Sage Journals: Discover world-class research

Abstract

The Domestic Violence Questionnaire (DVQ) is a measure that captures physical, sexual, economic, and psychological violence perpetrated against women. A team of local academicians and intervention experts evaluated several tools for domestic and gender-based violence, concluding that the DVQ fitted best in the context of evaluating the Living Peace intervention in conflict-affected eastern DRC. The purpose of this validation study was to: (1) validate the factor structure of the Swahili version of the DVQ, (2) to evaluate its convergent and divergent validity, and (3) to examine its internal consistency reliability. The factor structure was evaluated using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) while concurrent validity and internal consistency reliability were evaluated using Pearson correlation and Cronbach’s alpha coefficients, respectively. The study participants were women, partners of men that participated in the Living Peace program, a community-based intervention program that works on promoting positive masculinity among men that are perceived to be violent by their community. Data were obtained from two independent purposive samples of 704 participants for EFA and 878 participants for CFA, selected from 60 villages in North and South Kivu, DRC. The results indicated that five and three-factor structures could be meaningfully interpreted in EFA, each with 66% explained variance. For comparative purposes, CFA was conducted on the two newly created factor models and the original factor model. All models reach good fit indexes, with the three-factor model demonstrating the best fit. Its standardized factor loadings ranged from 0.57 to 0.91. The internal consistency was α = .90 for the total scale and ranged from α = .75 to .926 per dimension. Finally, scores of DVQ were negatively correlated with mental well-being (r = −0.23, p < .001), and positively with depression (r = .435, p < .001), as hypothesized. The Swahili version of DVQ has excellent psychometric properties and can be applied to evaluate the physical, sexual, economic and psychological violence in victims of domestic violence in humanitarian crisis settings.

Plain Language Summary

The purpose of this validation study was: (1) To validate the factor structure of the Swahili version of the DVQ, (2) to evaluate its convergent and divergent validity, and (3) to examine its internal consistency reliability. The factor structure was evaluated using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) while the concurrent validity and internal consistency reliability were evaluated using Pearson correlation and Cronbach’s alpha coefficients, respectively. The study participants were women, partners of men that participated in the Living Peace program, a community-based intervention program that works on promoting positive masculinity among men that are perceived to be violent by their community. Data were obtained from two independent purposive samples of 704 participants for EFA and 878 participants for CFA, selected from sixty villages in North and South Kivu, DRC. The results indicated that five and three-factor structures could be meaningfully interpreted in EFA, each with 66% explained variance. For comparative purposes, CFA was conducted on the two newly created factor models and the original factor model. All models reach good fit indexes, with the three-factor model demonstrating the best fit. Its standardized factor loadings ranged from 0.57 to 0.91. The internal consistency was α = 0.90 for the total scale and ranged from α = 0.75 to 0.926 per dimension. Finally, scores of DVQ were negatively correlated with mental well-being (r = −0.23, p < 0.001), and positively with depression (r = 0.435, p < 0.001), as hypothesized. The Swahili version of DVQ has excellent psychometric properties and can be applied to evaluate the physical, sexual, economic and psychological violence in victims of domestic violence in humanitarian crisis settings.

Keywords

psychometric validation factors analysis internal consistency concurrent validity domestic violence questionnaire Eastern DRC humanitarian crisis

Introduction

Eastern Congo is affected by extreme violence and conflict for several decades. It is estimated that more than 5 million people have lost their lives since 1988 (Kelly et al., 2021). The conflict-affected provinces of North and South Kivu, the geographical focus of this study, have been among the areas with the highest rates of violence and rape due to several armed groups (Kelly et al., 2021; Peterman et al., 2011). It is estimated that more than 120 militias and armed groups are active in eastern DRC (Global Centre for the Responsability to Protect, 2022) with an estimated 2,487 civilians killed by armed groups in North Kivu and South Kivu in 2020. Worryingly, a growing number of studies are documenting how living in a conflict-affected place may increase the risk of interpersonal violence, including domestic violence (DV), even long after the conflict is over (Kelly et al., 2021). In Liberia for instance, residents in more heavily conflict-affected districts were associated with a 50% increased IPV risk four years after formal peace was declared (Kelly et al., 2018). Therefore, the greatest threat of violence women face in the Democratic Republic of Congo (DRC) may not be from strangers but from their partners in their own homes (Peterman et al., 2011). DV is defined as “a behavior within an intimate relationship that causes physical, sexual, or psychological harm, including acts of physical aggression, sexual coercion, and psychological abuse and controlling behaviors” (WHO, 2010).

The prevalence of DV against women in humanitarian crisis settings like eastern DRC is high. The 2014 DRC Demographic and Health Survey (DHS) found that above 2/3 of ever-married women had experienced sexual, physical, or emotional violence by an intimate partner (Le Roux et al., 2020; Tlapek, 2015), placing the country at nearly twice the global prevalence (i.e., 1/3) of this form of violence (WHO, 2021). This figure is not significantly different from the prior 2007 DHS showing that 71% of women in DRC reported having experienced physical and emotional abuse in their homes (DRC Ministry of Planning, 2008). Using a different methodology, the Promundo’s IMAGES study (International Men and Gender Equality Survey), 45% of women in the Eastern DRC reported having ever experienced physical violence and 49% had ever experienced sexual violence from their partners (Le Roux et al., 2020; Slegh et al., 2014). The IMAGES authors further highlighted that rape as part of the conflict was reported by 22% of women. Happening in the homes, DV may still be underestimated as it may not be reported and may be accepted or condoned due to cultural norms and taboos that inhibit speaking out against this form of violence (Kelly et al., 2021; Ministère du Plan et Suivi de la Mise en œuvre de la Révolution de la Modernite et al., 2014; Parmar et al., 2012; Peterman et al., 2011).

Being a patriarchal society (Jonas et al., 2022), discrimination against Congolese women in all areas—health, education, access to economic and political rights and justice (Davis et al., 2014; WHO, 2021). Beliefs such as the one that men have a “right to sex” even if women refuse supported by 62% of North Kivu women and 48% of North Kivu men (Bronte, 2020) contribute to justifying sexual assaults. Similarly, almost a third of the men questioned stated that women “sometimes want to be raped,” and that a woman sexually assaulted «may enjoy it» (WHO, 2021). Additionally, there are other traditional believes that are misogynistic, such as the belief that having sex with a virgin confers power, good luck and even cures HIV/AIDS, a superstition behind the rape of young girls (WHO, 2021). About stigma, Congolese women and girls receive negative reactions from their community when disclosing violence experiences especially Sexual Violence (SV) (Murray et al., 2018). Community members’ blame might harm survivors’ relationships and marriageability (Glass et al., 2012; J T Kelly et al., 2011). Focus groups with SV survivors in the Democratic Republic of the Congo (DRC) found that survivors may experience barriers to revenue production and farming activities (Kelly et al., 2011). Loss of capacity to sustain one’s family can contribute to greater marginalization and loss of survivors in a vicious circle of marginalization and loss (Glass et al., 2012). This isolation can be severe; according to a research conducted in the DRC, 12.5% of SV survivors seeking medical assistance reported being compelled to leave their homes (Steiner et al., 2009).

Domestic violence is a public health and human rights issue given its magnitude and long-lasting consequences (WHO, 2010, 2019). The frequency and severity of domestic violence was repeatedly found to be associated with negative outcomes. Even so-called low levels of violence such as “pushing or shoving” are associated with depression (Mason & O’Rinn, 2014). Prior research has shown women who experienced DV have a risk of developing physical problems (Cheung et al., 2009; Imanishimwe et al., 2020; Kapiga et al., 2017), including difficulty accessing and using antenatal care services for pregnant women (Bahati et al., 2021), as well as mental health issues (Kapiga et al., 2017). Physical, sexual, psychological or emotional violence is positively associated with depression, low self-esteem, and PTSD (Calvete et al., 2007; Sharma et al., 2021) and negatively associated with mental well-being (Mootz et al., 2020).

To address the detrimental effects of DV, several authors developed and validated different tools to access domestic violence against women in many countries. The most commonly used tools are the domestic violence questionnaire (Indu et al., 2011); the abuse assessment screen (Weiss et al., 2003); the composite abuse scale (Hegarty et al., 2013); the conflict tactics scale-2 (Tiwari et al., 2010); the humiliation, afraid, rape, kick (Sohal et al., 2007); the hurt, insult, threaten, scream (Iverson et al., 2015); the index of spouse abuse (Cook et al., 2003); the ongoing abuse screen (Weiss et al., 2003); the ongoing violence assessment tool (Ernst et al., 2004); the parent screening questionnaire (Dubowitz et al., 2008); the partner violence screen (MacMillan, 2006); the slapped, things, threaten (Paranjape & Liebschutz, 2003); and the woman abuse screening tool (Wathen et al., 2008). A team of local academicians and intervention experts evaluated the content of the aforementioned tools looking at factor denominations and item formulation, concluding that the DVQ fitted best with the purpose of evaluating the impact of the Living Peace intervention in Eastern DRC. Furthermore, literature review and prior qualitative research, including the IMAGES study (Slegh et al., 2014) informed the decision (Basile et al., 2007; Indu et al., 2011; Tlapek, 2015; White et al., 2000; Zolotor et al., 2009).

According to the DVQ developers, items to measure domestic violence were selected from two questionnaires from India—the National Family Health Survey-3 Questionnaire (International Institute for Population Sciences, 2007) and the India SAFE Questionnaire (Kumar et al., 2005). The authors selected 11 items from the former, nine items from the latter, and six items commonly from both. They further conducted qualitative interviews for dimensionality and comprehensive coverage of items where 3 items were added based on expert opinion. Thus, a 29-item questionnaire was initially constructed with 17 and 12 items measuring psychological and physical (including sexual) violence respectively (Indu et al., 2011). The items of this questionnaire were conceived to represent the domains of physical, sexual and psychological violence, but Exploratory Factor Analysis (EFA) revealed five factors. “physical violence, controlling behavior, insulting behavior, neglecting behavior, and economic restriction” that explained 71.14% of the variance among the observed variables (Indu et al., 2011). The exploratory factor analysis also informed that 20 items to be retained in the final DVQ 13 items for psychological violence (i.e., controlling behavior, insulting behavior, neglecting behavior, and economic restriction) and 7 items for physical violence. The DVQ is a short, simple, self-administrable and discriminative instrument (Indu et al., 2011).

The current study aimed to examine the psychometric properties of the domestic violence questionnaire for the Congolese population. We hypothesized that the scores on DVQ would fit the five-factor model of the DVQ. We further hypothesized that the total scores of DVQ would be strongly and positively correlated with depression symptoms, but negatively correlated with mental well-being to confirm construct validity.

Methodology

Study Design

This cross-sectional study was conducted to examine the psychometric properties of the domestic violence questionnaire among adult Congolese women.

Sample

The sample was purposive made up of Swahili women speakers (N = 1,582) whose partners (husbands) are beneficiaries of the Living Peace intervention (LPint) in the eastern DRC. To be included in the LPint, men needed to be aged at least 18 years, living in a couple with a woman, and able to read and provide a written consent form in Swahili. In addition, men needed to be “perceived by community members as being violent.” To make such a determination, LPint facilitators collaborate with community leaders, family members, police and military hierarchy to select participants based on a consensus process. Community leaders and camp authorities were trained to have a common understanding of the identification of such families in their community or camp.

Measures

Data were collected using the 20-item Domestic Violence Questionnaire(Indu et al., 2011), the 15-item Hopkin Symptoms Checklist-depression subscale (Derogatis et al., 1974) and the 14-item Warwick-Edinburg Mental Wellbeing Scale (Clarke et al., 2011). The tools were adapted to the context of DRC (no items were removed and no major change in item content was needed) and translated into Swahili before the data collection process. In this process, a team of academic and field practitioners decided that the items of all scales should be scored on a 7-point Likert scale: Strongly disagree (1), to strongly agree (7).

Data Collection

Household-based, interview style using tablets. The data were collected as part of a larger RCT (Jansen et al., 2022). Data from the baseline were used and EFA and CFA were performed on group 1 (case group of the RCT) and group 2 (control group of the RCT) which are independent groups because of the RCT clustered randomization process.

Ethical Consideration

The ethical clearance was obtained from the Institute Review Board of the University of Rwanda, College of Medicine and Health Sciences in Rwanda (No 157/CMHS IRB/2019); from Commission d’éthique de l’ Institut Supérieur du Lac á Goma (No 059/ISL/PCE/MB/2018). At the beginning of each data collection phase, informed consent was obtained from participants. The consent form had information about the study scope, design and timeline, confidentiality, study benefits and risks, and the right to refuse or withdraw from the research at any time. As interviews on domestic violence or Gender-Based Violence present a heightened risk for the survivors, especially when conducted in a household setting, the research team followed the WHO guidelines on intimate partner violence (WHO, 2013) and also trained the data collectors on safekeeping procedures.

Statistical Analyses

All analyses were performed using SPSS, version 28 and SPSS AMOS, version 28 (IBM Corp, 2021) statistical software packages.

Factorial Validity

The Kaiser Meyer-Olkin Measure of Sampling Adequacy and Bartlett’s Test of Sphericity were used to assess the suitability of the data for factor analysis. Exploratory factor analysis (EFA) was first performed unrotated, using maximum likelihood extraction and eigenvalues > 1. Additionally, we performed EFA with oblimin rotation and enforced a five-factor solution to test the theoretical structure of the DVQ. CFA was then performed using maximum likelihood estimation. To investigate the model’s goodness of fit, several statistics were used: Overall χ², root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker Lewis index (TLI), and the standardized root mean square residual (SRMSR).

For the RMSEA, values close to .08 or smaller are recommended, whereas for the CFI, GFI, and TLI, values close to .95 or greater are recommended (Brown & Moore, 2012; Byrne, 2012). And if all factor loadings were statistically significant and salient (>.30), the factorial validity was supported (McKenzie et al., 2021). For chi-square statistic, a well-fitting factor solution should have a non-significant χ² statistic, but because the test is sensitive to sample size, we considered χ² adjusted for the degree of freedom (χ²/df) that should be at most five (Byrne, 2012; Kline, 2010). When the fit was poor, we used the LM (Lagrange Multiplier) test to detect improvements in model fit (Awang et al., 2015; Collier, 2020).

Two independent samples were obtained for the EFA and CFA, respectively. Only observations without missing items were used, resulting in 1,582 observations in total, 878 for the EFA and 704 for the CFA.

Internal Reliability

Internal consistency was assessed by calculating the Cronbach’s-α coefficient (Cronbach & Meehl, 1955) and Mean Inter-Item Correlation (MIIC). Internal consistency estimates of a magnitude of 0.70 or greater are considered satisfactory (Nunnally & Bernstein, 1994) while the recommended range of MIIC is 0.15 to 0.50 (Clark & Watson, 2016).

Concurrent Validity

Total scores and subscales of DVQ were tested on correlation with depression symptoms and mental well-being using the Pearson correlation coefficient.

Results

Sociodemographic Characteristics

Most of the participants in groups 1 and 2 were recruited from North Kivu (69.0%, resp. 75.1%). Regarding education, the rate of illiteracy in both group 1 (45.2%) and group 2 (50.1%) was high. Further, the sample in both group 1 (60.7%) and group 2 (58.8%) were dominated by women with 1 to 5 children (60.7% resp. 58.8%), followed by those with 6 to 10 children (33.0%, rep.33.3%). Polygamy was observed in both group 1 (14.1%) and group 2 (13.3%). The most reported types of relationships in group 2 were marriages and free unions (58.2%, resp. 41.8%), whereas these relationships were almost equally distributed among participants from group 1 (50.3%, resp. 49.7%) (Table 1).

Table 1.

Demographic Characteristics (n = 1,582).

Variables		Group 1 (N = 704)	Group 2 (N = 878)
		N (%)	N (%)
1. Province	North-Kivu	486 (69.0%)	659 (75.1%)
1. Province	South-Kivu	218 (31.0%)	219 (24.9%)
2. Study	Yes	386 (54.8%)	438 (49.9%)
2. Study	No	318 (45.2%)	440 (50.1%)
3. Number of children with an actual partner	1–5	427 (60.7%)	516 (58.8%)
	6–10	232 (33.0%)	292 (33.3%)
	0	27 (3.8%)	50 (5.7%)
	11–15	17 (2.4%)	19 (2.2%)
	16–20	2 (0.2%)	1 (0.1%)
4. Live with your partner	Live at home	683 (97.0%)	859 (97.8%)
	Periodic visit	20 (2.8%)	15 (1.7%)
	Lives elsewhere	1 (0.1%)	4 (0.5%)
5. Polygamy	No	599 (85.1%)	753 (85.8%)
	Yes	99 (14.1%)	117 (13.3%)
	Prefer not to say	6 (0.9%)	8 (0.9%)
6. Types of relationship	Free union	354 (50.3%)	367 (41.8%)
6. Types of relationship	Marriage (civil, religious and traditional)	350 (49.7%)	511 (58.2%)

Factor Analyses

The results indicated that the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.920 and Bartlett’s test of sphericity was significant (χ² = 9203.293, df = 190, Sig. < .001), indicating that the data were amenable to factor analysis.

Exploratory Factors Analyses

In the original analysis performed by the tool developers (Indu et al., 2011), 20 items clustered in five-five factors were retained. We performed a similar exploratory factor analysis with orthogonal rotations on our Group1 data set, which resulted in an uninterpretable four clusters of variables. For comparative purposes, we restricted items to load in five factors that resulted in five clusters of variables, explaining 66% of the variance, which could be approximately interpreted in the same way as the original factor model. Next, items factor loadings of at least .40 with “no cross-loading” were retained, resulting in a form with 18 items (Table 2). Items 7 and 12 were removed from the questionnaire due to their cross-loadings.

Table 2.

Five-Factor Model Exploratory Factor Analyses.

DVQ Items	Physical violence	Economic restriction	Neglecting behaviors with insults	Unfaithful behaviors	Social interaction restriction
1. Did not permit to meet/interact with female friends					.770
2. Restricted interaction with your family members					.789
3. Did not permit to handle money		.829
4. Did not permit to choose/buy things		.791
8. Did not allow you to partake in decision-making		.722
5. Irritated/suspicious/angry if you talked to other men				.786
6. Accused you of being unfaithful				.701
7. Treated you like a servant			.307	.319
9. He kept away from home for days or weeks without informing you/giving you money			.668
10. He was unfaithful to you/had extra-marital relationships			.561
11. Did not react against his relatives/agreed with his relatives, when they insulted you			.549
12. Insulted you in front of others	.457		.439
19. Ignored you purposely, by not having sexual intercourse with you for weeks			.637
20. Had sexual intercourse with you forcibly, when you were not interested			.727
13. Threatened to harm you physically (PV)	.755
14. Slapped you (PV)	.778
15. Beat you on other body parts	.844
16. Twisted your arm/pulled your hair	.872
17. Kicked you/dragged you	.903
18. Choked you or inflicted burns on you	.719

Note. The red color indicates the items removed.

To further explore the structure of the DVQ, we performed Principal Axis factoring with Oblimin rotation which resulted in three clusters of variables, which were easily interpretable: as physical violence (items 13, 14, 15, 16, 17, and 18), socio-economic restrictions (item 2, 3, 4, and 8) and insulting behaviors (item 5, 6, 7, 9, 10, 11, 12, 19, and 20), explaining 66% of the variance. The unfaithful behaviors from the original five-factor models did fully become part of the “insulting behavior factor in the 3-factor model.” Item 1 with cross-loading item 12 with poor loading were removed from the scale, thus the final form was composed of 18 items (Table 3).

Table 3.

Three Factor Model of DVQ.

DVQ Items	Physical violence	Socio-economic restrictions	Insulting behaviors	4
1. Did not permit to meet/interact with female friends		.454		.301
2. Restricted interaction with your family members		.493
3. Did not permit to handle money		.877
4. Did not permit to choose/buy things		.819
8. Did not allow you to partake in decision-making		.476
5. Irritated/suspicious/angry if you talked to other men			.408
6. Accused you of being unfaithful			.446
7. Treated you like a servant			.455
9. He kept away from home for days or weeks without informing you/giving you money			.720
10. He was unfaithful to you/had extra-marital relationships			.645
11. Did not react against his relatives/agreed with his relatives, when they insulted you			.506
12. Insulted you in front of others*	.341		.454
19. Ignored you purposely, by not having sexual intercourse with you for weeks			.40
20. Had sexual intercourse with you forcibly, when you were not interested			.565
13. Threatened to harm you physically	.697
14. Slapped you	.729
15. Beat you on other body parts	.846
16. Twisted your arm/pulled your hair	.886
17. Kicked you/dragged you	.930
18. Choked you or inflicted burns on you	.647

Note. The asterisk indicates the items removed.

Confirmatory Factor Analyses

Confirmatory factor analysis was performed on the Group 2 data and we tested all three models identified in the EFA: three-factor (18 items), five-factor (18 items), and five-factor (20 items). As the items showed acceptable skewness and kurtosis, we used a maximum likelihood estimation model with standardized coefficients. Observations with missing values were excluded. After estimating the models, goodness-of-fit statistics were obtained for all models.

Our results showed that all standard indices suggested an acceptable fit of the original model (i.e., five factors, 20 items): χ² (151) = 501.064, p = .000, χ² adjusted for degrees of freedom (χ²/df) was 3.318, the Goodness of Fit Index (GFI) was .935, the root mean square error of approximation (RMSEA) was .057, the comparative fit index (CFI) was .950, and the Tucker–Lewis index (TLI) was .937. Some of the indices (GFI and TLI) were below the recommended thresholds, even though they were close, indicating that the model did not have a good fit. The factor loadings of this model ranged from 0.53 to 0.938 (except for items 19, 1, and 8 with 0.396, 0.448, and 0.48 respectively).

The new five-factor models (18 items) demonstrated a good fit for all standard indices (χ² [114] = 324.903, p = .000, χ²/df = 2.850, GFI = .951, CFI = .965, RMSEA = .050, TLI = .953) and the three-factor model (χ² [119] = 314.142, p = .000, χ²/df = 2.640, GFI = .947, CFI = .968, RMSEA = .048, TLI = .959).

However, the three-factor model with 18 items had the best fit compared to the other two models with factor loadings ranging from .53 to .946 (except item 19, item 18 and item 5 with 0.50, 0.50, and 0.521 respectively). The factor loadings for the new five-factor model (18 items) fluctuated from .57 to 91 (except item 19 with 0.43), Table 4.

Table 4.

Factor Loadings from Confirmatory Factor Analyses.

DVQ items	Original five-factor, DVQ	Three-factor, DVQ	New five-factor, DVQ
Item 1	.448	NA	.683
Item 2	.50	.53	.653
Item 3	.879	.662	.886
Item 4	.833	.608	.822
Item 5	.458	.50	.639
Item 6	.583	.579	.735
Item 7	.63	.655	NA
Item 8	.481	.56	.478
Item 9	.731	.612	.619
Item 10	.744	.625	.634
Item 11	.568	.554	.6
Item 12	.792	NA	NA
Item 13	.865	.855	.878
Item 14	.869	.871	.828
Item 15	.938	.946	.843
Item 16	.732	.739	.757
Item17	.715	.722	.685
Item 18	.498	.521	.53
Item 19	.396	0.4	.422
Item 20	0.54	.50	.594

Note. NA: “Not Applicable” because the item was removed.

Reliability

Reliability was estimated using the Cronbach’s alpha and Mean inter-item correlation (MIIC) for the different factor models. As shown in Table 5, the original DVQ had Cronbach’s alpha of α = .908 and MIIC = .33 and subscales ranged from α = .65 to .926 and the MIIC range is .327 to .692. For the new 5-factor model (18-items), Cronbach’s alpha was α = .89 and MIIC = .32; while subscales ranged from α = .70 to .926 and MIIC range is .38 to .67. For the new 3-factor model (18-items), Cronbach’s alpha was α = .90 (MIIC = .32) and subscales range from α = .75 to .926 and the MIIC range is .34 to .67.

Table 5.

Internal Consistency of the DVQ Using Cronbach’s Alpha and MIIC Scores.

18-item three-factor model**	18-item five-factor model*	Original 20-item five-factor model
Total scale (α = .90, MIIC = .32)	Total scale (α = .89, MIIC = .32)	Total scale (α = .908, MIIC = .33)
1. Physical violence (α = .926, MIIC = .674), n = 6	1. Physical violence (α = .926, MIIC = .67), n = 6	1. Physical violence (α = .926, MIIC = .67), n = 6
2. Socio-economic restrictions (α = .81, MIIC = .34), n = 4	2. Economic restriction (α = .77, MIIC = .52), n = 3	2. Economic restriction (α = .82, MIIC = .69), n = 2
3. Unfaithful behaviors with Insults (α = .75, MIIC = .43), n = 8	3. Neglecting behaviors with insults (α = .75, MIIC = .38), n = 5	3. Controlling behaviors (α = .66, MIIC = .327), n = 4
	4. Unfaithful behaviors (α = .70, MIIC = .50), n = 2	4. Insulting behaviors (α = .741, MIIC = .364), n = 5
	5. Social Interaction restriction (α = .71, MIIC = .54), n = 2	5. Neglecting behaviors (α = .65, MIIC = .38) n = 3

Note. n = number of items.

Items 7 and 12 were removed.

Items 1 and 12 were removed.

As shown in Table 6, the corrected item-total correlation (CITC) was above .4 for all items except item 1 and below .75 for all items following the recommended range for this measurement, indicating the good relationship of items within the construct.

Table 6.

Descriptive Statistics and Item-Total Correlation of 20-Item DVQ.

DVQ items	CITC	M (SD)	α if item deleted
Item 1	.391	2.88 (2.52)	.904
Item 2	.412	2.43 (2.27)	.903
Item 3	.415	3.16 (2.47)	.903
Item 4	.40	2.92 (2.39)	.903
Item 5	.457	3.74 (2.68)	.902
Item 6	.537	2.30 (2.14)	.899
Item 7	.566	2.98 (2.47)	.899
Item 8	.414	3.54 (2.52)	.903
Item 9	.515	2.37 (2.12)	.900
Item 10	.536	2.51 (2.18)	.899
Item 11	.586	2.60 (2.11)	.898
Item 12	.698	2.57 (2.26)	.895
Item 13	.738	2.53 (2.19)	.894
Item 14	.695	2.76 (2.31)	.895
Item 15	.745	2.46 (2.18)	.894
Item 16	.668	1.97 (1.85)	.897
Item17	.670	2.12 (1.98)	.896
Item 18	.484	1.49 (1.43)	.901
Item 19	.485	2.73 (2.21)	.901
Item 20	.516	2.95 (2.41)	.900

Note.α = Cronbach’s alpha; CITC = corrected item-total correlation.

Concurrent Validity

To assess the concurrent validity of the three DVQ models, we analyzed correlations between the scores in the subscales of the DVQ and depression symptoms, and mental wellbeing. The existence of statistically significant correlations was verified and confirmed between all measurements proposed in the direction expected, that is, negatively correlated with mental well-being, and positively with depression symptoms (Table 7).

Table 7.

Inter-Correlation Between Three Factor Model, Depression and Mental Wellbeing.

DVQ total scales and subscales	Depression symptoms	Mental wellbeing
Three-factor model (18 items)
Total scale	.435**	−.230**
Physical violence	.363**	−.157**
Socio-economic restriction	.282**	−.152**
Insulting behaviors with unfaithful	.391**	−.234**
New 5-factor model (18 items)
Total scale	.432**	−.219**
Physical violence	.363**	−.157**
Economic restriction	.244**	−.160**
Unfaithful behavior	.271**	−.095**
Neglecting behaviors with insults	.354**	−.251**
Social interaction restriction	.260**	−.153**
Original 5-factor model (20 items)
Total scale	.434**	−.228**
Physical violence	.363**	−.157**
Controlling behavior	.342**	−.115**
Economic restriction	.194**	−.141**
Neglecting behavior	.329**	−.246**

p < .001.

Discussion

To the best of our knowledge, this the first study to evaluate the psychometric properties of the Swahili version of the Domestic Violence questionnaire (DVQ) among female survivors of domestic violence in DRC. These women had partners (men) who were perceived by community members and local leaders as using violence at home against their wives and children. We tested the psychometric characteristics of the DVQ (Indu et al., 2011) in the DRC and present (i) psychometric evidence of the validity and reliability of the original 20-item DVQ in Swahili; (ii) an improved 5-factor 18-item version of the DVQ that resolves model fit issues of the original DVQ and allows for clear interpretation of the factors; and (iii) an improved 3-factor 18-item version of the DVQ that presents the best model fit and allows for clear interpretation of the factors.

We performed exploratory factor analysis to verify whether the factor structure from the original analysis would also emerge from our data. This was not the case as this analysis lead to an uninterpretable 4-factor model. We then forced the exploratory model in 5 factors (as in the original model), but also explored other factor models; including a 3-factor model. After analysis, it turned out that the 3-factor model was easily interpretable and produced the best model fit for our data set. The three-factor solutions accounted for 66% of the total variance of women’s exposure to domestic violence. As suggested by Pettet al. (2003), it is desirable to have factor solutions explaining at least 50% of the total variance; and thus, these three identified factors adequately explained the women’s experience of domestic violence. We then went ahead with a CFA on an independent data set, with data collected from the same target population. Using fit estimations based on maximum likelihood estimation, the CFA has verified the adequacy of the five-correlated factor model (χ²/df = 3.318, GFI = .935, RMSEA = .057, CFI = .950, TLI = .937) proposed by tool developers (Indu et al., 2011), the new five (χ²/df = 2.850, GFI = .951, CFI = .965, RMSEA = .050, TLI = .953) and the new three factor models (χ²/df = 2.640, GFI = .947, CFI = .968, RMSEA = .048, TLI = .959). Using Brown’s (2015) recommendations of values of RMSEA close or less to .08 and CFI close to .95 or greater and TLI close to .95 or greater, we see that the new five (18 items) and three-factor (18 items) models both have a good fit (with the three-factor model having the best fit). However, the original model (20 items) does not have a good fit; even though the values come close to the recommended levels.

Furthermore, our findings indicate that the factor structures differ from those of the DVQ hypothesized in development and validated among Indian women aged 18 to 55 years (Indu et al., 2011). For instance, the item 8 “Did not allow you to partake in decision-making,” loaded on a factor related to the financials or economy in Congolese context, but in the Indian context, it belonged to the factor related to neglect (Indu et al., 2011). This may also explain why the items linked to insulting behaviors and neglecting behaviors in the Indian study are loading in one and the same factor in the DRC study. Also, four items composed of the factor “controlling behavior” were emerged into distinct factors “unfaithful behaviors” (did not permit to meet/interact with female friends, restricted interaction with your family members) and “social interaction restriction” (Irritated/suspicious/angry if you talked to other men, accused you of being unfaithful) in this study. A number of reasons could explain the differences in the factor structure in the India and DRC study, including cultural differences, education level and stigma related to reporting violence (Jonas et al., 2022; Kelly et al., 2011; Murray et al., 2018).

Reliability analysis also showed DVQ presented excellent values for internal consistency. All the item-total correlation coefficients were above .2, which showed good discriminating ability (Chen et al., 2019). Moreover, The Cronbach’s α coefficient of the total scale was .91 and that values of each subscales were α = .70 to .93, which are similar and even, in some cases, superior to those obtained in the original study on the DVQ. Regarding concurrent validity, this study presents new results about the relationship between domestic violence as assessed by DVQ and different variables of interest. A direct positive relationship was validated between depression symptoms and DVQ scores, congruent with prior findings, thus providing support for the internal validity of the DVQ (Andersen et al., 2022; Calvete et al., 2007; Sharma et al., 2021). Second, mental wellbeing presented significant negative correlations with DVQ scores (Mootz et al., 2020). These results are congruent in sign and size with findings described in previous literature (Calvete et al., 2007; Mootz et al., 2020; Sharma et al., 2021), thus providing support for the external validity of DVQ.

Study Strengths and Limitations

The main strength of the study is the large sample size (i.e., 1,582 participants). The study also involved a highly relevant target group as the participants were women whose partners were perceived to be violent in their families by other community members. Finally, the study took place in a high conflict setting, where it is difficult to get data from, but where needs are high as we know that societal conflict also increases domestic violence. The main limitation of this study is the lack of test-retest reliability.

It will be necessary to perform additional psychometric validation studies of the 18-item DVQ in different contexts and with different target populations to further evidence the reliability and validity of the tool; and include comparative concurrent validation with other well validated domestic violence tools.

Conclusion

The results showed that the original factor structure of the DVQ didn’t fit with our data set. However, when analyzing the data, a 3-factor model emerged that was clearly interpretable and delivered excellent psychometric properties. It can be concluded that the 18-item Swahili version of the domestic violence questionnaire is valid and reliable for evaluating physical and sexual violence, but also more invisible forms of violence as psychological and economical violence among women exposed to intimate partner violence in DRC. It thus gives information regarding the presence and nature of violence in couple relationships of Congolese women from a large sample, allowing its comparison with findings produced in India and elsewhere. Therefore, we promote the use of the DVQ as a reliable and valid tool to measure domestic violence in DRC. We also promote the use of the DVQ in different settings, but suggest performing psychometric analysis in such cases, as our study has demonstrated that the factor structure can be different in different contexts.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440241255440 – Supplemental material for Psychometric Validation of the Domestic Violence Questionnaire in Swahili Among Congolese Female Survivors of Domestic Violence

Supplemental material, sj-docx-1-sgo-10.1177_21582440241255440 for Psychometric Validation of the Domestic Violence Questionnaire in Swahili Among Congolese Female Survivors of Domestic Violence by Stefan Jansen, Japhet Niyonsenga, Epaphrodite Nsabimana, Mihigo Bonaventure, Eugene Rutembesa, Henny Slegh and Jean Mutabaruka in SAGE Open

Footnotes

Acknowledgements

The authors would like to thank all the participants who voluntarily agreed to participate in this study. Their patience and willingness to contribute to the study is greatly appreciated.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study is funded by a registered charity in England and Wales that finds solutions to complex humanitarian problems through research and innovation (ELRHA) which means Enhancing Learning and Research for Humanitarian Assistance (Reference number: #32443).

Ethical Consideration

The ethical clearance was obtained from the Institute Review Board of the University of Rwanda, College of Medicine and Health Sciences in Rwanda (No. 157/CMHS IRB/2019); from Commission d’éthique de l’ Institut Supérieur du Lac à Goma (No. 059/ISL/PCE/MB/2018).

ORCID iDs

Japhet Niyonsenga

Epaphrodite Nsabimana

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Material

Supplemental material for this article is available online.

References

Andersen

Rossi

Nyamkume

P. K.

Hubloue

(2022). Mental health and psychosocial support for the war-wounded: A retrospective cohort study from the Democratic Republic of Congo, Mali and Nigeria. PLoS One, 17(5), e0268737. https://doi.org/10.1371/journal.pone.0268737

Awang

Wan Afthanorhan

W. M. A.

Asri

M. A. M.

(2015). Parametric and non parametric approach in Structural Equation Modeling (SEM): The application of bootstrapping. Modern Applied Science, 9(9), 58–67. https://doi.org/10.5539/mas.v9n9p58

Bahati

Izabayo

Niyonsenga

Sezibera

Mutesa

(2021). Intimate partner violence as a predictor of antenatal care services utilization in Rwanda. BMC Pregnancy and Childbirth, 21(1), 1–12. https://doi.org/10.1186/s12884-021-04230-5

Basile

K. C.

Hertz

M. F.

Black

S. E.

(2007). Intimate partner violence and sexual violence victimization assessment instruments for use in healthcare settings. Version 1. Centers for Disease Control and Prevention (CDC).

Bronte

T. D.

(2020). Sexual violence in the DRC: The stereotype of “weapon of war” and its dangerous consequences (Issue January). Instituto Español de Estudios Estratégicos.

Brown

T. A.

Moore

M. T.

(2012). Confirmatory factor analysis. In Hoyle

R. H.

(Ed.), Handbook of structural equation modeling. Guilford Press.

Brown

T. A.

(2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press.

Byrne

B. M.

(2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge.

Calvete

Corral

Estévez

(2007). Cognitive and coping mechanisms in the interplay between intimate partner violence and depression. Anxiety, Stress, & Coping, 20(4), 369–382. https://doi.org/10.1080/10615800701628850

10.

Chen

Yang

Zhao

Zheng

Xiao

(2019). Psychometric validation of the Chinese version of the Second Victim Experience and Support Tool (C-SVEST). Journal of Nursing Management, 27(7), 1416–1422. https://doi.org/10.1111/jonm.12824

11.

Cheung

Leung

Tsui

(2009). Asian male domestic violence victims: Services exclusive for men. Journal of Family Violence, 24(7), 447–462. https://doi.org/10.1007/s10896-009-9240-9

12.

Clark

L. A.

Watson

(2016). Constructing validity: Basic issues in objective scale development. In Kazdin

A. E.

(Ed.), Methodological issues and strategies in clinical research (4th ed., pp. 187–203). American Psychological Association. https://doi.org/10.1037/14805-012

13.

Clarke

Friede

Putz

Ashdown

Martin

Blake

Adi

Parkinson

Flynn

Platt

Stewart-Brown

(2011). Warwick-Edinburgh Mental Well-being Scale (WEMWBS): Validated for teenage school students in England and Scotland. A mixed methods assessment. BMC Public Health, 11(1), 487. https://doi.org/10.1186/1471-2458-11-487

14.

Collier

J. E.

(2020). Applied structural equation modeling using Amos: Basic to advanced techniques. Routledge.

15.

Cook

S. L.

Conrad

Bender

Kaslow

N. J.

(2003). The internal validity of the index of spouse abuse in African American Women. Violence and Victims, 18(6), 641–657. https://doi.org/10.1891/vivi.2003.18.6.641

16.

Cronbach

L. J.

Meehl

P. E.

(1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957

17.

Davis

Fabbri

Alphonse

I. M.

(2014). Profil du pays en matière d’égalité de genre 2014 de la RDC. https://www.lauradavis.eu/wp-content/uploads/2014/07/Profil-genre-2014-RDC.pdf

18.

Derogatis

L. R.

Lipman

R. S.

Rickels

Uhlenhuth

E. H.

Covi

(1974). The Hopkins symptom checklist (HSCL) (pp. 79–110). https://doi.org/10.1159/000395070

19.

DRC Ministry of Planning. (2008). Demographic and Health Survey 2007. Macro International.

20.

Dubowitz

Prescott

Feigelman

Lane

Kim

(2008). Screening for intimate partner violence in a pediatric primary care clinic. Pediatrics, 121(1), e85–e91. https://doi.org/10.1542/peds.2007-0904

21.

Ernst

A. A.

Weiss

S. J.

Cham

Hall

Nick

T. G.

(2004). Detecting ongoing intimate partner violence in the emergency department using a simple 4-question screen: The OVAT. Violence and Victims, 19(3), 375–384. https://doi.org/10.1891/vivi.19.3.375.65769

22.

Glass

Ramazani

Tosha

Mpanano

Cinyabuguma

(2012). A congolese–US participatory action research partnership to rebuild the lives of rape survivors and their families in eastern Democratic Republic of Congo. Global Public Health, 7(2), 184–195. https://doi.org/10.1080/17441692.2011.594449

23.

Global Centre for the Responsability to Protect. (2022). Democratic Republic of the Congo: Various armed groups in the Democratic Republic of the Congo continue to perpetrate crimes against humanity, resulting in the largest displacement crisis in Africa. https://www.globalr2p.org/countries/democratic-republic-of-the-congo/

24.

Hegarty

O’Doherty

Taft

Chondros

Brown

Valpied

Astbury

Taket

Gold

Feder

Gunn

(2013). Screening and counselling in the primary care setting for women who have experienced intimate partner violence (WEAVE): A cluster randomised controlled trial. The Lancet, 382(9888), 249–258. https://doi.org/10.1016/S0140-6736(13)60052-5

25.

Imanishimwe

M. J.

Machakanja

Adjei

N. K.

(2020). Trends in prevalence and correlates of intimate partner violence against women in Zimbabwe, 2005–2015. BMC International Health and Human Rights, 20(1), 2. https://doi.org/10.1186/s12914-019-0220-8

26.

Indu

Remadevi

Vidhukumar

Anilkumar

Subha

(2011). Development and validation of the domestic violence questionnaire in married women aged 18–55 years. Indian Journal of Psychiatry, 53(3), 218. https://doi.org/10.4103/0019-5545.86811

27.

International Institute for Population Sciences. (2007). National Family Health Survey (NFHS-3), 2005-06: India: Volume I (Vol. 18, Issue 7). International Journal of Health Care Quality Assurance. http://www.emeraldinsight.com/doi/abs/10.1108/ijhcqa.2005.06218gab.007

28.

Iverson

K. M.

King

M. W.

Gerber

M. R.

Resick

P. A.

Kimerling

Street

A. E.

Vogt

(2015). Accuracy of an intimate partner violence screening tool for female VHA patients: A replication and extension. Journal of Traumatic Stress, 28(1), 79–82. https://doi.org/10.1002/jts.21985

29.

Jansen

Nsabimana

Kagaba

Mutabaruka

Rutembesa

Slegh

Mihigo

Mahwa

Ruratotoye

Haile

. (2022). A clustered randomized controlled trial to assess whether Living Peace Intervention (LPint) reduces domestic violence and its consequences among families of targeted men in Eastern Democratic Republic of the Congo (DRC): Design and methods. Evaluation and Program Planning, 95, 102154. https://doi.org/10.1016/j.evalprogplan.2022.102154

30.

Jonas

K. A. W. M.

Tang

Deng

Jean-Baptiste

K. N.

Basile

K. K.

Opoku

Meng

(2022). Prevalence and risk factors associated with physical and/or sexual abuse among female middle school students: A cross-sectional study in Kinshasa, DRC. Journal of Interpersonal Violence, 37(11–12), NP8405–NP8429. https://doi.org/10.1177/0886260520976221

31.

Kapiga

Harvey

Muhammad

A. K.

Stöckl

Mshana

Hashim

Hansen

Lees

Watts

(2017). Prevalence of intimate partner violence and abuse and associated factors among women enrolled into a cluster randomised trial in northwestern Tanzania. BMC Public Health, 17(1), 190. https://doi.org/10.1186/s12889-017-4119-9

32.

Kelly

J. T.

Betancourt

T. S.

Mukwege

Lipton

VanRooyen

M. J.

(2011). Experiences of female survivors of sexual violence in eastern Democratic Republic of the Congo: A mixed-methods study. Conflict and Health, 5(1), 25. https://doi.org/10.1186/1752-1505-5-25

33.

Kelly

J. T. D.

Colantuoni

Robinson

Decker

M. R.

(2018). From the battlefield to the bedroom: A multilevel analysis of the links between political conflict and intimate partner violence in Liberia. BMJ Global Health, 3(2), e000668. https://doi.org/10.1136/bmjgh-2017-000668

34.

Kelly

J. T. D.

Holmes

Gibbons

Matabaro

Voors

(2021, October 3–16). Conflict, displacement and overlapping vulnerabilities understanding risk factors for gender-based violence among displaced women in Eastern Democratic Republic of Congo. http://www.worldbank.org/prwp

35.

Kline

(2010). Principles and practice of structural equation modeling. The Guilford Press.

36.

Kumar

Jeyaseelan

Suresh

Ahuja

R. C.

(2005). Domestic violence and its mental health correlates in Indian women. British Journal of Psychiatry, 187(1), 62–67. https://doi.org/10.1192/bjp.187.1.62

37.

Le Roux

Corboz

Scott

Sandilands

Lele

U. B.

Bezzolato

Jewkes

. (2020). Engaging with faith groups to prevent VAWG in conflict-affected communities: Results from two community surveys in the DRC. BMC International Health and Human Rights, 20(1), 27. https://doi.org/10.1186/s12914-020-00246-8

38.

MacMillan

H. L.

(2006). Approaches to screening for intimate partner violence in health care settings. JAMA, 296(5), 530. https://doi.org/10.1001/jama.296.5.530

39.

Mason

O’Rinn

S. E.

(2014). Co-occurring intimate partner violence, mental health, and substance use problems: a scoping review. Global Health Action, 7(1), 24815. https://doi.org/10.3402/gha.v7.24815

40.

McKenzie

Metcalf

D. A.

Saliba

(2021). Validation of the Food Technology Neophobia Scale in a Chinese sample using exploratory and confirmatory factor analysis. Food Quality and Preference, 89, 104148. https://doi.org/10.1016/j.foodqual.2020.104148

41.

Ministère du Plan et Suivi de la Mise en œuvre de la Révolution de la Modernite Ministère de la Santé Publique, ICF International. (2014). Democratic Republic of Congo Demographic and Health Survey 2013–14: Key findings. Demographic and Health Survey, 21.

42.

Mootz

J. J.

Stabb

S. D.

Carlson

Helpman

Onyango Mangen

Wainberg

M. L.

(2020). Why place and space matter for intimate partner violence survivors’ mental wellbeing and communities in Northeastern Uganda. Women & Health, 60(9), 975–986. https://doi.org/10.1080/03630242.2020.1784366

43.

Murray

S. M.

Robinette

K. L.

Bolton

Cetinoglu

Murray

L. K.

Annan

Bass

J. K.

(2018). Stigma among survivors of sexual violence in Congo: Scale development and psychometrics. Journal of Interpersonal Violence, 33(3), 491–514. https://doi.org/10.1177/0886260515608805

44.

Nunnally

J. C.

Bernstein

I. H.

(1994). Psychometric theory (3rd ed.). McGraw-Hill, Inc.

45.

Paranjape

Liebschutz

(2003). STaT: A three-question screen for intimate partner violence. Journal of Women’s Health, 12(3), 233–239. https://doi.org/10.1089/154099903321667573

46.

Parmar

Agrawal

Greenough

P. G.

Goyal

Kayden

(2012). Sexual violence among host and refugee population in Djohong District, Eastern Cameroon. Global Public Health, 7(9), 974–994. https://doi.org/10.1080/17441692.2012.688061

47.

Peterman

Palermo

Bredenkamp

(2011). Estimates and determinants of sexual violence against women in the Democratic Republic of Congo. American Journal of Public Health, 101(6), 1060–1067. https://doi.org/10.2105/AJPH.2010.300070

48.

Pett

M. A.

Lackey

N. R.

Sullivan

J. J.

(2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Sage.

49.

Sharma

Kahle

Sullivan

Stephenson

(2021). Sexual agreements and intimate partner violence among male couples in the U.S.: An analysis of dyadic data. Archives of Sexual Behavior, 50(3), 1087–1105. https://doi.org/10.1007/s10508-020-01783-y

50.

Slegh

Barker

Levtov

(2014). Gender relations, sexual and gender-based violence and the effects of conflict on men and women in North Kivu, Eastern Democratic Republic of Congo: Results from the International Men and Gender Equality Survey (IMAGES). Promundo-US and Sonke Gender Justice. https://promundoglobal.org/wp-content/uploads/2015/01/Preliminary-Results-from-IMAGES-DRC.pdf

51.

Sohal

Eldridge

Feder

(2007). The sensitivity and specificity of four questions (HARK) to identify intimate partner violence: A diagnostic accuracy study in general practice. BMC Family Practice, 8(1), 49. https://doi.org/10.1186/1471-2296-8-49

52.

Steiner

Benner

M. T.

Sondorp

Schmitz

K. P.

Mesmer

Rosenberger

(2009). Sexual violence in the protracted conflict of DRC programming for rape survivors in South Kivu. Conflict and Health, 3(1), 3. https://doi.org/10.1186/1752-1505-3-3

53.

Tiwari

Fong

D. Y. T.

Yuen

K. H.

Yuk

Pang

Humphreys

Bullock

(2010). Effect of an advocacy intervention on mental health in chinese women survivors of intimate partner violence. JAMA, 304(5), 536. https://doi.org/10.1001/jama.2010.1052

54.

Tlapek

S. M.

(2015). Women’s status and intimate partner violence in the Democratic Republic of Congo. Journal of Interpersonal Violence, 30(14), 2526–2540. https://doi.org/10.1177/0886260514553118

55.

Wathen

C. N.

Jamieson

MacMillan

H. L.

(2008). Who is identified by screening for intimate partner violence? Women’s Health Issues, 18(6), 423–432. https://doi.org/10.1016/j.whi.2008.08.003

56.

Weiss

S. J.

Ernst

A. A.

Cham

Nick

T. G.

(2003). Development of a screen for ongoing intimate partner violence. Violence and Victims, 18(2), 131–141. https://doi.org/10.1891/vivi.2003.18.2.131

57.

White

J. W.

Smith

P. H.

Koss

M. P.

Figueredo

A. J.

(2000). Intimate partner aggression—what have we learned? Comment on Archer (2000). Psychological Bulletin, 126(5), 690–696. https://doi.org/10.1037/0033-2909.126.5.690

58.

WHO. (2010). Preventing intimate partner and sexual violence against women: Taking action and generating evidence. https://apps.who.int/iris/handle/10665/44350

59.

WHO. (2013). Responding to intimate partner violence and sexual violence against women. www.who.int/reproductivehealth

60.

WHO. (2019). Violence against women: Intimate partner and sexual violence against women: Evidence brief. World Health Organization.

61.

WHO. (2021). Violence against women prevalence estimates, 2018. World Report on Violence and Health.

62.

Zolotor

A. J.

Denham

A. C.

Weil

(2009). Intimate partner violence. Primary Care: Clinics in Office Practice, 36(1), 167–179. https://doi.org/10.1016/j.pop.2008.10.010

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB