72	4, 222	.03
Closeness	3.62 (.46)	4.65 (.28)^ab	4.60 (.28)	2.57 (.29)^ac	3.41 (.18)^bc	9.16***	4, 225	.14
Trust	3.99 (.40)	4.17 (.24)	4.05 (.24)	3.55 (.25)	3.75 (.16)	0.98	4, 225	.02
Sexual satisfaction	5.34 (.46)	4.94 (.28)	5.16 (.28)	4.59 (.29)	5.21 (.19)	1.02	4, 225	.02
Stress	5.11 (.34)	4.65 (.21)	4.81 (.21)	4.91 (.21)	5.00 (.14)	0.64	4, 225	.01
Communication and interaction dynamics
Affection	5.87 (.30)	5.81 (.18)^a	5.81 (.18)^b	5.02 (.19)^ab	5.42 (.12)	3.13*	4, 223	.05
Openness	6.04 (.35)	5.66 (.21)	5.79 (.21)	5.18 (.22)	5.58 (.14)	1.46	4, 223	.03
Maintenance	5.40 (.33)	5.29 (.20)	5.64 (.20)^a	4.76 (.21)^a	5.06 (.13)	2.51*	4, 222	.04
Conflict	4.77 (.37)	4.63 (.23)	4.75 (.22)	4.64 (.24)	4.52 (.15)	0.22	4, 222	.00
Work on relationship	5.25 (.32)	5.21 (.20)	5.70 (.20)^ab	4.74 (.20)^a	5.03 (.13)^b	3.10*	4, 225	.05
Structural factors
Proportion of positive turning points	.51 (.06)	.49 (.03)	.50 (.03)	.46 (.03)	.53 (.02)	.81	4, 235	.01
Proportion of negative turning points	.45 (.05)	.44 (.03)	.36 (.03)^a	.52 (.03)^a	.45 (.02)	2.84*	4, 235	.05
Relationship length/ turning points	9.05 (1.64)	7.72 (1.00)	11.52 (.98)^a	10.22 (1.01)	7.46 (0.64)^a	3.94**	4, 233	.06
Relationship length/renewals	28.95 (4.25)	16.85 (2.80)	19.31 (2.55)	18.11 (2.88)	18.74 (1.76)	1.58	4, 208	.03
Proportion of turning points that were breakups/renewals	0.49 (.06)	0.39 (.04)	0.39 (.04)	0.33 (.04)	0.36 (.02)	1.44	4, 235	.02

Note. Same superscripts indicate significant differences in pairwise comparisons.

*p < .05, **p < .01, ***p < .001.

Relational dynamics

When controlling for current relational status, two of the seven relational dynamics showed differences by trajectory. Those with a steady-low trajectory reported significantly less love with their on–off partner than all other trajectory types. For closeness, those with the trajectories of later dip and steady-high reported significantly more closeness than both those with a steady-low trajectory and a fluctuating trajectory. No significant differences were found between trajectories and similarity, ambivalence, relational stress, trust, or sexual satisfaction.

Communication and interaction dynamics

Of the five communication factors, three factors varied by trajectory. Those with the trajectories of later dip and steady-high reported significantly more affection than those with a steady-low trajectory. In addition, those with a steady-high trajectory reported significantly more maintenance than those with a steady-low trajectory. Finally, those with a steady-high trajectory reported significantly more relational work than both those with a steady-low trajectory and a fluctuating trajectory. No significant differences were found for openness or conflict.

Structural characteristics

For analyses including relationship length, the two outliers (i.e., 168 and 362 months) were excluded from the analyses. Of the five structural characteristics, two characteristics varied by trajectory when controlling for current status. Those with a steady-high trajectory reported significantly lower proportion of negative turning points than those with a steady-low trajectory. Trajectory varied for the length of the relationship in relation to the number of turning points, with the fluctuating group reporting a smaller ratio of length to turning points than the steady-high group. No significant differences were found between the trajectory groups and the proportion of positive turning points, length of relationship in relation to the number of renewals, or the proportion of turning points that were breakups or renewals.

On–off types and trajectories (RQ4)

Analysis of RQ4 showed that commitment trajectories varied by on–off relationship type, χ ²(16) = 32.29, p < .01.⁷ The early dip participants were more likely to endorse controlling (53.3%), the later dip and steady-high participants were more likely to endorse capitalized on transitions (35.7 and 39.0%, respectively) followed by controlling (23.8 and 26.8%, respectively), the stable-low group was fairly equally likely to endorse habitual (27.0%), mismatched (21.6%), and controlling (29.7%), and the fluctuating group was more likely to endorse mismatched (29.3%) or controlling (27.2%).

Discussion

Previous research on romantic relationships has emphasized the importance of assessing turning points throughout the life span of a relationship (e.g., Baxter & Bullis, 1986). Researchers have examined trajectories of dating (Surra & Hughes, 1997), marital (Huston et al., 1981), and postdissolution (Koenig Kellas et al., 2008) relationships. However, on–off relationships are in many ways different from noncyclical relationships (Dailey et al., 2009) and are also unique in that turning points may occur during breakups and renewals as well as other salient relational events. Although previous research has examined the breakups and renewals as turning points in on–off relationships (Dailey et al., in press), the present study extends on this by examining all potential turning points and the varying trajectories the turning points reflect.

Turning points in on–off relationships

Many of the turning point categories that emerged in this study overlap with the existing literature. However, perhaps due to both the nature of on–off relationships and the current nonstudent sample, several novel turning points emerged or were more predominant, such as pregnancy/having children, career-related obstacles, special events, serendipity, and relational transgressions.

The pregnancy/having children and career-related obstacles categories represent larger forces that might push a relationship together or pull a relationship apart. For example, pregnancy or having children may be acting as an investment or external psychological barrier to exiting their relationships (Levinger, 1976, 1999; Rusbult, 1983). Previous research indeed shows that dissolution is less likely when children are present (Kamp Dush, 2012). Conversely, career-related moves or a high dedication to work may hinder commitment to a relationship. This finding also substantiates the mismatched on–off type, in which external factors such as life stages, bad timing, or geographic distance played a role in the on–off transitions (Dailey et al., in press). In on–off relationships, these larger forces might have influences or implications less considered in research on dating relationships such as financial costs (e.g., traveling to see the relational partner, additional childcare costs), which may lead partners to renew or break off the relationship.

Additionally, several participants reported turning points relating to special or serendipitous events that often led to renewing the relationship. Although relatively infrequent, these types of turning points suggest either that these events happen more in on–off relationships or that partners who enter into on–off relationships are more susceptible to these events. Future research is needed to explore how these events play a role in the on–off nature of these relationships as compared with other romantic relationships.

Relational transgressions were also reported relatively frequently. These transgressions tended to be more severe in nature such as aggression, deception, or infidelity, which can have implications for the stability of the relationship (Afifi, Falato, & Weiner, 2001; DeSteno, Bartlett, Braverman, & Salovey, 2002). These on–off partners did not necessarily permanently dissolve their relationship when faced with a severe relational transgression. Or viewed in a different way, the transgressions may have been serious enough to warrant a breakup, yet the partners found a way to resolve the transgression and reconcile the relationship. As such, the process of forgiveness in on–off relationships would be insightful to examine in future research. For example, perhaps these partners are more likely to employ a conditional type of forgiveness (e.g., Waldron & Kelley, 2005) that facilitates the on–off nature of the relationship.

Trajectories of on–off relationships

Examining relationship trajectories is a natural extension of a turning points approach (Huston et al., 1981; Koenig Kellas et al., 2008; Surra 1985, 1987), and examining how trajectories differentiate on–off relationships based on a variety of characteristics provides a richer understanding of how on–off relationships function. Our analysis revealed three general trajectories and several subtypes—steady (low and high), dip in commitment (early and later), and fluctuating. One major conclusion across the findings is that individuals with a steady-low trajectory reported lower levels of closeness, relationship work, affection, and love. That these differences emerged for a group which is characterized by consistently low levels of relational quality is not particularly surprising. However, the significant differences remained even when controlling for current relational status. In other words, some participants in steady-low relationships reported lower levels of certain relationship and communication dynamics than other trajectories, regardless of whether they were together. This finding somewhat contrasts with the previous on–off research, which has typically found that a positive characteristic (e.g., those who capitalize on transitions enact positive change in a relationship) differentiates types of on–off relationships (e.g., Dailey, Jin, et al., 2012; Dailey et al., in press). When considering on–off relationships as a function of their trajectories, it appears that some partners remain in relationships with consistently low commitment, and these couples tend to be different from other types of on–off relationships. Perhaps, as interdependence theory suggests (e.g., Thibault & Kelley, 1959), these are individuals who perceive no greater alternatives and prefer the companionship of a less satisfying relationship than to be alone (e.g., Felmlee, Sprecher & Bassin, 1990; Sprecher, 2001). Hence, it is not surprising that this steady-low group was relatively more likely to endorse being in the habitual on–off type, a type that repeatedly returns to the relationship because of a desire for companionship and with minimal discussion about changing the relationship.

Additionally, although individuals who reported a steady-low trajectory tended to report fewer positive factors (e.g., love and affection), they did not necessarily report more negative factors in their relationship. For example, individuals with a steady-low trajectory did not report significantly more stress or conflict, yet they did experience a higher proportion of negative turning points. Given this, it appears that most on–off relationships are characterized by some negative perceptions or behaviors, regardless of their trajectory or turning points.

Interestingly, despite the fluctuating trajectory reflecting oscillations in commitment (e.g., seemingly less stability), individuals with fluctuating trajectories only differed in their reports of relational closeness, work, and love—typically falling in between the steady-high and steady-low trajectories. The current fluctuating group resembles relational trajectories that emerged in previous research. Koenig Kellas et al. (2008) reported a “turbulent” trajectory in postdissolution relationships, and people who reported a turbulent trajectory had lower quality relationships than individuals who reported an upward projection trajectory (which is conceptually similar to individuals with a steady-high or dip in commitment trajectories in the present study). Additionally, Surra and Hughes (1997) found evidence for an event-driven pattern of commitment in premarital relationships defined as frequent and dramatic changes in commitment due to a focus on singular events similar to the fluctuating trajectory. Also similar to the present study, individuals with event-driven patterns differed in some ways (e.g., stability of commitment) from the other trajectory types, but did not differ significantly for factors such as perceived alternatives, love, or current status (Surra & Hughes, 1997). Overall, these findings generally parallel those of the present study.

Finally, individuals with dip in commitment and steady-high trajectories did not significantly differ from one another. Even if partners experience a dip in commitment at some point in their relationship, they still report similar overall relational quality as individuals with a stable, highly committed trajectory. These trajectories resemble Surra’s (1985) accelerated and accelerated–arrested, Surra and Hughes’ (1997) relationship-driven, and Koenig Kellas et al.’s (2008) upward relational progression trajectories, which reflected more positive relationship dynamics. The extent to which on–off partners capitalize on their relational transitions closely relates to relational quality (Dailey, Jin, et al., 2012), so it may be that individuals with only one or two dips in commitment throughout their relationship were able to capitalize on the transition to improve their relationship. Indeed, as discussed in the following section, individuals with a dip in commitment and steady-high trajectories were more likely to report a capitalizing on transitions type of relationship.

These patterns in trajectories generally parallel the findings of Dailey, Middleton, et al. (2012) who presented a model of on–off relationships in determining which relationships would achieve relative stability, permanently dissolve, or continue to cycle. Those with only a dip in commitment or steady-high commitment are likely those who will achieve relative stability given their higher relational quality. Those with a consistently low level of commitment are likely those who will eventually permanently terminate their relationship. With low levels of relational quality, these partners are likely waiting for a better alternative and will dissolve the relationship if this alternative materializes. The fluctuating trajectory likely reflects those who will continue to cycle. In the findings of Dailey, Middleton, et al., those expecting their relationship to continue cycling reported moderate levels of relational quality when compared with the other groups, which is consistent with the current findings for the fluctuating trajectory. Combining the current factors associated with the trajectories with the factors assessed by Dailey, Middleton, et al. (e.g., relational uncertainty and relational maintenance) provides more evidence regarding how on–off relationships differ in relational quality; the fluctuating or continually cycling group has more tepid perceptions of their relationship, which may result in not feeling comfortable in fully committing to the relationship or leaving the relationship.

Given these patterns, those with short dips or consistently high commitment levels or steady, low levels of commitment may be relatively similar to other dating relationships. The fluctuating group, however, may be the most distinct from other dating relationships. For example, the current fluctuating group in some ways contrasts with the work of Arriaga (2001; see also Arriaga, Reed, Goodfriend, & Agnew, 2006), which shows that individuals reporting fluctuations in satisfaction were more likely to dissolve their relationships even after controlling the overall level of satisfaction. Controlling for current status, our data revealed that the fluctuating group fared better than those with steady, low commitment. Perhaps fluctuations in one’s own commitment operate differently than satisfaction, which may fluctuate on a daily basis as compared to a general sense of commitment with the relationship. Alternatively, on–off partners may expect and better tolerate fluctuations in their relationships than other types of romantic relationships. Regardless of the explanation, the fluctuating group may be the most revealing about the nature of on–off relationships.

More generally, in looking at the findings across the three dimensions of relationships, no clear patterns emerged in terms of which factors differentiated the trajectories best. Although more of the communication dynamics showed significant differences among the types, the relationship dynamics yielded the largest effect sizes. Perhaps the strongest conclusion is that the structural factors least differentiated the trajectories. This is consistent with the previous on–off research suggesting that relational and communication dynamics play a larger role in the varying natures of on–off relationships, whereas structural features play a minimal role (Dailey, Middleton, et al., 2012; Dailey et al., 2009; Dailey et al., in press). Hence, on–off partners appear to have differing experiences based on their perceptions of their relationship and interaction patterns rather than on whether they have more or less turning points or renewals relative to their relationship length. As such, future research and theory building should focus on these dynamic qualities of relationships rather than the structural features. We should also note that less than half of the factors we assessed yielded significant differences. Yet, when current relational status is not controlled, most do yield differences by trajectory. Although there are valid reasons for controlling the current status, this is somewhat limiting in the assessment of on–off relationships given that relational status often fluctuates.

Triangulating types based on trajectories

The study also investigated whether individuals differed on their self-reported on–off relationship type based on their trajectory. As mentioned in the previous section, individuals with a dip in commitment or steady-high trajectories were more likely to report capitalized on transitions type of relationship. This is a chief finding from this analysis and supports that some couples are able to use the transitions to improve or stabilize the relationship (Dailey et al., in press). In addition, individuals with a fluctuating trajectory were more likely to report a mismatched or controlling type. The controlling type involves one partner persistently controlling the progression of the relationship, and mismatched types often have external factors, such as different life stages or geographic distance, which keep the relationship in a fluctuating state (Dailey et al.). In the present study, it appears that individuals who experienced frequent, often dramatic changes in commitment over the course of their relationship were also more likely to self-categorize their relationship as being influenced by external factors and partner control. Future research would be helpful in determining if there are multiple types of fluctuating trajectories in terms of experiences in and outcomes of the relationship.

Despite some overlap, there was not an exact, one-to-one match between trajectories and types. Further, a surprising finding was that many of the participants in the three relatively steady and high commitment trajectories selected the controlling type, which is highly counterintuitive. The weak level of overlap and counterintuitive findings may be due to the measure of on–off types. To decrease fatigue effects and to clearly classify participants into one type, we employed paragraph descriptions of each type and asked the participants to select the one paragraph that described their relationship best. Although most participants reported the paragraph that described their relationship well (M = 5.72 on a 7-point scale), more sophisticated measures may reveal stronger associations between the types and trajectories.

Limitations and future directions

The present study has several strengths, including the extension of the RIT to an online format and assessing the on–off relationships from a developmental perspective including the recall of all potential turning points and not just the breakups and renewals. However, there are several limitations that should be addressed in future studies. First, the study utilized only half of the sample in identifying the trajectories, as only individuals who reported more than three turning points were included, and those who reported three or more turning points did differ from those reporting only one or two (e.g., less likely to be currently together and more stressed, but also more affectionate and open). Additionally, an interview-based method of RIT may have provided an opportunity to inquire about smaller turning points. Yet, the potential drawback of the online version is mitigated by the fact that this methodology allowed for the recruitment of a larger and more diverse sample. The diversity of the sample could, however, be increased. The sample was not representative of the U.S. population and important differences may exist for those of different life stages or ethnicities. In addition, although sex differences did not strongly emerge in these data, men and women may experience on–off relationships differently (e.g., pregnancy as a turning point). Furthermore, we included married individuals in the current sample, as preliminary analyses did not show they were characteristically different from those who were dating; yet, marital status may impact certain dimensions and experiences of on–off relationships.

Coding trajectories with only three turning points may also present validity issues given that the addition of a fourth turning point might have changed the classification. Furthermore, many individuals may experience more turning points after participation, which could change their trajectory. Despite this difficulty, the coders had relatively high agreement when assessing the trajectories. Perhaps this indicates that a minimum relationship length should be employed; yet, the correlation between the relationship length and number of turning points was −.02, p = .597. Excluding trajectories with fewer turning points would also ignore certain nuances that may be important in identifying the types of on–off relationships.

Finally, the trajectories did not differ on several relational qualities, including openness, work on the relationship, stress, sexual satisfaction, and conflict after controlling for current relational status. Yet, perhaps commitment (the central variable in the RIT) is not the optimum variable for examining on–off relationships. For example, in an on–off relationship, in particular, partners may break up but still have high commitment to their relationship. Future research should investigate whether another variable better suits the investigation of on–off relationships such as satisfaction or perceived viability of the relationship.

Conclusion

The present study extends the previous research on the trajectories and turning points of romantic relationships by obtaining retrospective accounts of major events during on–off relationships. Several unique turning points (such as having children) have emerged. In addition to supporting the previous research’s finding that some on–off couples can utilize their transitions to make positive changes in the relationship, the current study offers new contributions in understanding on–off relationships. For example, a stable and low-level of commitment or involvement in the relationship may be an additional factor to consider; this trajectory was most clearly different than the others, a finding that has not emerged in previous on–off research. Additionally, partners in the fluctuating trajectory, which would seemingly represent less stability, actually reported more moderate perceptions of their relationships, faring better than the steady-low commitment group. Overall, categorizing on–off partners based on their relational trajectories provides further insight into the heterogeneous nature of on–off relationships.

Footnotes

Authors’ note

René M. Dailey (University of California, Santa Barbara) is an associate professor in the Communication Studies Department at the University of Texas at Austin where NB (Arizona State University), LL (University of Wisconsin-Milwaukee), and BC (Texas State University, San Marcos) are doctoral students. A previous version of this paper was presented at the 2012 International Association for Relationship Research conference in Chicago, Illinois, USA.

Notes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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