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Abstract

Intimate partner violence (IPV) results in many adverse effects on physical, mental, and societal well-being. Research has highlighted the significance of implementing interventions that encourage effective communication and resolution of IPV within couples. Accurately identifying and distinguishing between different forms of IPV is crucial. To recognize different types of IPV, it is important to use reliable IPV screening tools and ensure that both partners share a common understanding of the constructs being assessed. Hence, examining measurement invariance (MI) within a dyadic framework is necessary to establish a reliable tool for screening IPV. The current study examined the dyadic MI of the 10-item Continuum of Conflict and Control Relationship Scale (CCC-RS-10). The CCC-RS-10 measures an individual’s sense of physical and reactive aggression, psychological and controlling aggression, and relationship conflict. Utilizing a sample of 613 opposite-gendered couples, we tested MI using confirmatory factor analysis within a dyadic framework. The CCC-RS-10’s three-factor model supported configurational, metric, scalar, and residual invariance. Men reported a higher mean of psychological and controlling aggression (d = 0.31) and a lower mean of relationship conflict (d = 0.23) than women. The functioning of the CCC-RS-10 and the meaning attributed to the conflict and control relationship were the same for both partners. Hence, the variation in the level of the CCC-RS-10 scale between dyad partners most likely reflects actual gender differences rather than the instrument measuring different constructs in the two groups. We discuss implications for practice and research.

Keywords

measurement invariance dyadic data intimate partner violence couples relationships

The Centers for Disease Control and Prevention defines intimate partner violence (IPV) as physical, sexual, or psychological abuse inflicted by a current or former intimate partner (Centers for Disease Control and Prevention [CDCP], 2021). The National Coalition Against Domestic Violence reports that a partner’s violence affects one in three women and one in four men in their lifetime, with around 20 Americans being physically abused by an intimate partner every minute (National Coalition Against Domestic Violence [NCADV], 2020). These statistics emphasize the pervasiveness and urgency of this public health concern.

The effects of IPV yield many negative physical, mental, and societal consequences (Arias & Ikeda, 2006; CDCP, 2021; Coker et al., 2002). Physical health effects can include injuries and the onset of chronic health problems (CDCP, 2021; Coker et al., 2002). Victims and survivors are also likely to suffer from mental health conditions such as depression, anxiety, substance abuse, decreased self-esteem, post-traumatic stress disorder (PTSD), and suicidality (Coker et al., 2002; Lagdon et al., 2014; Sanderson, 2009). Moreover, IPV occurring between partners has an impact on the entire family unit (Stith et al., 2008; Walker-Descartes et al., 2021). IPV, together with the observation of unhealthy conflict resolution, has a detrimental impact on the mental and physical health of children from such relationships. These children are more likely to engage in IPV relationships themselves (Arias & Ikeda, 2006; Gordon et al., 2022; Hammett et al., 2020; Stith et al., 2000, 2003).

Several researchers have emphasized the importance of implementing and promoting interventions, education, and programs that focus on the dyadic nature of IPV and promote communication and conflict resolution among couples (Ahmadabadi et al., 2020; Dunkle et al., 2020; Hammett et al., 2020; Li et al., 2010; Wheeler et al., 2022). According to Ahmadabadi et al. (2020) and Jaspaert and Vervaeke (2014), IPV takes on a bidirectional nature, indicating that both men and women can be victims and perpetrators; thus, interventions targeting the resolution of IPV should be multifaceted, addressing men’s vulnerability to IPV in addition to women’s victimization. In their study, Copp et al. (2016) suggested that resolving IPV may be done by focusing on the couple as a cohesive unit. This approach aims to benefit both the family and the individual involved (Copp et al. (2016). Although conjoint therapy for couples involved in IPV is controversial due to perceived increased risk (Karakurt et al., 2016; Stith & McCollum, 2011), couples with low levels of aggression may benefit from couple counseling and relationship education (Cleary Bradley & Gottman, 2012; Karakurt et al., 2016; McCollum & Stith, 2007). No level of IPV is deemed acceptable and IPV that is considered to involve low levels of aggression can be subjective. However, IPV involving lower levels of aggression is often the result of poor conflict resolution skills or communication and is infrequent and often characterized by gender mutuality, low violence severity, and low psychopathology (Carlson & Jones, 2010).

Given the alarming prevalence of IPV and its profound ramifications for individuals and families, it is imperative for clinicians to adeptly screen for IPV. Failure to properly address relationship violence may result in ineffective or potentially detrimental treatment (Daire et al., 2014). It is important not only to identify IPV but also to differentiate between different IPV typologies (Carlson & Jones, 2010). IPV typologies developed as a result of research that indicated not all intimate violence is homogenous (Johnson, 1995; Johnson & Leone, 2005). Typologies address contextual differences that may be important for screening and intervention. Thus, accurate, trustworthy measurement of IPV is essential.

The revised Conflict Tactics Scale (CTS2; Straus et al., 1996) is the most widely used measure of IPV (Ahmadabadi et al., 2020; Straus & Douglas, 2004). The CTS2 has 39 items to measure three strategies that people and their partners employ when there is conflict in their relationships: psychological aggression, physical assault, and negotiation. It also includes two extra subscales measuring sexual coercion and injury from assault. Despite its widespread use, researchers have identified limitations with the CTS2. Lehrner and Allen (2014) suggested that the CTS2 might erroneously categorize acts and individuals and overestimate IPV in young women. Babcock et al. (2019) found that conceptualizing violence as man-only, woman-only, and bilateral on the questionnaire was too simplistic and failed to categorize different types of bilateral aggression. The CTS2 also lacks attention to the context and motives for abuse and fails to accurately measure non-physical incidents of abuse, such as emotional, economic, and sexual abuse (Ahmadabadi et al., 2020).

Another often-used screening tool is the Intimate Justice Scale (IJS; Jory, 2004). During therapy sessions, clients can quickly complete and evaluate the IJS form. The IJS measures ethical dynamics in relationships and behaviors of psychological abuse and violence, identifying three levels of violence (no violence, minor violence, and severe violence) (Jory, 2004). However, when answering IJS questions, respondents report their partner’s behavior without considering their own, thereby failing to capture the assessment taker’s IPV (Johnson & Leone, 2005). Furthermore, Jory (2004) acknowledged that the IJS did not inquire about the types of violence, their frequency, severity, or injuries, and recommended using it in conjunction with other questionnaires.

To facilitate practitioners’ understanding of IPV typologies and enhance their ability to differentiate between them, Carlson and Jones (2010) created the Continuum of Conflict and Control (CCC) model. Carlson et al. (2017) utilized the CCC model to develop the Continuum of Conflict and Control Relationship Scale (CCC-RS). Wheeler et al. (2022) subsequently identified a shorter version of this scale, consisting of 10 items which was a reduction from the original 22.

Continuum of Conflict and Control Relationship Scale

The CCC model represents a spectrum of different IPV manifestations, from milder forms of violence that do not escalate to more severe forms of violence that result in severe physical and mental harm (Carlson & Jones, 2010). The CCC model considers various elements, such as the frequency of violence, the psychopathology of the abuser, the situation, and the consequences for the victim (Carlson & Jones, 2010).

The CCC includes three typology groups, from least to most severe. The first group is based on family violence typology models of Hamberger et al. (1996), Holtzworth-Munroe and Stuart (1994), and Johnson & Leone (2005). In Group 1, the violence is gender mutual, escalates from conflict, and is less severe and infrequent. The victimizer(s) do not typically abuse drugs and have low anger proneness and psychopathology and do not resort to violence outside of the relationship. Couples in this group experience minimal fear and may frequently threaten divorce (Carlson & Jones, 2010).

Group 2 is based on Gondolf’s (1988) antisocial batterer, Holtzworth-Munroe and Stuart’s (1994) dysphoric-borderline violent offender, Hamberger et al.’s (1996) passive-aggressive dependent offender, Gottman et al.’s (1995) pit bull, and the moderate to severe violence of Simpson et al. (2007). Couples in Group 2 experience more frequent violence than those in Group 1, with low to moderate severity. Similarly to Group 1, violence does not typically transpire outside of the relationship. The victimizer may have substance abuse issues; mental health issues, such as depression or anxiety; as well as moderate anger proneness. The victim in Group 2 may experience some fear, may be prone to PTSD and depression, and may ultimately pursue divorce (Carlson & Jones, 2010).

Group 3 is the most severe. Group 3 is based on Gondolf’s (1988) sociopathic batterer, Holtzworth-Munroe and Stuart’s (1994) violent antisocial, Hamberger et al.’s (1996) antisocial, Johnson’s (1995) intimate terrorist, and Gottman et al.’s (1995) cobra. The violence is more frequent and is mostly used to obtain and maintain power and control. Furthermore, the victimizer is usually male and has a propensity for violence outside of the relationship. Other characteristics of the victimizer include substance dependence, high anger proneness, antisocial personality, and a history of criminal behavior. The victim in this group is highly fearful, may have to resort to violence for self-defense, and is prone to PTSD and depression. Divorce is improbable due to fear of the victimizer’s response (Carlson & Jones, 2010).

The CCC-RS was originally a 22-item scale. It was created subsequently using the CCC model to categorize different types of partner violence, considering the behaviors of both partners. The CCC-RS consists of three subscales: Controlling Violence (nine items), Relational Conflict (eight items), and Conflict Repair (five items). Participants express their degree of agreement using a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

Wheeler et al. (2022) later identified the 10-item version of the CCC-RS (CCC-RS-10) as part of the process of validating the measurement model of the CCC-RS. Wheeler et al. (2022) conducted exploratory factor analysis (EFA) on data from 332 low-income adults participating in a community-based relationship education program. They removed 12 items from the CCC-RS because they were weakly related to the factor (i.e., loadings <0.03) or strongly related to more than one factor. The remaining 10 items resulted in a 3-factor latent structure, including Physical and Reactive Aggression (3 items), Psychological and Controlling Aggression (3 items), and Relationship Conflict factors (4 items).

Following the EFA, Wheeler et al. (2022) conducted a confirmatory factor analysis (CFA) and estimated the reliability of the CCC-RS-10 factors. Regarding the factor structure of the CCC-RS-10, they found adequate support for the three-factor solution with acceptable fit index values: χ² (32) = 91.54, p < .001; CFI = 0.92; TLI = 0.88; RMSEA = 0.10; SRMR = 0.08. The authors also reported the scale reliability for the new subscales using McDonald’s omega reliability interval estimates (ɷ = .75 for Physical and Reactive Aggression factor, ɷ = .70 for Psychological and Controlling Aggression factor, ɷ = .64 for Relationship Conflict factor).

Wheeler et al.’s (2022) work is the only empirical study to date that has examined the underlying structure of the CCC-RS-10. There are still significant gaps in the research on the validity of the CCC-RS-10. Currently, there have been no examinations conducted on the latent structure of the CCC-RS-10 involving dyadic samples in committed relationships. Moreover, no research has explored the measurement invariance (MI) of the CCC-RS-10 using dyadic data.

Measurement Invariance Between Dyad Members

Prior to conducting comparisons between groups (e.g., gender group), it is crucial to ensure that the individuals in both groups conceptualize and evaluate the constructs measured by survey items in the same way. The absence of MI may suggest that the same instrument assesses different concepts among different groups, or that the same concept has varying interpretations among different groups (Brown, 2015; Vandenberg & Lance, 2000). Nevertheless, it is commonly assumed in research that an instrument functions similarly for all members of a dyad, even when dyadic data are analyzed without conducting MI testing or without appropriately modeling the non-independent nature of dyadic data (Claxton et al., 2015).

Researchers, for example, divide their data on heterosexual couples according to gender. However, this approach results in the loss of important information on the interactions between members of the dyad (Kenny et al., 2006). Researchers recommended a less biased method of adding intercorrelations across factors and indicators among dyad members at the item and factor levels (Claxton et al., 2015; Sakaluk et al., 2021). Given the potential psychometric qualities of the CCC-RS-10, it is important to use a dyadic testing strategy to clarify whether it measures IPV in the same way across genders.

Gender Differences in IPV

Gender differences in IPV have previously been examined in relationship research (Caldwell et al., 2012). Violence against women is more prevalent, especially in terms of injuries or fear (Caldwell et al., 2012; Sardinha et al., 2022), and women experience a greater decrease in relationship satisfaction after experiencing IPV (Shortt et al., 2010). Men can also be victims of IPV; however, typically they experience it with lower levels of lethality (CDCP, 2021).

Prior research has shown that men and women had different perceptions of their own and their partners’ IPV (Caldwell et al., 2012; CDCP, 2021; Sardinha et al., 2022; Shortt et al., 2010). Nevertheless, prior studies have focused on examining differences between genders rather than exploring within-dyad gender differences in IPV. Thus, this study aimed to investigate the gender differences in IPV among dyads, considering the dyadic MI of the CCC-RS-10 holds across partners. Given that the participants in this study were opposite-gendered couples, we posited that partners in the dyad would exhibit differences in specific aspects of IPV.

Purpose of the Present Study

IPV has a substantial and pervasive impact on the physical, emotional, and social well-being of both men and women (Arias & Ikeda, 2006; CDCP, 2021; Coker et al., 2002; NCADV, 2020). For this reason, it is imperative to provide a dependable and consistent screening tool for IPV. Although Wheeler et al. (2022) found the CCC-RS-10 to be a reliable instrument, no investigation has been conducted to verify the similarity of the constructs examined by the CCC-RS-10 among dyad partners. The current study was undertaken with three objectives derived from prior research. Initially, our objective was to assess the pre-determined latent factor structure of the CCC-RS-10 for heterosexual couples. Furthermore, we assessed the dyadic MI of the CCC-RS-10 among members of the dyad. Assuming the dyadic MI holds, we investigated the presence of differences in IPV between the dyad partners. Consequently, we set out to address the following research questions:

What is the latent factor structure of the CCC-RS-10 for opposite-gendered dyad partners?

Does the dyadic measurement invariance of the CCC-RS-10 hold across dyad partners?

Do committed couples’ perceptions of IPV differ across dyad partners?

Methods

Participants and Procedures

Participants included 613 opposite-gendered couples from a larger federally funded study investigating the effects of relationship education delivered in different formats (i.e., in-person vs. online) on the outcome related to couple relationships. The Administration for Children and Families’ Office of Family Assistance funded the study. Participants were recruited from local partners, such as county health departments, libraries, back-to-school events, and word of mouth. Eligibility criteria included being over the age of 18, in a committed relationship, and willing to participate in either a face-to-face or online program. Following the initial recruitment, individual partners received a unique identifier and were sent a link to complete the study consent and baseline questionnaires via Qualtrics. Once both partners completed the baseline questionnaires, couples were then randomly assigned to either receive in-person relationship education using Prevention and Relationship Education 8.0 or online (asynchronous) using the Our Relationship program.

The current study included baseline survey data only and did not examine any intervention effects. Thus, we used data from couples assigned to both intervention groups. The university’s Institutional Review Board authorized all processes. Table 1 displays the demographic characteristics of the participants.

Table 1.

Demographic Characteristics of Dyad Partners Sample (N = 613 Heterosexual Couples).

Variables	Males, n (%)	Females, n (%)
Age
18–20	2 (0.3)	3 (0.5)
21–24	43 (7.0)	66 (10.8)
25–34	188 (30.7)	194 (31.7)
35–44	204 (33.3)	211 (34.4)
45–54	111 (18.1)	94 (15.3)
55–64	43 (7.0)	35 (5.7)
65+	22 (3.6)	10 (1.6)
Ethnicity
Hispanic or Latino	332 (54.2)	387 (63.1)
Not Hispanic or Latino	281 (45.8)	226 (36.9)
Race
American Indian or Alaska Native	6 (1.0)	4 (0.7)
Asian	11 (1.8)	17 (2.8)
African American	142 (23.2)	131 (21.4)
Native Hawaiian or other Pacific Islander	2 (0.3)	3 (0.5)
White	320 (52.2)	345 (56.3)
Multi-racial	31 (5.06)	22 (3.6)
Other	101 (16.5)	91 (14.9)
Highest degree
No degree or diploma earned	22 (3.6)	10 (1.6)
High school general education development (GED)	33 (5.4)	13 (2.1)
High school diploma	79 (12.9)	29 (4.7)
Vocational/technical certification	64 (10.4)	49 (8.0)
Some college but no degree completion	101 (16.5)	76 (12.4)
Associate degree	71 (11.6)	98 (16.0)
Bachelor’s degree	156 (25.5)	196 (32.0)
Master/advance degree	82 (13.4)	141 (23.0)
Income in the past 30 days
No earning	30 (4.9)	121 (19.7)
<$500	25 (4.1)	40 (6.5)
$500 to $1,000	47 (7.7)	61 (10.0)
$1,001 to $2,000	111 (18.1)	126 (20.6)
$2,001 to $3,000	126 (20.6)	122 (20.0)
$3,001 to $4,000	105 (17.1)	71 (11.6)
$4,001 to $5,000	66 (10.8)	24 (3.9)
>$5,000	99 (16.2)	46 (7.5)
Current marital status
Married	376 (61.3)	377 (61.5)
Engaged	80 (13.1)	76 (12.4)
Romantically involved	142 (23.2)	147 (24.0)
On-again and off-again	15 (2.5)	13 (2.1)
Number of children, mean (SD)	1.46 (2.1)	1.41 (1.4)

Note. SD = standard deviation.

Instruments

Ten-Item Continuum of Conflict and Control Relationship Scale

We used the CCC-RS-10 in the current study. The CCC-RS-10 is comprised of three subscales and assesses the perceptions of self and partner’s relational aggressions. First, the Physical and Reactive Aggression subscale consists of three items, such as “When we disagree, my partner or I use physical aggression,” with a subscale total ranging from 0 to 15. Second, the three items, such as “My partner tells me who I can spend time with outside of our relationship,” are part of the Psychological and Controlling Aggression subscale. The total of the Psychological and Controlling Aggression subscale ranges from 0 to 15. Lastly, the Relationship Conflict has four questions with a subscale total ranging from 0 to 20, such as “I blame my partner when arguments get out of hand.” Respondents indicated their level of agreement from “strongly disagree—1” to “strongly agree—5.” Higher scores suggest greater relationship conflict and control.

Data Analysis

We conducted analyses using the Mplus software (version 8.4; Muthén & Muthén, 2017). The maximum likelihood robust (MLR) estimator was employed when specifying all CFA models. Since the MLR is known to be robust to non-normality, it was recommended that it should be used to handle the usage of ordinal data in the current investigation (Bandalos, 2014). This method adjusts the test statistic and standard errors according to the data, thereby reducing the bias resulting from the non-normal distribution (Satorra & Bentler, 1994).

We evaluated non-independence between partners prior to MI testing. To statistically assess the lack of independence between dyad partners, a Pearson product–moment correlation was employed. Moderate correlations were indicative of non-independence between dyadic partners (Kenny et al., 2006). Subsequently, CFAs were performed independently for men and women to verify the applicability of the previously identified three-factor model for later MI testing.

Reliability and Validity

We examined McDonald’s omega to assess scale reliability for men and women separately. McDonald’s omega is based on a factor analytic approach and is recognized for exhibiting less bias when measuring the internal consistency of Likert-type rating scales (Hayes & Coutts, 2020). As a measure of internal consistency, McDonald’s omega values above .7 are advised, but values above .6 are considered acceptable (Hair et al., 2022).

Convergent validity evaluates whether all indicators converge well by loading significantly on their targeted construct (Cheung et al., 2023). We assessed the convergent validity of the CCC-RS-10 for men and women separately by examining the following criteria: acceptable overall model fit, statistically significant standardized factor loadings higher than .4, and average variance extracted (AVE) greater than .5 (Cheung et al., 2023). The AVE represents the average amount that the construct can account for variances in the indicators (Fornell & Larcker, 1981).

Dyadic Measurement Invariance

Dyadic MI was tested by fitting a series of CFA models with various equality constraints on parameters across the gender groups: configural (equal factor structure), metric (equal factor loadings), scalar (equal intercepts), and strict (equal residual variance) invariance (Cheung & Rensvold, 2002; Steenkamp & Baumgartner, 1998; Vandenberg & Lance, 2000). For each step, covariances between equivalent indicators and between common factors for men and women were estimated to control for non-independence.

The configural invariance model for dyadic data was specified with no constraints on the loadings, mean, variance, and covariance of each factor. Support for the configural invariance indicates that the overall factor structure (i.e., the number of factors and pattern of loadings) fits well for both groups. Next, the metric invariance model was specified with factor loading constrained to be equivalent across groups while still holding the specifications from the configural invariance model. The metric invariance indicates that the relations between the items and factors are equivalent across groups (Cheung & Rensvold, 2002; Vandenberg & Lance, 2000). The third step examined the scalar invariance, which involved constraining the item intercepts to be equivalent across groups with the metric invariance model’s constraints maintained. Scalar invariance indicates that there are equivalent factor loadings and intercepts across groups (Cheung & Rensvold, 2002; Vandenberg & Lance, 2000). If the fit of the metric invariance model is not noticeably worse than the configural invariance model, it serves as the foundation for comparing latent means across groups (Cheung & Rensvold, 2002; Vandenberg & Lance, 2000). The last step was to test for residual invariance, which involved constraining the item residuals to be equivalent in the two groups while retaining the constraints applied in the scalar invariance model. Residual invariance indicates that the error or residual variances that are not explained by the latent factors in a model are equal across groups (Cheung & Rensvold, 2002; Vandenberg & Lance, 2000).

Across all models, we assessed the model fit using various fit criteria, including chi-square, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI; Hu & Bentler, 1999). The chi-square test has been demonstrated to be inflated by larger sample sizes (Brown, 2015). Due to the present sample size (613 couples), models with significant chi-square values were not necessarily rejected in this study (Bentler & Bonett, 1980). RMSEA values below 0.06, SRMR values below 0.08, CFI values above 0.95, and TLI values above 0.95 indicate a good fit (Brown, 2015). Standardized factor loadings greater than 0.4 were interpreted as significantly related to the factor (Brown, 2015).

When comparing the nested models (i.e., models that can be obtained from each other by the imposition of any parametric restrictions), fit statistics were examined after each subsequent model before proceeding to the next. A significant increase in model misfit after each additional set of constraints suggests variance between groups. When comparing models at each step of invariance testing, we used the chi-square difference test (Δχ²) to compare these nested models. A significant Δχ² indicates statistically significant invariance between the two models. However, the chi-square likelihood ratio always remains large and statistically significant when dealing with complex models and/or large samples, and it is also sensitive to deviations from multivariate normality (Chen, 2007). Therefore, we also looked at relative goodness-of-fit indices, such as the difference in the models’ RMSEA values (ΔRMSEA) and CFI values (ΔCFI). Invariance is supported by ΔRMSEA less than or equal to 0.015 and ΔCFI less than or equal to 0.01 (Chen, 2007; Cheung & Rensvold, 2002).

Results

Descriptive Statistics and Evaluation of Non-Independence

Table 2 presents descriptive statistics of items by gender. The first three items measuring physical and reactive aggression showed high levels of skewness and kurtosis and small variances (i.e., around 92% of men and 90% of women selected “Strongly Disagree” on those items), indicating that there was not much physical violence occurring in our sample. The skewness of the remaining seven items (Item 4–Item 10) in absolute values ranged between .42 and 2.66 among men and between .31 and 2.58 among women. Absolute values of kurtosis ranged between .01 and 6.62 among men and between .43 and 6.33 among women. As a result, the MLR estimator, which is well-known for being robust to non-normality, was used for further analyses (Bandalos, 2014).

Table 2.

Descriptive Statistics by Gender.

Item	Mean		SD		Skewness		Kurtosis
Item	M	F	M	F	M	F	M	F
Sometimes I need to defend myself against physical harm during arguments	1.19	1.18	0.67	0.60	4.26	3.89	18.69	16.29
Our arguments frequently escalate toward physical violence	1.13	1.13	0.55	0.51	5.43	4.73	31.93	25.33
When we disagree, my partner or I use physical aggression	1.14	1.18	0.56	0.58	4.94	3.84	26.72	16.64
My partner tells me who I can spend time with outside of our relationship	1.66	1.39	1.14	0.90	1.72	2.45	1.87	5.30
I sometimes become afraid of my partner	1.39	1.36	0.92	0.83	2.66	2.58	6.62	6.33
My partner becomes jealous when I spend time with others	1.98	1.73	1.32	1.16	1.14	1.55	.01	1.32
I sometimes threaten divorce/separation during an argument	1.70	2.12	1.15	1.37	1.57	.94	1.32	−.43
My partner or I are quick to anger during an argument	2.43	2.64	1.29	1.39	.50	.31	−.84	−1.17
My partner sometimes blames me when arguments get out of hand	2.52	2.56	1.44	1.49	.42	.40	−1.21	−1.29
I blame my partner when arguments get out of hand	2.26	2.44	1.24	1.33	.61	.47	−.71	−1.00

Note. M = male; F = female; SD = standard deviation.

As shown in the bivariate correlations (Table 3), all the items were significantly correlated (p < .001) for men and women. While some of the correlations were small, ranging from r = .10 to r = .46, the majority were moderate (roughly r = .25), suggesting that the scores for men and women are related. Because non-independence has been shown conceptually (i.e., individuals in committed relationships) and statistically, the dyad should be the unit of analysis rather than the individual when assessing the MI across dyad partners.

Table 3.

Bivariate Correlations.

Items	Item 1	Item 2	Item 3	Item 4	Item 5	Item 6	Item 7	Item 8	Item 9	Item 10
Item 1	.22	.27	.31	.15	.16	.13	.17	.17	.17	.19
Item 2	.22	.26	.33	.13	.15	.10	.12	.12	.14	.13
Item 3	.23	.29	.35	.15	.16	.10	.18	.16	.14	.16
Item 4	.25	.22	.21	.23	.23	.16	.30	.23	.24	.23
Item 5	.16	.19	.16	.21	.26	.24	.31	.27	.28	.24
Item 6	.18	.16	.17	.15	.13	.12	.21	.22	.21	.19
Item 7	.22	.17	.16	.22	.22	.21	.27	.40	.41	.33
Item 8	.15	.14	.15	.20	.23	.24	.28	.45	.45	.38
Item 9	.21	.20	.20	.20	.27	.24	.32	.46	.45	.38
Item 10	.16	.15	.17	.19	.19	.21	.28	.37	.38	.33

Note. Male scores are on the horizontal axis and female scores are on the vertical axis. All correlations were significant at p < .001.

Factor Structure

Building on the factor analytic research with the CCC-RS-10 (Wheeler et al., 2022), we assessed the three-factor structure for men and women separately. The three-factor model demonstrated good fit in both samples (male: χ² [31] = 82.177, p < .001; RMSEA = 0.053, 90% CI [0.039, 0.066]; SRMR = 0.047; CFI = 0.959; TLI = 0.940; female: χ² [31] = 37.604, p < .001; RMSEA = 0.019 [0.000, 0.038]; SRMR = 0.031; CFI = 0.990; TLI = 0.982). Table 4 depicts standardized factor loadings with a statistically significant relation between items and latent factors. The factor loadings ranged from .574 to .972 for men, and those for women ranged from .657 to .889, indicating a positive relationship between the items and corresponding latent factors.

Table 4.

Standardized Factor Loadings for Male and Female Partners.

Factors	Item	Factor Loadings (Male Sample)	Factor Loadings (Feale Sample)
Physical and reactive aggression	Sometimes I need to defend myself against physical harm during arguments.	.773	.777
	Our arguments frequently escalate toward physical violence.	.972	.889
	When we disagree, my partner or I use physical aggression.	.840	.843
Psychological and controlling aggression	My partner tells me who I can spend time with outside of our relationship.	.688	.657
	I sometimes become afraid of my partner.	.773	.762
	My partner becomes jealous when I spend time with others.	.692	.681
Relationship conflict	I sometimes threaten divorce/separation during an argument.	.574	.673
	My partner or I are quick to anger during an argument.	.818	.851
	My partner sometimes blames me when arguments get out of hand.	.876	.874
	I blame my partner when arguments get out of hand.	.767	.811

Note. All loadings were significant at p < .001.

Reliability and Validity

The results of the reliability and validity analysis are presented in Table 5. We examined McDonald’s omega reliability to assess the internal consistency of each subscale. We found strong scale reliability with both men (.76 ≤ ɷ ≤ .89) and women (.74 ≤ ɷ ≤ .88) for all three factors. We examined the AVE as evidence of convergent validity. The AVE ranged between .52 and .75 for men and between .49 and .70 for women.

Table 5.

Reliability and Validity Estimates for CCC-RS-10 subscales.

Subscale	Internal Consistency Reliability (Omega Coefficient)		AVE
Subscale	Male	Female	Female	Female
Physical and reactive aggression	.89	.88	.75	.70
Psychological and controlling aggression	.76	.74	.52	.49
Relationship conflict	.85	.88	.59	.65

Note. AVE = average variance extracted; CCC-RS-10 = 10-item Continuum of Conflict and Control Relationship Scale.

Dyadic Measurement Invariance Analysis

The results of the dyadic MI testing are summarized in Table 6. For all the MI models, we added covariances between common indicators and between common factors for men and women (see Figure 1). In other words, we accounted for non-independence at both the item and factor levels for all the dyadic MI models. First, the configural invariance model yielded a statistically significant chi-square value, but all the other fit index values represented good fit: χ² (143) = 214.708, p < .001; RMSEA = 0.047, 90% CI [0.041, 0.053]; SRMR = 0.041; CFI = 0.969; TLI = 0.962), indicating the same factor structure (i.e., the same number of factors and same factor patterns) across dyad partners.

Table 6.

Model Fit.

Model	χ² (df)	Δχ² (Δdf)	RMSEA [90% CI]	ΔRMSEA	SRMR	CFI	ΔCFI	TLI
CFA
Male sample	82.177 (31)**		0.053 [0.039, 0.066]		0.047	0.959		0.940
Female sample	37.604 (31)**		0.019 [0.000, 0.038]		0.031	0.990		0.982
Invariance
Configural	214.708 (143)**		0.047 [0.041, 0.053]		0.041	0.969		0.962
Metric	231.370 (150)	16.662 (7)*	0.049 [0.043, 0.055]	0.002	0.044	0.965	−0.004	0.959
Scalar	277.129 (158)	45.759 (8)**	0.054 [0.048, 0.060]	0.005	0.054	0.956	−0.009	0.950
Residual	340.714 (166)	63.586 (8)**	0.060 [0.055, 0.065]	0.006	0.058	0.946	−0.010	0.939

Note. χ² = chi-square statistic; df = degrees of freedom; Δχ² = chi-square difference; Δdf = difference in degrees of freedom; RMSEA = root mean square error of approximation; ΔRMSEA = difference in RMSEA; SRMR = standardized root mean square residual; CFI = comparative fit index; ΔCFI = difference in CFI; TLI = Tucker-Lewis index; CI = confidence interval; CFA = confirmatory factor analysis.

p < .05. **p < .001.

Figure 1.

Factor structure of the CCC-RS-10 with correlated factors and correlated indicators between male and female partners.

Moving to metric invariance, factor loadings were held equal across gender groups. Although the chi-square difference tests suggested a statistically significant difference between the two models, the small changes in RMSEA (ΔRMSEA = 0.002) and CFI (ΔCFI = −0.004) reflected that adding constraints did not significantly increase misfit from the configural to metric models. Therefore, results supported metric invariance, indicating equivalent item loadings across dyad partners. Next, scalar invariance was tested by holding the factor loadings and intercepts of all factor loadings equal across groups, and the resulting model was compared to the metric model. Although the two models’ chi-square differences were statistically significant, the change in RMSEA (ΔRMSEA = 0.005) and CFI (ΔCFI = −0.009) supported scalar invariance across groups, indicating partners with the same underlying level of the latent factor will have, on average, equivalent observed item scores. Lastly, the residual variance was also supported, constraining the equality of residual variance across each. The chi-square difference between the two nested models was significant, but alternative fit indices supported residual variance (ΔRMSEA = 0.006; ΔCFI = −0.010), indicating the error or residual variances were equivalent across dyad partners.

Comparison of Latent Means Across Dyad Partners

Given that full invariance was met, differences in the latent factor means between partners were tested. To investigate the size of the latent mean differences, we computed Cohen’s (1988) d effect size. The computed value of d showed that women reported significantly lower latent mean scores than men (d = −0.31) on the Psychological and Controlling Aggression factor. On the other hand, women reported significantly higher latent mean scores in the Relationship Conflict factor (d = 0.23). According to Cohen’s rules for interpreting effect size values, these results indicate small effects. There were no significant differences between dyad partners in the Physical and Reactive Aggression factor.

Discussion

Employing appropriate assessment measures can empower clinicians in detecting the presence of IPV and discerning the specific type of violence involved. Moreover, the use of appropriate measures assists in identifying appropriate treatment interventions. The goal of the current study was to better assist clinicians and researchers in using the CCC-RS-10 with couples by investigating the factor structure and MI of the instrument. The three-factor structure with Physical and Reactive Aggression, Psychological and Controlling Aggression, and Relationship Conflict factors was supported with acceptable fit index values. Findings from tests of dyadic MI in the context of CFA demonstrated support for factorial invariance across gender groups. Having established invariance of the CCC-RS-10 across genders, differences between men and women can be interpreted as arising from actual differences in conflict and control relationships, not that the instrument is measuring different concepts in the two groups. Differences found between men and women on this scale most likely relate to actual differences in gender rather than different constructs being measured by the same instrument.

In addition to MI, we tested within couple of mean differences on the CCC-RS-10 factors. Using latent mean values, the results of this study showed the differences in the computed effect sizes (d) between men and women on Psychological and Controlling Aggression and Relationship Conflict scores. Findings are consistent with those reported in the study of IPV among couples in romantic relationships (Kar & O’Leary, 2013; Woodin, 2011), where small but statistically significant differences were identified between men and women in ratings about the perceptions of self and partner’s IPV. Regarding the gender differences in psychological and controlling aggression, Kar and O’Leary (2013) asked married couples to rate their own levels of psychological aggression, dominance, and jealousy. The authors discovered that, in comparison to men, women rated their own levels of psychological aggression, dominance, and jealousy at significantly higher rates. Comparably, the findings of our study showed that men rather than women reported more domineering and psychologically aggressive behavior while women reported more relationship conflict than men. It is not uncommon for women to be more aware of relationship distress issues than men as previous research demonstrated that women are more likely to engage in relationship help-seeking and generally report lower overall relationship satisfaction than men (Doss et al., 2003).

Implications

The results from this study have implications for family science scholars as well as clinical practitioners. From a practitioner perspective, many clinicians are not well-trained to recognize and address IPV, and the majority of them do not employ standardized techniques and assessments to screen for IPV (Daire et al., 2014; Juarez et al., 2020). The use of guidelines, such as the International Classification of Diseases, 11th Revision (ICD-11; Armstrong, 2021), may help identify whether a relationship issue is present; however, it is not as helpful in making distinctions between different forms of relationship problems (Juarez et al., 2020). IPV assessment in mental health settings may help with the development of more accurate case conceptualization and treatment plans (Burns et al., 2022). IPV assessment is a complicated process because it requires discerning the appropriateness of couple-based treatment interventions based on the severity of the reported IPV. For example, couples who demonstrate more serious IPV marked by the establishment of power and control are not good candidates for conjoint treatment because a victim’s safety may be jeopardized during the treatment process (Bograd & Medelos, 1999). However, identifying the presence, and discerning the nature/context, of IPV within couples is challenging as there are not standard approaches or assessments. The use of a measure like the CCC-RS-10 may be helpful for clinicians because it provides a formal method for asking tough relationship conflict-related questions, as well as providing inferences toward the severity and nature of the IPV. However, no measure should be used in isolation and clinicians should consider a formal IPV-screening protocol to help guide how to handle IPV disclosures and how treatment decisions will be made (Daire et al., 2014).

Some relationship treatment modalities, such as relationship education, have demonstrated effectiveness with both improving relationship quality and decreasing aggression (Carlson et al., 2017; Wilde & Doherty, 2011). Relationship education may be an effective intervention with couples who experience IPV because of the focused relationship skills acquisition and the universal nature of its implementation. When provided in short durations and to couples who volunteer (as opposed to those who are court-mandated), some relationship education scholars argued against formal screening processes (Stanley et al., 2020). Conversely, clinicians treating couples in private practice settings and implementing treatment modalities that are individualized and more intense should carefully consider how to assess for IPV and the safety of working with couples (Schacht et al., 2009).

Furthermore, the sample consisted of predominantly young, low-income, Hispanic/Latine couples. IPV is higher among young ethnic and racial minorities (Ahmadabadi et al., 2020; Capaldi et al., 2012; Gordon et al., 2022). Socioeconomic disadvantages such as poverty, low income, low education, and financial stress are risk factors for IPV as they may stimulate disagreements and conflict due to increased stress (Ahmadabadi et al., 2020; Cano & Vivian, 2003; Copp et al., 2016; Schwab-Reese et al., 2016). Other salient stress factors with this population include discrimination and acculturation stress (Caetano et al., 2007; Cano & Vivian, 2003; Hammett et al., 2020; Trail et al., 2012). Copp et al. (2016) suggested that partner violence often occurs as a reactive response to experienced stressors. This further highlights the importance of considering different factors other than power and control. Furthermore, the CCC-RS-10 can also be used to broach conversations about prevalent stressors—including cultural stressors, that may warrant attention.

Limitations and Future Research

Although our investigation of dyadic MI of the CCC-RS-10 marks a promising step toward improving the quality of IPV measures, it is not without limitations. First, the sample consisted of opposite-gendered dyads. Future studies should investigate the CCC-RS-10 with same-gendered couples. However, given the findings that indicated invariance between genders, we have little reason to speculate that the CCC-RS-10 would not be relevant for same-gendered couples as well.

Second, regarding the demographic characteristics, most participants in the current study were between the ages of 25 and 44 (64% men and 66.1% women). In terms of ethnicity, 54.3% of men and 63.1% of women partners identified as Hispanic or Latino(a). As demographic characteristics may also impact the rating of IPV measured by the CCC-RS-10 form, future studies should explore the MI across different ethnic or age groups or language. We used both an English and Spanish version of the measure but have not conducted tests of invariance by language.

Ultimately, tailored interventions that consider the nature and context of couple conflict may be most effective. However, we are not aware of studies that examined specific IPV profiles or classes of couples who began treatment and tested interventions based on those profiles. Thus, perhaps the ultimate utility of a measure like the CCC-RS-10 would be to directly inform treatment approaches. Future research should aim to test tailored interventions based on unique dyadic results of the CCC-RS-10.

Conclusion

This study provides valuable information regarding the CCC-RS-10 across dyad partners in romantic relationships. To the best of our knowledge, this study is one of the few confirmatory factor analytic studies of this measure and the first to explore MI between men and women partners on the CCC-RS-10. Further study topics include conducting MI testing to compare romantic partners across a range of age groups or from different ethnic groups. In confirming that the CCC-RS-10 is measuring the same concepts (conflict and control in relationships) in men and women, we provide continued support for the use of this measure in research and clinical settings.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: This research was supported by a grant from the U.S. Department of Health and Human Services (DHHS), Administration for Children and Families, Office of Family Assistance, Grant number 90ZB0009. Any opinions, findings, conclusions, or recommendations are those of the authors and do not necessarily reflect the views of U.S. DHHS, Office of Family Assistance.

ORCID iD

Jungsun Go

Author Biographies

Jungsun Go, PhD, is a research associate at the Research, Evaluation, and Measurement Center (REM Center) at the University of South Carolina. She teaches classes in classroom assessment and educational research methods. She is interested in structural equation modeling, survey design, and advanced statistical models and applying those methods to explore K-12 school-related issues.

Annette Ibarra Skeen, is a licensed professional counselor in private practice. She has worked in the counseling field since 2005, primarily working with vulnerable populations and has experience working in non-profit organizations, community agencies, and college/educational settings. She is currently a doctoral candidate at the University of South Carolina and has focused on research related to victims of intimate partner violence.

Ryan G. Carlson, PhD, is a professor and chair of the Department of Educational and Development Sciences at the University of South Carolina. He is a licensed professional counselor with training in several couple’s counseling/relationship education approaches who has experience working in private practice and community mental health. He has studied and is published in the areas of (a) outcomes for low-income couples and individuals who received relationship education; (b) efficacious recruitment/retention of low-income participants into research; and (c) intimate partner violence typologies and implications for counseling. Dr. Carlson is the evaluator of Project Harmony—a randomized controlled trial testing two modalities of relationship education with low-income couples.

Dalena Dillman Taylor, PhD, is an associate professor at the University of North Texas. Her primary research interests include: advancement of Adlerian Play Therapy field toward evidence-based practice; counseling and educational services for high-need children and families; and counselor development and supervision. She is co-principal investigator of Project Harmony—a large grant-funded project and impact study that provides relationship education to low-income couples. Dr. Dillman Taylor has been active in the areas of research, teaching, and service.

Sejal M. Barden, PhD, is a professor at the University of Central Florida in the Department of Counselor Education and School Psychology and Executive Director of the Marriage and Family Research Institute. Dr. Barden’s primary research interests include: supporting healthy relationships for individuals and couples from underserved populations; developing integrated health interventions for chronically ill persons; and conducting culturally sensitive outcome research for couples and families. She is principal investigator of Project Harmony—a large grant-funded project and impact study that provides relationship education to low-income couples. Dr. Dillman Taylor has been active in the areas of research, teaching, and service.

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Dyadic Measurement Invariance of the 10-Item Continuum of Conflict and Control Relationship Scale

Abstract

Keywords

Continuum of Conflict and Control Relationship Scale

Measurement Invariance Between Dyad Members

Gender Differences in IPV

Purpose of the Present Study

Methods

Participants and Procedures

Instruments

Ten-Item Continuum of Conflict and Control Relationship Scale

Data Analysis

Reliability and Validity

Dyadic Measurement Invariance

Results

Descriptive Statistics and Evaluation of Non-Independence

Factor Structure

Reliability and Validity

Dyadic Measurement Invariance Analysis

Comparison of Latent Means Across Dyad Partners

Discussion

Implications

Limitations and Future Research

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Author Biographies

References