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. 2022 Aug 29;62(2):795–817. doi: 10.1111/famp.12811

Measuring “we‐ness” in couple relationships: A social identity approach

Tegan Cruwys 1,✉, Erica I South 2, William Kim Halford 2, Judith A Murray 2, Martin P Fladerer 3
PMCID: PMC10946554  PMID: 36038954

Abstract

Research on couple relationships has increasingly focused on the concept of “we‐ness”, the subjective closeness of the couple bond, as crucial to predicting relationship outcomes including satisfaction and dissolution. However, diverging perspectives on the definition, terminology, and measurement of this concept persist. We drew upon social identity theorizing to clarify the nature of we‐ness and investigate its predictive utility. Participants were 375 members of the general community in long‐term intimate relationships. The sample were aged 18–74 (M = 37.22; SD = 12.00) and 69% were women. Participants completed seven measures of we‐ness drawn from both the couple literature and the social identity literature. We used exploratory factor analyses to establish the latent structure of we‐ness, and regression analyses to examine the utility of each we‐ness factor in predicting relationship satisfaction and likelihood of dissolution. A four‐factor solution was extracted and the factors were labeled couple identity, partner liking, relationship orientation, and partner similarity. Each of the four factors explained unique variance in relationship quality, with couple identity being most strongly associated with positive outcomes. We conclude that couple research can fruitfully draw upon social identity theorizing in conceptualizing we‐ness. This has implications both for more effectively measuring key concepts and for more precisely targeting interventions in couple therapy.

Keywords: couple identity, divorce, group membership, relationship quality, relationship satisfaction, separation, we‐ness

INTRODUCTION

Couple relationships are of great significance because of their close links to functioning, wellbeing, and health. Most people describe the state of their intimate relationship as one of the most important determinants of their quality of life (Vaillant, 2008). Indeed, systematic reviews indicate that having a high‐quality marital or intimate partner relationship predicts mental health, physical health, and longevity (Kiecolt‐Glaser & Wilson, 2017; Robles et al., 2015). The quality of couple relationships not only contributes to the outcomes of adults but also predicts outcomes for their children across their lifespan (Halford et al., 2018).

Over the years, substantial research effort has gone into establishing the predictors of couple relationship quality. In particular, researchers have been concerned with identifying warning signs of a relationship in trouble many months or years before the relationship becomes unsatisfactory or unstable, as this enables better targeting of early intervention and support. This has led to a focus on the subjective, psychological closeness of the bond between the two partners (often called we‐ness, which is the term we will use here). Decades of research studies have found that we‐ness positively predicts concurrent satisfaction with, and commitment to, the relationship (Alea et al., 2015; Reid et al., 2006). We‐ness also negatively predicts the likelihood of subsequent decline in relationship quality and ultimately separation or divorce (Buehlman et al., 1992; Slattery et al., 2011). Furthermore, some evidence‐based couple therapies place a strong focus on developing a shared understanding of stresses impacting on the couple, and conjoint coping with such stresses (Falconier et al., 2015). Couple‐based interventions that focus on conjoint or communal coping have been found to enhance individual wellbeing (Fischer & Baucom, 2018), and help people to manage health crises like cancer, alcohol use, or chronic illness (Badr & Ahmad, 2020; Martire et al., 2010). Some of these interventions explicitly target couple we‐ness (e.g., Rohrbaugh, 2021; Scott et al., 2004). It might well be that a better understanding of we‐ness and how to increase it could further enhance outcomes of couples therapy.

Although there is strong evidence for this link between we‐ness and relationship quality, defining the nature of we‐ness and determining how it should be measured have proved more contentious. The terminology used by researchers differs, with terms including couple identity, mutuality, and relationship‐specific identification used semi‐interchangeably with we‐ness (e.g., Gildersleeve et al., 2017; Givertz et al., 2016; Linardatos & Lydon, 2011). In terms of measurement, several approaches have been taken, each of which overlaps with one another but have rarely been compared in the same study.

For example, one approach to measuring we‐ness that has had sustained interest has been one's attraction to, or liking of, one's partner (Montoya et al., 2018). Previous research has indicated that interpersonal attraction predicts behaviors such as smiling, eye contact, and proximity (Dainton et al., 1994; Montoya et al., 2018; Veksler & Eden, 2017).

Other researchers have measured we‐ness in the form of interdependence—the degree to which each member of the couple tends to define themselves in terms of their relationships more generally (e.g., Garrido & Acitelli, 1999, from Thibaut & Kelley, 1959). There are individual and cultural differences in the degree to which people are invested in and committed to social relationships as an important aspect of their self‐concept. This has been found to predict relationship commitment (Terzino & Cross, 2009) and costly sacrifice in the interests of the partnership (Day & Impett, 2018), as well as the likelihood of making a benign interpretation of a partner transgression (Linardatos & Lydon, 2011).

A third type of measure used to assess we‐ness are those that focus on the degree to which members of a couple see themselves as a collective entity, rather than two separate individuals. The Inclusion of Other in Self scale (IOS; Aron et al., 1992) has been widely used to capture this (Emery et al., 2021; Gächter et al., 2015; Hernandez et al., 2019; Walsh & Neff, 2018). This scale is a single‐item, visual representation of the couple that asks participants to choose a set of overlapping circles (from seven options) representing the degree of overlap between oneself and one's partner. Multi‐item measures have also been developed to capture this same idea of perceived closeness, but with superior psychometric properties (e.g., the Unidimensional Relationship Closeness scale [URCS]; Dibble et al., 2012). Consistent with this reasoning, researchers have commonly included both the URCS and the IOS as a conglomerate measure of we‐ness (e.g., Ahmad et al., 2017).

Perhaps most common, however, both in couple research and in couple therapy, have been measures of the degree to which members of a couple describe themselves in terms of the collective relationship (e.g. “us as a team”). This ‘we‐talk’ has most commonly been measured by coding the number of plural pronouns a person uses while talking or writing about their romantic relationship, as a proportion of total words (Karan et al., 2019), although self‐report scales have also been used (Topcu‐Uzer et al., 2021).

We believe that a key barrier to reaching consensus in the measurement of we‐ness and utilizing this construct more effectively in research and therapy practice is the limited theorizing about the nature of we‐ness, and indeed, how it fits with modern theorizing about social relationships more generally. The current study seeks to address this by drawing upon an influential framework for studying social relationships: the social identity approach.

A social identity approach to intimate partnerships

Meanwhile, developing in parallel to this body of couple research has been the social psychological science of social relationships, and of group‐based relationships in particular. Of particular relevance is the social identity approach, a meta‐theoretical framework encompassing social identity theory (Tajfel & Turner, 1979) and self‐categorization theory (Turner et al., 1987), as well as subsequent conceptual advances (e.g., Jetten et al., 2017; Reicher et al., 2012). At its heart, the fundamental principle of this framework extends the traditional idea of self‐concept, which focused on a person's sense of how they are distinctive and separate from others (e.g., personality traits and preferences). The extension is that people also define themselves in terms of their affiliation with, and similarity to, various social groups (e.g., as a mother, a lawyer, or a New Zealander).

Early work in the social identity tradition focused on intergroup relations (e.g., discrimination; stereotyping). In more recent work, there is an increasing emphasis on intragroup phenomena, such as schisms in groups (Sani, 2008), dynamics of trust and cooperation within groups (Cruwys, Greenaway, et al., 2020; Greenaway et al., 2015), and how group membership affects our wellbeing and functioning (Haslam et al., 2021; Wakefield et al., 2020). Recent evidence from randomized controlled trials suggests that clinical interventions that foster social identification are effective in enhancing individual psychological functioning (Cruwys et al., 2022; Steffens et al., 2021). These recent developments have particular relevance to the study of couple relationship quality. For instance, a series of studies led by Sani and colleagues speak to the critical protective capacity of social identification as a member of one's family. Family identification has been found to predict reduced depression and better physical health (Sani et al., 2012; Wakefield et al., 2016). In another example of how social identity processes are relevant to couple relationships, a large Swiss longitudinal study found that couples who coordinate their parenting identities (e.g., where one member of the couple identifies with a provider identity and the other with a domestic identity) had better wellbeing 2 years later (Turner‐Zwinkels & Spini, 2020).

The concepts of social identity and we‐ness are superficially similar. Indeed, some of the measures of we‐ness reviewed above have also been utilized to measure social identification. This is particularly true of we‐talk (e.g., Koschate et al., 2021) and the IOS (e.g., Swann et al., 2010). Widely used measures of social identification (e.g., the Multicomponent In‐Group Identification scale [MIGI]; Leach et al., 2008) also have overlapping content with we‐ness measures. For example, the MIGI includes the item “The fact that I am in this [relationship] is an important part of my identity” and the URCS includes the item “My relationship with my partner is important in my life”. However, no research has empirically investigated the degree to which we‐ness and social identity overlap. This is primarily due to the distinct disciplinary backgrounds from which these research traditions have emerged. As a result, social identity researchers have rarely explored the relevance of their theorizing to couple relationships, and couple researchers have rarely integrated theorizing that focuses on other kinds of social relationship. This means that the capacity of social identity theorizing to advancing our understanding of we‐ness has not been evaluated. Addressing this gap could suggest novel hypotheses and meaningfully inform couple interventions. For example, social identity processes have been found to predict attitude polarization, us‐them thinking, and conflict within groups (e.g., Colvin et al., 2020; Postmes et al., 2005). There is also a growing literature on predictors of social identification, such as coordinated action and signaling trust (Cruwys, Stevens, et al., 2020; Koudenburg et al., 2015; van Mourik Broekman et al., 2018). Reconceptualizing the couple as an emergent social identity may thus shed light on these phenomena where they occur within the dyad.

The current project seeks to address these lacunae by investigating the structure of we‐ness in couples, utilizing validated measures from both (a) couples research and (b) social identity research. In so doing, we seek to understand whether we‐ness can be equated with social identification as a couple (which would provide evidence for the relevance of social identity theorizing), and clarify the number and nature of constructs captured by widely used measures of we‐ness. Finally, we seek to investigate the degree to which the multiple facets of we‐ness predict relationship quality. Clarifying which aspects of we‐ness predict couple relationship quality could guide future enhancement of couple‐based interventions.

Hypotheses

The first aim of the study was to investigate the underlying factor structure of we‐ness as assessed by the measures most commonly used in the literature. In connection to this, we hypothesized that the primary factor would be couple identity (H1) as represented by items that measure the degree to which oneself and one's partner are perceived to be a collective or “team.” The second aim of the study was to investigate the predictive utility of the different facets of we‐ness for key indicators of relationship quality: relationship satisfaction and the likelihood of relationship dissolution. More specifically, we hypothesized that the couple identity factor would be most strongly associated with relationship quality (H2), relative to other components of we‐ness.

METHOD

Participants and design

People were eligible to complete the study if they were aged 18 years or over and in a romantic relationship lasting at least 1 year. Recruitment was conducted via Amazon Mechanical Turk (MTurk), which is an online platform where registered users complete brief tasks in exchange for a small fee. We used a best‐practice screening procedure to ensure that all participants met eligibility criteria. Chandler and Paolacci (2017) found that screening questionnaires that cover several unrelated domains are ideal, as this reduces the likelihood that respondents are incentivized to deceive researchers to gain access to the full paid study. Specifically, all adult MTurk workers were invited to complete a very brief “health screener” that paid USD$0.03 for answering six screening questions. These questions asked about participants' age, gender, relationship status (“Are you in a romantic relationship at the current time?”), length of relationship (“How long have you continuously been in your current romantic relationship?”), and two questions about their history of incarceration (screening items for an unrelated study, Kyprianides, et al., 2019). A total of 3832 people completed the screening questionnaire, of whom 2505 met our eligibility criteria. The current study (titled “Couple relationships, personality, and wellbeing”) was subsequently only made available through MTurk to this subset of people, to ensure that only eligible participants viewed the advertisement for the study.

A recruitment target of 400 participants was set, to exceed standard recommendations for adequate statistical power for EFA (Hoe, 2008; Maccallum & Austin, 2000). Four hundred and thirteen respondents commenced the survey, which was closed once our target was reached. Of these, 19 were excluded who were missing data on more than 30% of the measures of interest, and an additional 19 were excluded for failing an attention check (described below). Therefore, the final sample for analyses was 375. Participants were compensated USD$1.50 for completing the survey. Participants provided informed consent and all materials and procedures were approved by the Human Research Ethics Committee at the University of Queensland. Deidentified data are available on reasonable request.

Measures

The order of measures was randomized, except for demographics that were completed last by all participants. The survey was conducted in English.

We‐ness

Seven distinct measures of we‐ness were included that have been previously used in the literature, corresponding to the constructs described in the introduction: perception of the couple as a collective, describing the partnership in collective terms, partner attraction or liking, and interdependence. The specific measures were as follows:

Social identification with partner

The MIGI (Leach et al., 2008) was used to measure to degree to which participants perceived their relationship with their partner to form an emergent social identity. Twelve items from the original 14‐item scale were included in the survey, with two not used due to poor fit for the couple context (from the in‐group self‐stereotyping subscale, e.g., “I am similar to the average [in‐group] person”). This scale includes items such as “I feel solidarity with my partner” measured on a seven‐point scale (1 = strongly disagree, 7 = strongly agree), α = 0.95. Substantial evidence has been published for the validity and reliability of this scale in the study of diverse social groups (see Postmes et al., 2013 for a review), and it is most commonly utilized as a single construct rather than as five subscales.

Social identification as a partnered person

A separate social identification scale was used to assess participants' perception of themselves as a person in a romantic relationship. Although this is a relatively nuanced distinction from the preceding scale, we theorized (along the lines of Acitelli et al., 1999; Fisher & Sakaluk, 2019) that it is possible for one's identity to be defined less in terms of one particular romantic relationship and more in terms of the state of being partnered (as opposed to single). The widely used and well‐validated Four Item Social Identification scale (Doosje et al., 1995; Postmes et al., 2013) was used, which includes items such as “I identify with other people in romantic relationships” and is measured on a seven point scale (1 = strongly disagree, 7 = strongly agree), α = 0.77.

Inclusion of Other in the Self

The IOS (Aron et al., 1992) was included, which is a single‐item, visual scale that asks participants to choose a set of overlapping circles to indicate the degree of overlap between oneself and one's partner on a seven‐point visual scale. In addition to the original version, here we also included a second item, more commonly used in group process research (Tropp & Wright, 2001), which asks about the degree of overlap between oneself and the in‐group (in this case, the couple relationship). This was to capture the possible conceptual difference between perceived overlap with one's partner as an individual, versus overlap with the collective partnership comprised of both oneself and one's partner. The items were strongly associated with one another, r = 0.70, p < 0.001.

Unidimensional Relationship Closeness

The URCS (Dibble et al., 2012) is an 11‐item measure of perceived closeness with one's partner including items such as “My partner and I have a strong connection” rated on a seven‐point scale (1 = strongly disagree, 7 = strongly agree). As in previous studies, we found that the URCS had excellent reliability, α = 0.95.

Relational‐Interdependent Self‐Construal

The Relational‐Interdependent Self‐Construal Scale (RISC; Cross et al., 2000) was included to measure the degree to which participants saw their social relationships (in general) as a central part of their self‐concept. This 11‐item scale designed to measure interdependence includes items such as “My close relationships are an important reflection of who I am” measured on a seven‐point scale (1 = strongly disagree, 7 = strongly agree), α = 0.88.

Interpersonal attraction

Interpersonal attraction, or the degree to which a person likes their partner as an individual person, was measured using Rubin's (1970) 13‐item Liking scale. This scale includes items such as “[My partner] is the sort of person whom I myself would like to be” measured on a seven‐point scale (1 = not at all true, 7 = definitely true), α = 0.93.

We‐talk

Following previous research, we gave participants a free‐response writing task adapted from Agnew et al. (1998) with the following prompt “Please share some of your thoughts concerning your relationship. Please write any thoughts you have – they can be positive or negative. For each thought, we ask that you write a complete sentence. You can write as many or as few thoughts as you'd care to. Keep in mind there are no right or wrong answers.” Participants completed this task for a minimum of 1 min and a maximum of 10 min and wrote a mean of 57.13 words (SD = 44.33) Using the Linguistic Inquiry and Word Count method (LIWC; Tausczik & Pennebaker, 2010), we‐talk was calculated as the number of plural pronouns as a proportion of total words.

Relationship quality

Two measures of relationship quality were included:

Relationship satisfaction

The Couple Satisfaction Index was used to measure relationship satisfaction (CSI; Funk & Rogge, 2007). The CSI is widely used due to its excellent psychometric qualities (α = 0.98). The 16‐item CSI includes items such as “How well does your partner meet your needs?” measured on a seven‐point scale from not at all true (1) to completely true (7).

Relationship instability

The (in)stability of each participants' relationship was assessed using the 11‐item Relationship Status Inventory (Slattery et al., 2011). This modern adaptation of the Marital Status Inventory (Weiss & Cerreto, 1980) assesses “dissolution potential,” or the number of steps taken toward separation. This scale included items such as “Thoughts of separation or ending my relationship occur to me very frequently, as often as once a week or more,” with response options of false (0) and true (1). Each item endorsed by participant counts toward a total relationship instability score from 0 to 11.1

Other variables

Social desirability bias

A 10‐item social desirability scale (Strahan & Gerbasi, 1972) was included to assess individual differences in the degree to which participants tended to portray themselves positively. This scale includes items such as “I have never intensely disliked anyone” with two response options (1 = false, 2 = true). A sum of the items provides an indication of social desirability bias for each participant (α = 0.66). The reliability of this measure was below conventional guidelines; however, this has been found previously (Thompson & Phua, 2005) and has been attributed to the (intentional) lack of face validity of this measure.

Attention checks

Consistent with best practice for online survey studies (Meade & Craig, 2012; Paolacci et al., 2010), three attention check items were included throughout the study. Each of these asked participants to provide a particular response (e.g. “Please answer ‘strongly disagree’ so that we know you are paying attention”). Respondents who incorrectly answered more than one of these items were excluded prior to analyses. In addition, one item asked, “In your honest opinion, should we use your data in our analyses of this study?” (Yes or No). Respondents could optionally provide a reason for their answer. All people who responded ‘no’ were excluded prior to analyses.

Demographics

Participants also provided their age, gender, gender of partner, relationship status, cohabitation status, whether they were raising children with their partner, as well as their before‐tax household income.

Analysis plan

H1 was assessed using an exploratory factor analysis (EFA; using principal axis factoring, de Winter & Dodou, 2012) to empirically derive the best model for conceptualizing we‐ness. The EFA was refined using communalities, eigenvalues, scree plots, and the interpretability of the factors (Reise et al., 2000; Watkins, 2018). Factors that were purely attributable to measurement artifacts (e.g., contained only reverse‐scored items) were not deemed sufficiently meaningful to retain in the final solution (Floyd & Widaman, 1995; Moosbrugger & Hartig, 2002). Once the number of factors was finalized, a varimax rotation was used to assess factor weightings. Although we acknowledged the likely overlap between facets of we‐ness, H1 was concerned with establishing the number of non‐redundant constructs being captured by typical measures of ‘we‐ness’, a task to which oblimin rotation is ill‐suited. Finally, any items which loaded onto multiple factors or did not load onto any factors were removed. The cut‐off used to determine loadings was 0.45, chosen as a stringent criterion (Tabachnick & Fidell, 2019).

H2 was assessed using the factor scores from the EFA‐derived solution as predictors in two linear regression analyses, each controlling for demographic characteristics and social desirability.

RESULTS

Table 1 provides descriptive statistics and bivariate correlations. The sample of 375 included 115 men (31.2%) and 254 women (68.8%) with no respondents indicating other gender. Participants ranged in age from 18 to 74 (M = 37.22; SD = 12.00) and all were in a romantic relationship lasting at least 1 year. The majority of participants were in a relationship with a person of the opposite sex (92.3%) and lived with their romantic partner (81.8%), and 50.4% of participants were raising children with their romantic partner. Our sample were diverse in terms of income, with more than 10% of participants in each of the six income bracket categories, such that 13.4% had an annual household before tax of less than USD20,000, and 15.3% of the sample had an annual household income before tax of greater than USD100,000. There was also evidence that our sample was diverse in terms of “we‐ness” and relationship quality, with the full range of each item and scale being used. Based on the standard cut‐off on the relationship status inventory (Slattery et al., 2011), over a quarter of participants were classified as being in an unstable relationship.

TABLE 1.

Bivariate Correlations

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
Demographics and covariates
1. Age —
2. Gender (1 = male; 2 = female) 0.05 —
3. Mixed‐sex relationship (0 = No; 1 = Yes) −0.02 −0.09 —
4. Cohabiting (0 = No; 1 = Yes) 0.13* 0.03 −0.03 —
5. Raising children together (0 = No; 1 = Yes) 0.02 0.08 0.09 0.32* —
6. Household income 0.12* −0.07 0.07 0.14* 0.12* —
7. Social desirability bias 0.15* 0.08 −0.05 0.03 0.05 −0.10 —
“We‐ness” factor scores
8. Couple identity −0.03 0.00 0.07 0.08 0.01 −0.02 −0.04 —
9. Partner liking 0.13* −0.05 −0.04 −0.01 −0.01 0.07 0.11* 0.04 —
10. Relationship orientation −0.01 −0.04 0.01 −0.01 −0.07 −0.03 0.07 0.01 0.02 —
11. Partner similarity −0.05 −0.06 −0.02 −0.02 −0.03 −0.11* 0.07 0.04 0.05 0.05 —
Relationship quality
12. Satisfaction 0.00 −0.08 0.01 0.04 −0.04 0.01 0.10 0.74* 0.40* 0.26* 0.33* —
13. Instability −0.07 0.05 0.00 −0.02 −0.01 −0.09 −0.11* −0.47* −0.32* −0.12* −0.14* −0.60*
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*

p < 0.05.

Exploratory factor analysis

The initial exploratory factor analysis with all 54 we‐ness items yielded 9 factors with eigenvalues greater than one, which together explained a total of 70% of the variance. The Kaiser‐Meyer‐Olkin (KMO) measure of sampling adequacy suggested that the data were suitable for factor analysis (0.954), as did Barlett's Test of Sphericity, χ 2(1431) = 17,122.76, p < 0.001. The communalities indicated strong associations between all items (all >0.45initial) except for we‐talk (0.12initial and 0.05extracted). Inspection of the scree‐plot suggested that a four‐ or five‐factor solution was most appropriate. In the rotated factor solution, factors 6, 7, 8, and 9 consisted solely of low‐loading items, cross‐loaded items, or negatively worded items (i.e., measurement artifacts). Therefore, the factor analysis was repeated with (a) we‐talk removed and (b) a limit of five factors specified. This solution also had strong indicators that factor analysis was appropriate (KMO = 0.954; Barlett's χ 2[1378] = 17,343.15, p < 0.001) and explained 63% of the variance. Inspection of the rotated factor matrix indicated that the fifth factor was still capturing measurement artifact and so the factor analysis was then restricted to a four‐factor solution explaining 59% of the variance.

This four‐factor solution (KMO = 0.954; Barlett's χ 2[1378] = 17,358.58, p < 0.001) yielded four clearly interpretable factors (described below). We then removed one cross‐loading item (>0.45 on two factors) and five items which did not load highly (>0.45) onto any factor. This final factor solution thus contained 47 items and showed good suitability for factor analysis (KMO = 0.955; Barlett's χ 2[1081] = 15,838.36, p < 0.001, α = 0.97) and explained 63% of the variance. We labeled the four factors: couple identity (e.g., “I feel a bond with my partner”, α = 0.96), partner liking (e.g. “I think that [my partner] is one of those people who quickly wins respect”, α = 0.93), relationship orientation (e.g., “In general, my close relationships are an important part of my self‐image”, α = 0.91), and partner similarity (e.g., “My partner and I are very similar to one another”, α = 0.79). Table 2 presents the rotated factor loadings for each item, along with mean and standard deviation for each item, details of the items removed from the final factor solution, and item‐total correlation for the items comprising each factor score.

TABLE 2.

Final factor solution used in the full sample to derive the four factors of “we‐ness”. Based on 47 items taken from seven previously validated scales

Item Mean (standard deviation) Range of responses Factor 1 Couple identity (25 items) Factor 2 Partner liking (10 items) Factor 3 Relationship orientation (9 items) Factor 4 Partner similarity (3 items) Item‐total correlation a
IDwithpartner4. I am glad to be in my romantic relationship 6.26 (1.17) 1–7 0.843 0.321 0.133 0.010 0.864
URCS4. My partner and I have a strong connection 6.12 (1.22) 1–7 0.825 0.245 0.157 0.212 0.880
IDwithpartner7. Being in my romantic relationship gives me a good feeling 6.09 (1.25) 1–7 0.809 0.292 0.194 0.067 0.849
URCS1 My relationship with my partner is close 6.12 (1.16) 1–7 0.801 0.220 0.182 0.226 0.859
IDwithpartner1. I feel a bond with my partner 6.25 (1.07) 1–7 0.800 0.220 0.135 0.138 0.826
IDwithpartner6. It is pleasant to be in my romantic relationship 6.07 (1.28) 1–7 0.780 0.307 0.170 0.112 0.834
URCS5. My partner and I want to spend time together 6.17 (1.15) 1–7 0.774 0.294 0.105 0.267 0.855
IDaspartnered3. I am glad to be in a romantic relationship 6.22 (1.12) 1–7 0.766 0.339 0.097 0.033 0.805
IDwithpartner3. I feel committed to my romantic relationship 6.43 (0.92) 1–7 0.761 0.252 0.071 0.066 0.771
URCS10. My relationship with my partner is important in my life 6.35 (0.96) 1–7 0.758 0.307 0.150 0.069 0.802
URCS6. My partner is a priority in my life 6.19 (1.11) 1–7 0.749 0.280 0.151 0.198 0.828
URCS7. My partner and I do a lot of things together 5.90 (1.33) 1–7 0.705 0.148 0.228 0.206 0.769
URCS3. My partner and I disclose important personal things to each other 6.21 (1.08) 1–7 0.704 0.126 0.102 0.127 0.704
IDwithpartner5. I think that my partner and I have a lot to be proud of 6.17 (1.15) 1–7 0.694 0.288 0.203 0.001 0.738
URCS8. When I have free time I choose to spend it alone with my partner 5.75 (1.28) 1–7 0.643 0.237 0.128 0.299 0.737
URCS9. I think about my partner a lot 6.02 (1.09) 1–7 0.641 0.214 0.248 0.205 0.728
URCS11. I consider my partner when making important decisions 6.38 (0.92) 1–7 0.634 0.299 0.094 0.062 0.681
UCRS2. When we are apart, I miss my partner a great deal 5.84 (1.34) 1–7 0.616 0.185 0.173 0.261 0.689
IDwithpartner8. I often think about my romantic relationship 5.92 (1.19) 1–7 0.595 0.240 0.220 0.070 0.655
IDwithpartner2. I feel solidarity with my partner 6.11 (1.11) 1–7 0.593 0.323 0.217 0.178 0.704
IDaspartnered2. I see myself as someone in a romantic relationship 6.10 (1.19) 1–7 0.582 0.140 0.120 0.080 0.595
IOS: Degree of self/partner overlap 5.23 (1.53) 1–7 0.557 0.242 0.072 0.399 0.688
IDwithpartner9. My romantic relationship is an important part of my identity 5.77 (1.36) 1–7 0.546 0.131 0.378 0.381 0.700
IDwithpartner10. My romantic relationship is an important part of how I see myself 5.65 (1.34) 1–7 0.527 0.144 0.417 0.334 0.684
IOS: Degree of self/relationship overlap 5.17 (1.54) 1–7 0.474 0.115 0.152 0.297 0.578
Attraction9. I think that _____ is one of those people who quickly wins respect 7.35 (1.73) 1–9 0.250 0.790 0.174 0.094 0.800
Attraction13. It seems to me that is very easy for _____ to gain admiration 7.16 (1.78) 1–9 0.216 0.720 0.224 0.190 0.763
Attraction8. I would vote for _____ in a class or group election. 7.23 (2.08) 1–9 0.239 0.679 0.164 0.123 0.716
Attraction6. Most people would react very favorably to _____ after a brief acquaintance 7.61 (1.60) 1–9 0.292 0.664 0.151 0.027 0.692
Attraction5. I have great confidence in _____'s good judgment 7.36 (1.78) 1–9 0.399 0.659 0.204 0.215 0.800
Attraction4. In my opinion, ______ is an exceptionally mature person 7.07 (2.04) 1–9 0.374 0.647 0.196 0.219 0.779
Attraction11. _____ is one of the most likeable people I know 7.46 (1.79) 1–9 0.324 0.636 0.150 0.165 0.707
Attraction10. I feel that _____ is an extremely intelligent person 7.70 (1.61) 1–9 0.337 0.625 0.070 0.092 0.683
Attraction12. _____ is the sort of person whom I myself would like to be 6.56 (2.19) 1–9 0.331 0.593 0.264 0.416 0.753
Attraction3. I would highly recommend ______ for a responsible job 7.83 (1.68) 1–9 0.237 0.563 0.145 0.030 0.606
RISC7. In general, my close relationships are an important part of my self‐image 5.27 (1.42) 1–7 0.146 0.126 0.816 0.162 0.806
RISC4. I think one of the most important parts of who I am can be captured by looking at my close friends and understanding who they are 5.17 (1.42) 1–7 0.101 0.163 0.784 0.090 0.768
RISC5. When I think of myself, I often think of my close friends and family also 5.31 (1.40) 1–7 0.177 0.116 0.736 0.044 0.729
RISC2. When I feel very close to someone, it often feels to me like that person is an important part of who I am 5.63 (1.21) 1–7 0.210 0.168 0.735 0.148 0.745
RISC11. When I establish a close friendship with someone, I usually develop a strong sense of identification with that person 5.18 (1.31) 1–7 0.089 0.100 0.711 0.089 0.692
RISC10. My sense of pride comes from knowing who I have as close friends 4.63 (1.57) 1–7 −0.003 0.080 0.676 0.124 0.643
RISC1. My close relationships are an important reflection of who I am 5.69 (1.19) 1–7 0.245 0.186 0.675 0.108 0.699
RISC3. I usually feel a strong sense of pride when someone close to me has an important accomplishment 5.93 (1.05) 1–7 0.288 0.153 0.597 −0.041 0.625
RISC6. If a person hurts someone close to me, I feel personally hurt as well 5.68 (1.26) 1–7 0.117 0.133 0.526 0.033 0.539
Attraction7. I think that _____ and I are quite similar to each other 6.33 (2.24) 1–9 0.286 0.320 0.213 0.650 0.714
IDwithpartner12. My partner and I are very similar to each other 5.22 (1.60) 1–7 0.405 0.234 0.298 0.582 0.711
Attraction1. When I am with ______, we are almost always in the same mood 5.93 (2.02) 1–9 0.257 0.213 0.157 0.462 0.518
Items excluded from the final factor solution Mean (standard deviation) Range of responses Reason removed
We‐talk 0.04 (0.04) 0–0.17 Communalities indicated poor correlation with other items
IDwithpartner11. My partner and I have a lot in common with each other 5.55 (1.34) 1–7 Cross‐loading on Factors 1 and 4
IDaspartnered1. I identify with other people in romantic relationships 5.11 (1.37) 1–7 Did not load onto any factor at 0.45 or greater
IDaspartnered4.I feel strong ties with others who are in a romantic relationship 4.94 (1.50) 1–7 Did not load onto any factor at 0.45 or greater
RISC8. Overall, my close relationships have very little to do with how I feel about myself 3.42 (1.92) 1–7 Did not load onto any factor at 0.45 or greater
RISC9. My close relationships are unimportant to my sense of what kind of person I am 3.13 (1.87) 1–7 Did not load onto any factor at 0.45 or greater
Attraction2. I think that _______ is unusually well‐adjusted 6.03 (2.21) 1–9 Did not load onto any factor at 0.45 or greater
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Note: Bold indicated factor to which each item was assigned (loading >0.45). N = 375.

Abbreviations: IOS, Inclusion of Other in Self scale; RISC, Relational‐Interdependent Self‐Construal Scale; URCS, Unidimensional Relationship Closeness Scale; IDwithpartner, Social identification with one's partner (Multicomponent In‐Group Identification scale); IDaspartnered, Social identification as a partnered person (Four Item Social Identification scale).

a

Item‐total correlation represents the correlation between each item and the sum of all other items included in the factor (1–4) to which it was assigned.

Predicting relationship quality

We specified two predictive regression models to evaluate H2. Table 3 provides full model statistics. In each case, Step 1 included demographics: gender, age, whether the couple were raising children together (yes/no), household income, and whether the relationship was mixed‐sex or same‐sex. Step 2 included social desirability to partial out variance (in either the independent or dependent variables) that was driven by impression management or demand characteristics, rather than features of the relationship itself. Step 3 included the four‐factor scores representing the unique facets of we‐ness as identified in the final factor solution: couple identity, partner liking, relationship orientation, and partner similarity.2

TABLE 3.

Linear regression models predicting relationship quality

Relationship satisfaction Relationship instability
β SE p β SE p
Step 1
Age 0.01 0.08 0.860 −0.06 0.01 0.265
Gender −0.08 2.00 0.121 0.06 0.28 0.290
Raising kids −0.04 1.86 0.499 0.00 0.26 0.935
Household income 0.00 0.29 0.942 −0.08 0.04 0.140
Mixed‐sex relationship 0.00 3.44 0.987 0.01 0.47 0.824
Step 2
Social desirability 0.12 0.42 0.026 −0.13 0.06 0.018
Step 3
Couple identity 0.71 0.41 <0.001 −0.46 0.11 <0.001
Partner liking 0.34 0.43 <0.001 −0.27 0.11 <0.001
Relationship orientation 0.23 0.42 <0.001 −0.10 0.11 0.021
Partner similarity 0.27 0.46 <0.001 −0.10 0.12 0.017
Open in a new tab

Note: Coefficients are provided for the step at which each variable was entered into the model.

Bolding indicated significant effects.

Relationship satisfaction

In predicting relationship satisfaction, Step 1 of the model was non‐significant, F(5, 361) = 0.63, p = 0.680, R 2 = 0.009. None of the five covariates significantly predicted relationship satisfaction. Step 2 significantly improved the model, F change(1, 360) = 5.02, p = 0.026, Rchange2 = 0.014. Social desirability bias was positively associated with reported relationship satisfaction, rsemi partial2 = 0.013, p = 0.026, but this was a small effect size. The addition of Step 3 significantly improved the model, F change(4, 356) = 377.57, p < 0.001, Rchange2 = 0.791. Each of the four we‐ness factors was a significant independent predictor of relationship satisfaction, with couple identity the strongest predictor with a large effect size (rsemi partial2 = 0.501, p < 0.001), followed by partner liking with a medium effect size (rsemi partial2 = 0.110, p < 0.001), and small effect sizes for partner similarity (rsemi partial2 = 0.069, p < 0.001), and relationship orientation (rsemi partial2 = 0.050, p < 0.001).

Relationship instability

In predicting relationship instability, Step 1 of the model was non‐significant, F(5, 361) = 1.04, p = 0.396. None of the five covariates significantly predicted relationship instability. Step 2 significantly improved the model, F change(1, 360) = 5.61, p = 0.018, Rchange2 = 0.015. Social desirability bias was negatively associated with reported relationship instability, rsemi partial2 = 0.015, p = 0.018, but this was a small effect size. The addition of Step 3 significantly improved the model, F change(4, 356) = 44.40, p < 0.001, Rchange2 = 0.323. All four we‐ness factors were significant negative predictors of relationship instability. Couple identity was the strongest predictor with a medium to large effect size (rsemi partial2 = 0.211, p < 0.001), followed by partner liking with a medium effect size (rsemi partial2 = 0.069, p < 0.001), and small effect sizes for partner similarity (rsemi partial2 = 0.011, p = 0.017) and relationship orientation (rsemi partial2 = 0.010, p = 0.021).

DISCUSSION

This study sought to advance couple research by (a) providing insights into the underlying structure of the concept of we‐ness, informed by the social identity approach, and (b) establishing the utility of each facet of we‐ness for predicting relationship quality. The results indicated that the diverse ways in which we‐ness have been measured in the literature are, indeed, capturing several distinct concepts. However, these concepts did not map directly onto the seven distinct scales from which these measures were drawn. Instead, a four‐factor solution was derived, which incorporated the facets of couple identity, partner liking, relationship orientation, and partner similarity. The first factor (couple identity), onto which 54% of the we‐ness items loaded, accounted for much of the variance. Couple identity was comprised of items from four different measures. Of the existing complete scales, unidimensional relationship closeness and social identification with one's partner best captured this construct. The second and third factors each included items from a sole scale: partner liking was comprised of a subset of the interpersonal attraction items, and relationship orientation was comprised of a subset of the relational interdependent self‐construal items. Finally, the fourth factor of partner similarity comprised only three items from two scales (interpersonal attraction and social identification with one's partner).

The second major finding of the present study was that each of the four we‐ness factors was independently associated with relationship quality (i.e., satisfaction and instability). Couple identity and then partner liking were the strongest predictors in each regression model, while relationship orientation and partner similarity were much weaker predictors in both models. The take‐away message from this, then, is that there is likely utility in measuring all four aspects of we‐ness because each was independently related to relationship quality outcomes. Where this is not feasible, a unitary measure of couple identity is the highest priority, and partner liking is the next most important priority, for inclusion in future research.

Unexpectedly, we‐talk, which is one of the most common ways of calculating ‘we‐ness’ in the literature, was excluded from the factor analysis because it did not load on any of the factors. Moreover, we‐talk was only weakly associated with relationship instability (r = −0.16, p = 0.002) and not related to relationship satisfaction (r = 0.04, p = 0.466). The mean rate of the use of ‘we’ was quite low in the writing samples we elicited, and many respondents did not use ‘we’ at all. This truncated range of scores might have limited the usefulness of this measure. It is possible that a longer sample of text and/or eliciting verbal rather than written samples might elicit higher rates of “we” pronouns and a more reliable measure. We‐talk appears to be more strongly related to relationship quality in couples in which one spouse had a major health problem (e.g., Rentscher et al., 2017), so perhaps we‐talk might be more predictive when couples are discussing impactful conjoint stressors. On the other hand, the finding of a weak or absent relationship between measures of we‐talk and relationship quality is consistent with the findings of a recent meta‐analysis, which found an r of only 0.08 across 30 studies (Karan et al., 2019), suggesting that we‐talk may not warrant its position as the most commonly used measure of we‐ness. Six other items were also excluded from the final factor solution. While the reasons for exclusion differed, four of these items focused on a person's social identification as a partnered person or with other partnered persons. This concept is perhaps insufficiently distinct from the other facets of we‐ness.

Implications

The current study has implications both for theoretical models of self and social relationships as well as practical implications for research and interventions with couples. First, this study provided the strongest evidence to date that couple we‐ness can be conceptualized as a social identity. It follows, then, that decades of theorizing and thousands of empirical studies in the social identity tradition may have relevance to the study of intimate partnerships. For example, the finding that social identities protect against depression (Cruwys et al., 2013, 2014) may be relevant to understanding the depression risk posed by separation and divorce (Stack & Scourfield, 2015). Similarly, social identity research on when people choose to leave dysfunctional social groups versus recommit to improving the group “from the inside” may advance our understanding of when people choose to work through threats to their romantic relationship versus end the relationship (Anvari et al., 2019). Perhaps most crucially, there is emerging evidence that psychotherapeutic interventions that build social identity benefit both social relationships and wellbeing (Cruwys et al., 2022; Steffens et al., 2021). This warrants further research to investigate potential benefits of integrating social identity principles into best‐practice couple therapy. This might include, for example, activities that are used in evidence‐based social identity interventions, such as social identity mapping (Cruwys et al., 2016) or practical steps to manage unsupportive social ties (e.g., Haslam et al., 2016).

Second, this study has implications for how groups are conceptualized in the field of social identity. Researchers have tended implicitly to define a group as containing more than two people. However, this has not been logically or empirically justified, and conceptually there is every reason to anticipate that the subjective, psychological perception of a collective “team” can emerge between only two people (see Cruwys, Stevens, et al., 2020 for similar reasoning). Indeed, many argue that the couple relationship is, for most partnered people, their most significant social relationship in terms of its impact on individual wellbeing (e.g., Fincham et al., 2018). The potential role of group processes in romantic partnerships would signal a major expansion of the relevance of social identity theorizing, but this remains to be fully explored.

The evidence presented here that couple relationships can be fruitfully conceptualized as social identities opens up several avenues for future research. In particular, it suggests novel hypotheses that warrant empirical investigation. For example, determinants of (group‐based) social identities may be relevant to the development and maintenance of couple identity. Recent research has suggested that when members of group perform complementary roles (e.g., singing different parts in a choir), this leads to a stronger emergent sense of shared identity than when all group members perform interchangeable roles (e.g. singing in unison; Koudenburg et al., 2015; van Mourik Broekman et al., 2018). The relatively weak contribution of partner similarity in predicting relationship quality hints at this possibility in the couple context; however, future research is needed to explore this.

Strengths and limitations

This study had several strengths, including its rigorous methodology, recruitment methods and screening strategy. All of the seven distinct measures of we‐ness were selected due to their superior psychometric properties (e.g., all αs > 0.77) and widespread use, although in some cases (e.g., the MIGI), their use has been more widespread outside of couple research. This study was not advertised as research on relationship quality or couples, but instead, participants completed a generic screener questionnaire and were only referred to the study if they met inclusion criteria. This meant that the sample was likely to be more representative of actual romantic relationships, rather than biased toward couples with either very high or very low satisfaction, who might take a particular interest in the topic. However, one weakness of the recruitment approach is that we did not assess both members of each couple, which is generally considered best practice in couple research. This is perhaps less crucial to our investigation of the structure of we‐ness (H1) but potentially undermined the accuracy of our estimation of the relationship quality indicators (e.g., leading to an underestimation of relationship instability) and thus affected H2. Similarly, the correlational design was not problematic for the assessment of H1, but the cross‐sectional evaluation of H2 cannot shed light on the causal direction of these relationships.

CONCLUSIONS

Decades of couple research have led to a growing focus on we‐ness as a critical determinant of relationship quality. However, ongoing debates about the best terminology, definition, and measurement of this concept have stifled progress. We sought to advance the field by evaluating the factor structure of we‐ness drawing measures from both couple research and social identity theorizing. Findings indicated that there are multiple facets of we‐ness, with couple identity (the degree to which the romantic relationship is central to one's sense of self) being the most important. Couple identity not only explained the majority of the variance across seven measures of we‐ness but was also most strongly associated with relationship satisfaction and relationship instability. Ultimately, this research not only sheds light on what we‐ness is and the best way to measure it but also bolsters the evidence for its role as a central variable in couple research and couples therapy.

ACKNOWLEDGMENTS

The first author is supported by a National Health and Medical Research Council Australia Emerging Leadership Fellowship #1173270. The School of Psychology, University of Queensland provided support for the costs of the research. Open access publishing facilitated by Australian National University, as part of the Wiley ‐ Australian National University agreement via the Council of Australian University Librarians.

Cruwys, T. , South, E. I. , Halford, W. K. , Murray, J. A. , & Fladerer, M. P. (2023). Measuring “we‐ness” in couple relationships: A social identity approach. Family Process, 62, 795–817. 10.1111/famp.12811

Footnotes

1

Some previous researchers (e.g., Slattery et al., 2011) have dichotomized this variable such that any participant with a score above 4 was categorized as having an unstable relationship at risk of dissolution. We treated relationship stability as a continuous variable for our primary analyses in the manuscript in order to maximize power. However, a sensitivity analysis that repeated the test of H2 using the dichotomized version of this measure (in a binary logistic regression) replicated the results reported in text, although partner similarity was only a marginal predictor of relationship instability in that model (p = 0.052).

2

To ensure that our dependent variables (relationship satisfaction and relationship instability) were distinct constructs from the predictor variables (the four facets of we‐ness), an additional EFA was conducted using the same criteria which also included the items comprising the two dependent variables. This yielded a six‐factor solution in which the same four factors described in text were identified (factors 1, 3, 4, and 6). The second factor included 15 out of 16 items from the couple satisfaction index, as well as 3 items from the relationship status inventory (i.e., all relationship quality indicators). The fifth factor included three items from the relationship status inventory (only). The remaining relationship quality items did not load on any factor, and poor communalities suggested that these items were, indeed, only weakly associated with other items in the EFA and thus conceptually distinct. We concluded from this that relationship quality was sufficiently distinct from we‐ness in order to test H2.

DATA AVAILABILITY STATEMENT

Data and materials will be made available upon reasonable request. The study was not pre‐registered.

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Data Availability Statement

Data and materials will be made available upon reasonable request. The study was not pre‐registered.

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