553118
research-article2014
JIVXXX10.1177/0886260514553118Journal of Interpersonal ViolenceTlapek
Article
Women’s Status and
Intimate Partner
Violence in the
Democratic Republic of
Congo
Journal of Interpersonal Violence
2015, Vol. 30(14) 2526­–2540
© The Author(s) 2014
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DOI: 10.1177/0886260514553118
jiv.sagepub.com
Sarah Myers Tlapek1
Abstract
Women’s greatest risk of violence in the Democratic Republic of Congo
(DRC) may come from an intimate partner, but few studies have analyzed
context-specific risk and protective factors for intimate partner violence (IPV)
in the DRC. This study analyzed data from the most recent Demographic
and Health Survey (DHS) in Congo to assess risk and protective factors for
IPV and the role of women’s status, a factor implicated in prior IPV research.
Using a sample of 1,821 married or cohabiting women between the ages
of 15 and 49, four logistic regression models tested relationships between
physical, sexual, emotional, or any violence and independent variables of
interest. Results indicated that 68.2% of respondents had experienced at
least one of the three types of IPV. An attitude of acceptance toward spousal
violence was associated with increased risk for physical and emotional IPV.
Women who were the only wife of their husband were half as likely to
experience IPV compared with women whose husbands had other wives
or women who did not know their husbands’ marital status. Partner’s use
of alcohol was associated with nearly doubled risk for both physical and
sexual IPV. The study’s results indicate that IPV occurs frequently and is
justified as acceptable by many women in the DRC. Findings suggest that
1Washington
University in St. Louis, MO, USA
Corresponding Author:
Sarah Myers Tlapek, Washington University in St. Louis School of Social Work, 1 Brookings
Drive, Campus Box 1196, St. Louis, MO 63130, USA.
Email: [email protected]
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awareness-raising campaigns may be a helpful intervention and that partner
characteristics should be considered when assessing women’s risk for IPV.
Keywords
intimate partner violence, domestic violence, spousal violence, physical
violence, sexual violence, emotional violence, women’s status, risk and
protective factors, Demographic and Health Surveys, Democratic Republic
of Congo
Introduction
The threat women face from sexual violence in Democratic Republic of
Congo (DRC or Congo) is increasingly well-documented. Researchers
and non-governmental organizations (NGOs) report horrific violence
against women perpetrated by combatants and civilians in conflictaffected areas of the DRC, and the U.S. State Department recently named
gender-based violence as one of the top three human rights violations in
eastern DRC (Johnson et al., 2010; Kelly, Betancourt, Mukwege, Lipton,
& VanRooyen, 2011; U.S. Department of State, 2011). Forty percent of
women in a 2011 population-based survey reported sexual violence;
another study estimated that between 1.6 and 1.8 million Congolese
women have been raped in their lifetime (Johnson et al., 2010; Peterman,
Palermo, & Bredenkamp, 2011).
Recent research indicates that the greatest threat of violence women face
in the DRC may not be from strangers but from intimate partners in their
own homes (Peterman et al., 2011). According to the official report from the
2007 DRC Demographic and Health Survey (DHS), 35% of women experienced sexual intimate partner violence (IPV)—more than 3 million women
according to one estimate (DRC Ministry of Planning, 2008; Peterman et al.,
2011). When physical and emotional abuse were included, 71% of women in
DRC reported having experienced IPV in their lifetime (DRC Ministry of
Planning, 2008).
IPV against women is not only a violation of women’s rights but has serious consequences for developing countries such as the DRC. Women’s health
and security is threatened by IPV, and the country’s progress toward development goals is hindered (Bunch, 1997; DRC Ministry of Gender, Family, &
Children, 2009). Female victims of IPV may experience death, disability, and
poor health or mental health, and the woman’s household, community, and
society are affected (Ellsberg, Jansen, Heise, Watts, & Garcia-Moreno, 2008;
World Bank, 2009).
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Researchers often use an ecological risk and protective factor framework
to conceptualize IPV. The risk and protective factor framework presents perpetration of IPV as influenced by factors from different ecological levels (i.e.,
personal, situational, and sociocultural) and acknowledges the role of both
victim and perpetrator characteristics (Heise, 1998). The framework is a
pragmatic way to study the phenomenon of IPV and to identify potentially
modifiable factors to target for interventions and policy.
Research in multiple country settings has identified factors consistently
associated with IPV against women (Abramsky et al., 2011). Women’s status
is a factor of particular interest as women’s educational, social, and economic
empowerment seems to be generally protective against IPV, and countries
with higher gender equality and greater individualism report lower prevalence rates for intimate partner assaults on women (Archer, 2006; Jewkes,
2002; Vyas & Watts, 2009). Specific measures of women’s status such as
young age and attitudes supportive of wife-beating have been identified as
consistently significant risk factors for IPV in multiple settings (Abramsky et
al., 2011), and several characteristics of male partners have been consistently
significantly associated with IPV against women, such as partner’s alcohol
abuse and polygamy (Abramsky et al., 2011).
Experts in IPV emphasize that risk and protective factors are specific to
local context, and an understanding of the contextually relevant contributing
factors is important for developing effective interventions (Watts &
Zimmerman, 2002). However, in the DRC, little research exists on factors
associated with IPV against women. The role of women’s status on IPV has
not been tested empirically using national-level data, and risk and protective
factors specific to the DRC context have not been identified.
The aims of this study were to describe risk and protective factors for IPV
against women in the DRC and examine the relationship between women’s
status and IPV. The study sought to (a) assess the extent to which women’s
status (as measured by education, age at first marriage, and attitude toward
wife-beating) is associated with lifetime victimization from IPV, and (b)
examine the associations between IPV against women and other common
risk factors in the DRC context (i.e., age, alcohol use, polygamy, residence,
years married, poverty, and number of children). Findings may inform IPV
policies and programs in Congo.
Method
The study analyzed cross-sectional secondary data from the 2007 DHS in
Congo. DHSs have been conducted in more than 90 countries, funded by
United States Agency for International Development (USAID) with technical
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support from ICF International, using standardized questionnaires and methodologies. Surveys are conducted in partnership with host-country governments and statisticians and are approved by local Institutional Review Boards.
More information about questionnaires and sampling is available at http://
www.measuredhs.com and in the DRC DHS final report (DRC Ministry of
Planning, 2008). As this study involved anonymized data from a publicly
available data set, the Institutional Review Board of Washington University
in St. Louis classified it as outside the purview of review.
Participants and Sampling
Participants were married women living in the DRC, selected for the DHS via
a stratified multistage sampling strategy. The Congo DHS used a sampling
frame from the 1984 census to divide the country’s 10 provinces and the capital city, Kinshasa, into primary and secondary sampling units from which 300
clusters were selected. More than 500 data collectors were extensively trained
on the sampling and data collection procedures and the administration of
standardized questionnaires. Data collectors enumerated households and created a sampling frame to select 30 households in each cluster. These households were visited for an in-person interview, and 9,995 women between the
ages of 15 and 49 were interviewed at a response rate of 99% (DRC Ministry
of Planning, 2008). Multiple women per household were interviewed, but
only one woman per household was randomly selected to receive an optional
domestic violence module, creating a sub-sample of 3,436 women, 2,859 of
whom were married or cohabiting. Interviewers followed World Health
Organization (WHO) guidelines for domestic violence research to ensure the
safety and security of participants, such as only administering the domestic
violence module if total privacy could be obtained (WHO, 2001).
Measures
All variables included in the analysis were based on respondents’ self-report.
All instruments in the DHS survey are standardized measures intended for
comparison across multiple countries, and an extensive process is followed to
ensure reliability and validity of measures as questionnaires are adapted and
translated into the local language(s) for each specific country.
Dependent variables. Experience of IPV is assessed in the DHS using items
from the WHO’s modified version of the Conflict Tactics Scale (Kishor &
Johnson, 2006; Straus, Hamby, Boney-McCoy, & Sugarman, 1996). The four
dependent variables for this study were lifetime experience of (a) physical
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IPV, (b) sexual IPV, (c) emotional IPV, and (d) lifetime experience of at least
one of the three types of IPV. Women responded to a checklist of items
describing the actions of their current husband or partner. Physical violence
was assessed by the endorsement of seven possible actions (e.g., Does/did
your (last) husband/partner ever push you, shake you, or throw something at
you?). Emotional violence was assessed based on the response to two items:
(a) “Does/did your (last) husband/partner ever say or do something to humiliate you in front of others?” and (b) “threaten you or someone close to you?”
Sexual violence was assessed by asking “Does/did your (last) husband/partner ever (a) physically force you to have sexual intercourse with him even
when you did not want to? (b) force you to perform any sexual acts you did
not want to?” The DHS data set included dichotomous variables coded positively (=1) if the respondent had endorsed any of the items for each type of
violence. A new dichotomous dependent variable was created to measure the
experience of at least one type of IPV, coded positively if the respondent
endorsed any of the items from the physical, sexual, or emotional violence
scales.
Independent variables. Independent variables in the analysis included women’s status variables (level of education, age at first marriage, and attitude
toward the acceptability of wife-beating), covariates (partner’s alcohol use
and other wives), and demographic characteristics as control variables.
Women’s status. DHS survey questionnaires have included indicators of
women’s access to economic and social opportunities and resources since
1991, but the measurement of women’s status was not a primary goal of
the DHS studies, and the number of indicators is limited (Kishor & Neitzel,
1996). Measures of women’s status for the DHS were developed by a panel
of gender experts and designed to be relevant in a wide variety of cultures
(Kishor, 2005). For women’s education, respondents reported the highest
level of school attended (primary, secondary, or higher), and for this analysis, responses were coded into three possible choices: (a) no education, (b)
primary education, or (c) secondary or higher. Age at first marriage was calculated in the DHS data set as the difference between the date when a woman
reported first living with her husband and her date of birth.
Respondent’s attitude toward spousal violence was assessed using five
hypothetical scenarios. The survey questions were as follows: “Sometimes a
husband is annoyed or angered by things that his wife does. In your opinion,
is a husband justified in hitting or beating his wife in the following situations:
(a) If she goes out without telling him? (b) If she neglects the children? (c) If
she argues with him? (d) If she refuses to have sex with him? (e) If she burns
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the food?” Responses were originally coded as “yes,” “no,” or “don’t know.”
For this analysis, questions were recoded to create a dichotomous variable
found to have good psychometric properties in prior analyses (Hindin, 2014).
A woman was considered to have justified the acceptability of wife-beating if
she answered “yes” to one or more of the five questions. Respondents who
did not consider wife-beating acceptable in any of the five scenarios were
considered to be unaccepting of wife-beating. All “don’t know” values were
set to missing.
Covariates. Covariates were selected based on existing evidence, theoretical importance, and their association with IPV. Partner’s alcohol use was
measured based on one dichotomous question asking if the respondent’s partner drank alcohol. The survey assessed the possibility of the partner having multiple wives by asking the respondent if her partner had other wives
or women with whom he cohabited. Women responded with “yes,” “no,”
or “don’t know.” A binary variable was created to separate respondents into
those whose partner had no other wives and those who were uncertain about
or reported the presence of other wives.
Sociodemographic characteristics. Sociodemographic variables included
respondent’s age, residence (rural/urban), number of years married, poverty
status, and number of children. Respondent’s age was calculated in the DHS
data set based on self-reported date of birth. Residence was a binary variable
coded in the data set as urban or rural. Number of years married was a DHS
variable calculated from date of first marriage. Poverty status was based on
the DHS wealth quintiles. The DHS wealth variable uses data on asset ownership and household dwellings to produce normally distributed standardized asset scores which are broken into quintiles (poorest, poorer, middle,
richer, and richest; Rutstein & Johnson, 2004). This variable was recoded
into a dichotomous variable by separating respondents in the poorest quintile
from respondents in the other four wealth categories. Number of children was
measured by respondent’s report of the total number of children to whom
they have given birth. This variable was recoded into four categories: 0 children, 1 to 3 children, 4 to 6 children, and 7 or more children.
Data Analysis
To test the research questions, a series of four logistic regression models were
conducted. Logistic regression was selected for its ability to model a binary
outcome variable with both continuous and categorical predictors; the data
met the assumptions of logistic regression. Both women’s status factors and
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covariates were included in each of the four models, controlling for demographic characteristics. Descriptive analyses were conducted for all variables,
and continuous variables were tested for normality.
Bivariate analyses (chi-square and t tests) were conducted to test the association between women’s status variables, covariates, and IPV. A criterion for
inclusion of independent variables into the model was a significant bivariate
relationship with the dependent variable. Retained independent variables
(shown in Table 3) were regressed on IPV in four survey logistic regression
models: one for each type of IPV and one for any IPV. The models were
tested for multicollinearity, and the experience of IPV was modeled.
Descriptive and bivariate analyses did not include survey weights; however,
the multinomial logistic regression models accounted for the DHS’s stratified
cluster sampling design and weighting, using the survey command and the
weighting variable created for the DHS domestic violence module. All data
analysis was done with SAS 9.4.
Results
The analysis was restricted to presently married women who completed the
domestic violence module, were currently living with an intimate partner,
and had been married only once (n = 1,836). The sample was restricted in this
way because women reported on lifetime (rather than current) IPV—women
with prior marriages were excluded to reduce the likelihood that reported IPV
had been committed by someone other than the current partner. This also
ensured that partner variables measured characteristics of respondents’ current intimate partner. Of these respondents, data were analyzed only for those
with no missing data for all variables of interest (n = 1,821, 99%). This sample was compared with those with missing data; no significant differences
were found on any of the key study variables.
Table 1 presents indicators of women’s status for the sample and key characteristics of respondents’ partners. Seventy-eight percent of women reported
that their husband or partner had only one wife (i.e., the respondent). More
than one quarter of the sample had received no formal education, and 80% of
respondents justified wife-beating as acceptable in at least one hypothetical
scenario.
Of the women in the sample, 30% had experienced sexual violence from
an intimate partner in their lifetime, 41.4% had experienced emotional violence, and 53.6% had experienced physical IPV. Results indicated that 68.2%
of respondents experienced at least one of the three types of IPV in their
lifetime. Table 2 presents sociodemographic characteristics of respondents
who had experienced IPV compared with respondents who had never
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Table 1. Women’s Status and Partner Characteristics of the Sample (n = 1,821).
Partner factors
Alcohol use, n %
Partner drinks alcohol
Partner does not drink alcohol
Number of wives, n %
Respondent is the only wife
Partner has other wives or respondent unsure
Women’s status factors
Age at first marriage, M SD
Acceptability of wife-beating, n %
Wife-beating acceptable in at least one
scenario
Wife-beating acceptable in no scenarios
Education level, n %
No education
Primarya
Secondary or higherb
aSix
M or n
SD or %
872
949
47.9
52.1
1,426
395
78.3
21.7
18.0
3.7
1,465
80.5
356
19.5
495
745
581
27.2
40.9
31.9
years of education or less.
or more years of education.
bSeven
experienced IPV. Respondents who had experienced IPV were similar to
those who had not experienced IPV on nearly all sociodemographic characteristics except having children; respondents who had experienced IPV more
frequently had children.
Table 3 presents the results of four regression models examining the relationship among partner factors, women’s status factors, and different types of
IPV. IPV was more likely to be reported by women whose partners drank alcohol and less likely to be reported by women whose partners had only one wife,
controlling for sociodemographic characteristics. Respondents whose partners
drank alcohol were 1.6 times more likely to report at least one type of IPV, 1.9
times more likely to report emotional IPV, and 1.8 times more likely to report
physical IPV compared with respondents whose partners did not drink alcohol.
Respondents whose partners had only one wife were significantly less likely to
report IPV in all four models. Women who were the only wife of their husband
were 43% less likely to experience physical IPV or emotional IPV, 40% less
likely to experience sexual IPV, and 50% less likely to experience at least one
of the three types of IPV compared with women whose partners had multiple
wives or who did not know their partner’s marital status.
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Table 2. Demographic Characteristics of the Sample by IPV Experienced
(n = 1,821).
Characteristics
None
Age
Residence
Urban
Rural
Years married
Number of children ever born
(in categories)
0
1-3
4-6
7 or more
Wealth ranking
Poorest
Not poorest
Partner’s education level
No education
Primary educationa
Secondary or higher
educationb
Ever Experienced
IPV
30.2 (8.7)
29.4 (8.1)
216 (37.4)
361 (62.6)
11.1 (8.7)
493 (39.4)
751 (60.4)
11.2 (8.1)
56 (9.7)
258 (44.7)
144 (25.0)
119 (20.6)
79 (6.3)
557 (44.8)
380 (30.6)
228 (18.3)
120 (20.8)
457 (79.2)
291 (23.4)
953 (76.6)
64 (11.3)
164 (28.9)
339 (59.8)
128 (10.5)
342 (28.2)
743 (61.3)
p Value
0.078
0.371
0.925
0.010
0.218
0.818
Note. Data are in terms of M (SD) or n (%). p values are for bivariate tests (t test and χ2) of
differences between groups. IPV = intimate partner violence.
aSix years of education or less.
bSeven or more years of education.
Being older at the time of marriage was associated with a reduced likelihood of reporting IPV of at least one type but was not significantly associated
with any of the individual types of IPV when modeled alone. Respondents
who justified wife-beating as acceptable were 1.5 times more likely to report
physical IPV, 1.4 times more likely to report emotional IPV, and 1.6 times
more likely to report at least one type of IPV compared with respondents who
did not justify wife-beating as acceptable. Women’s education was not associated with experiencing IPV.
Discussion
This study’s descriptive results of women’s status were consistent with other
reports of women’s status in the DRC; women in the DHS survey reported
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Table 3. Logistic Regression Results for Physical, Sexual, Emotional, and Any IPV
(n = 1,821).
Variables
Demographics
Age
Physical IPV
Sexual IPV
Emotional IPV
Any IPV
OR (CI)
OR (CI)
OR (CI)
OR (CI)
1.00
[0.97, 1.02]
0.99
[0.96, 1.02]
1.36
[0.65, 2.84]
1.39
[0.69, 2.84]
1.01
[0.40, 2.55]
1.31
[0.59, 2.91]
1.87
[0.80, 4.38]
1.24
[0.48, 3.20]
1.92
[1.45, 2.54]***
0.57 [0.42,
0.76]***
1.62
[1.24, 2.10]***
0.50
[0.36, 0.69]***
0.99
[0.95, 1.04]
1.42
[1.05, 1.92]*
0.95
[0.91, 1.00]*
1.57
[1.12, 2.21]**
0.78
[0.53, 1.14]
0.73
[0.49, 1.07]
1.18
[0.73, 1.91]
1.26
[0.83, 1.91]
1.00
0.98
[0.97, 1.03]
[0.95, 1.02]
Number of children (ref. category = 0)
1-3
2.32
1.15
[1.38, 3.90]**
[0.61, 2.18]
4-6
2.47
1.74
[1.34, 4.54]**
[0.96, 3.18]
7 or more
1.82
1.90
[0.92, 3.61]
[0.84, 4.27]
Partner factors
Partner drinks
1.83
1.33
alcohol (yes)
[1.38, 2.43]***
[0.96, 1.84]
Partner has only
0.57
0.60
one wife
[0.42, 0.76]***
[0.45, 0.82]**
Women’s status factors
Age at first
0.96
1.00
marriage
[0.92, 1.01]
[0.95, 1.05]
Justifies wife1.52
1.48
beating (yes)
[1.08, 2.14]*
[0.99, 2.19]
Education level (ref. category = none)
Primary
1.04
0.91
[0.66, 1.64]
[0.58, 1.41]
Secondary or
1.23
0.89
higher
[0.85, 1.77]
[0.61, 1.31]
Note. IPV = intimate partner violence; OR = odds ratio; CI = confidence interval.
*p < .05. **p < .01. ***p < .001.
low levels of formal education, frequent early marriage, and high levels of
accepting attitudes toward wife-beating (DRC Ministry of Gender, Family, &
Children, 2009; U.S. Department of State, 2011). However, only a woman’s
justification of wife-beating as acceptable was significantly associated with
an increased risk for IPV. This finding confirms research in multiple countries indicating that supportive attitudes toward wife-beating are a significant
risk factor for IPV (Abramsky et al., 2011; Alio et al., 2011; Stith, Smith,
Penn, Ward, & Tritt, 2004). Women may be socialized to believe that violence is normal; Congolese women in one study identified beating as the way
a husband “educates” his wife (Alio et al., 2011; Lwambo, 2011). Widespread
acceptance of wife-beating may also increase violence risk if these norms are
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held at community level to the extent that they prevent intervention (Boyle,
Georgiades, Cullen, & Racine, 2009).
Findings suggest that partner characteristics such as alcohol use and polygamy play a role in women’s risk for IPV in the Congo, consistent with systematic reviews (Jewkes, 2002; Stith et al., 2004). Men’s alcohol consumption is
a way to assert masculinity in the DRC, and heavy use of alcohol may reduce
inhibitions to commit violence or reduce men’s sense of responsibility for
violence (Jewkes, 2002; Lwambo, 2011). Having multiple partners is commonly associated with “traditional” masculinity, and consistently associated
with higher risk of IPV, regardless of whether or not the woman knows about
the additional partners (Abramsky et al., 2011). Men in DRC with multiple
wives may be more likely to hold to other “traditional” definitions of masculinity that allow IPV. Recent reports indicate widespread acceptance of patriarchal norms related to men’s domination in Congo (Sonke Gender Justice &
Promundo-US, 2012). Research has generally found women’s formal education to be protective against IPV, inconsistent with the results of this study
(Abramsky et al., 2011; Jewkes, 2002; Stith et al., 2004). One explanation may
be that other factors not measured in this analysis may have “muted” the protective effect of women’s education; studies in other settings found women’s
education to be moderated by community factors, such as literacy, standard or
living, or the level of community acceptance of violence against women
(Ackerson, Kawachi, Barbeau, & Subramanian, 2008; Boyle et al., 2009).
Although poverty is often a risk factor for women, it was not associated with
IPV at the bivariate level in this study. Jewkes suggests that inequality between
partners may be more important than the absolute level of poverty, but examination of this relationship requires additional data (2002).
This study is one of very few empirical analyses of risk and protective
factors for IPV against women in Congo, and it contributes to knowledge on
context-specific risks for IPV. However, the findings should be interpreted in
consideration of the study limitations. The 2007 DHS data set is the most
recent available and may not reflect current conditions in the DRC; for example, recent research and media attention may have increased efforts to reduce
violence against women in the DRC. As always, cross-sectional data do not
allow temporal ordering of independent variables of interest and women’s
experience of IPV, so the direction of relationships in the analysis is unknown.
In particular, the direction of the relationship between an accepting attitude
toward wife-beating and IPV is unclear; women’s acceptance of wife-beating
may be a means of coping with or making sense of violence after it occurs
(Lawoko, 2006).
The data collection procedure for the domestic violence module of the
DHSs may underestimate reports of IPV; all variables in this analysis were
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self-reported by the respondent, including partner factors, and women may be
more likely to give socially desirable responses in an interviewer-administered survey (Ellsberg, Heise, Pena, Agurto, & Winkvist, 2001). Sampling
only women of reproductive age fails to capture IPV experienced by women
of other ages (Lawry, Reis, Kisielewski, & Asher, 2011). Although administering the domestic violence module to a sub-sample of women was important
for women’s safety, it could have introduced non-response bias if it excluded
women at greater risk for IPV, for example, women who could not obtain
privacy for the interview. The experience of IPV for women with non-cohabiting partners was not captured in this sample.
Using standardized measures rather than ones developed or validated for
a study of IPV in the Congo is a limitation. The validity of the Conflict Tactics
Scale for IPV in non-Western populations has been critiqued, as aggression
may be expressed differently in other cultures (White, Smith, Koss, &
Figueredo, 2000). Findings were also limited by the relatively few indicators
of women’s status and IPV risk and protective factors measured in the DHS
surveys. Although women’s social standing and economic power are likely
protective factors for IPV, it was not possible to include measures of these
factors in the analysis (Jewkes, 2002; Vyas & Watts, 2009). Analysis was
limited to variables in the form they existed in the data set; for example, the
polygamy variable did not account for partner’s non-marital concurrent intimate relationships, and the alcohol variable measured use rather than abuse.
Critics of prior analyses of Congo DHS data recommend the use of multilevel
statistical models to account for community-level factors; several have been
implicated in women’s IPV risk but were not included here (Ackerson et al.,
2008; Boyle et al., 2009; Heise, 1998; Linos & Kawachi, 2012). The inclusion of more culturally valid measures or additional variables may have
improved the prediction ability of the models.
In spite of limitations, the findings have implications for practice and policy. The study’s use of an ecological risk and protective factor framework
highlights how practitioners working with women in the DRC should be
aware of the IPV risk associated not only with women’s characteristics but
also her partner’s characteristics, such as polygamy or use of alcohol. The
high percentage of women willing to justify wife-beating indicates the need
for country-wide awareness of women’s rights and education on IPV. The
government’s official strategy to reduce gender-based violence assumes that
IPV exists because of women’s low status relative to men; these findings
provide some evidence to support targeted efforts to improve women’s status
in the DRC, although additional research is needed (DRC Ministry of Gender,
Family, & Children, 2009). The study emphasizes the need for attention not
only to conflict-related violence against women but also to the violence that
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women face from intimate partners. Future studies on IPV in DRC would
benefit from improved measures of women’s status, the inclusion of community-level factors, and tests of possible moderating effects.
Acknowledgment
The author thanks Dr. Shanta Pandey for advice and recommendations on working
with Demographic and Health Survey (DHS) data sets. Special thanks to the
MEASURE DHS project for making the DHS data publicly available to researchers.
Author’s Note
The Congo Demographic and Health Survey (DHS) was conducted in cooperation
with the Congolese Ministries of Planning and Health with technical support from the
global DHSs project (MEASURE DHS) of Macro International, Inc.
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) received no financial support for the research, authorship, and/or publication of this article.
References
Abramsky, T., Watts, C. H., Garcia-Moreno, C., Devries, K., Kiss, L., Ellsberg, M.,
. . . Heise, L. (2011). What factors are associated with recent intimate partner
violence? Findings from the WHO multi-country study on women’s health and
domestic violence. BMC Public Health, 11, Article 109.
Ackerson, L. K., Kawachi, I., Barbeau, E. M., & Subramanian, S. V. (2008). Effects
of individual and proximate educational context on intimate partner violence: A
population-based study of women in India. American Journal of Public Health,
98, 507-514.
Alio, A. P., Clayton, H. B., Garba, M., Mbah, A. K., Daley, E., & Salihu, H. M.
(2011). Spousal concordance in attitudes toward violence and reported physical
abuse in African couples. Journal of Interpersonal Violence, 26, 2790-2810.
Archer, J. (2006). Cross-cultural differences in physical aggression between partners: A social-role analysis. Personality and Social Psychology Review, 10,
133-153.
Boyle, M. H., Georgiades, K., Cullen, J., & Racine, Y. (2009). Community influences
on intimate partner violence in India: Women’s education, attitudes towards mistreatment and standards of living. Social Science & Medicine, 69, 691-697.
Bunch, C. (1997). The intolerable status quo: Violence against women and girls.
Retrieved from http://www.unicef.org/pon97/40-49.pdf
Downloaded from jiv.sagepub.com at PENNSYLVANIA STATE UNIV on May 12, 2016
2539
Tlapek
DRC Ministry of Gender, Family, & Children. (2009). National strategy to fight
against gender-based violence. Retrieved from http://monusco.unmissions.org/
LinkClick.aspx?fileticket=RxbG_S-GaVo=
DRC Ministry of Planning. (2008). Demographic and Health Survey 2007. Calverton,
MD: Macro International.
Ellsberg, M., Heise, L., Pena, R., Agurto, S., & Winkvist, A. (2001). Researching
domestic violence against women: Methodological and ethical considerations.
Studies in Family Planning, 32, 1-16.
Ellsberg, M., Jansen, H. A., Heise, L., Watts, C. H., & Garcia-Moreno, C. (2008).
Intimate partner violence and women’s physical and mental health in the WHO
multi-country study on women’s health and domestic violence: An observational
study. The Lancet, 371, 1165-1172.
Heise, L. L. (1998). Violence against women: An integrated ecological framework.
Violence Against Women, 4, 262-290.
Hindin, M. J. (2014). Adolescent childbearing and women’s attitudes towards wife
beating in 25 Sub-Saharan African countries. Maternal and Child Health Journal,
18(6), 1488-1495.
Jewkes, R. (2002). Intimate partner violence: Causes and prevention. The Lancet,
359, 1423-1429.
Johnson, K., Scott, J., Rughita, B., Kisielewski, M., Asher, J., Ong, R., & Lawry, L.
(2010). Association of sexual violence and human rights violations with physical
and mental health in territories of the Eastern Democratic Republic of the Congo.
The Journal of the American Medical Association, 304, 553-562.
Kelly, J. T., Betancourt, T. S., Mukwege, D., Lipton, R., & VanRooyen, M. J. (2011).
Experiences of female survivors of sexual violence in eastern Democratic
Republic of Congo: A mixed-methods study. Conflict and Health, 5(25), 2-8.
Kishor, S. (Ed.). (2005). A focus on gender: Collected papers on gender using DHS
data. Calverton, MD: ORC Macro.
Kishor, S., & Johnson, K. (2006). Reproductive health and domestic violence: Are the
poorest women uniquely disadvantaged? Demography, 43, 293-307.
Kishor, S., & Neitzel, K. (1996). The status of women: Indicators for twenty-five
countries (DHS Comparative Studies No. 21). Retrieved from http://www.measuredhs.com/publications/publication-CS21-Comparative-Reports.cfm
Lawoko, S. (2006). Factors associated with attitudes toward intimate partner violence: A study of women in Zambia. Violence and Victims, 21, 645-656.
Lawry, L., Reis, C., Kisielewski, M., & Asher, J. (2011). Problems in reporting sexual
violence prevalence [Letter to the editor]. American Journal of Public Health,
101, 2004-2005.
Linos, N., & Kawachi, I. (2012). Community social norms as social determinants
of violence against women [Letter to the editor]. American Journal of Public
Health, 102, 199-200.
Lwambo, D. (2011). “Before the war, I was a man”: Men and masculinities in the
Eastern Democratic Republic of Congo. (HEAL Africa Report). Retrieved from
Downloaded from jiv.sagepub.com at PENNSYLVANIA STATE UNIV on May 12, 2016
2540
Journal of Interpersonal Violence 30(14)
HEAL Africa website: http://www.healafrica.org/wp-content/uploads/2011/10/
men-and-masculinities-in-eastern-dr-congo.pdf
Peterman, A., Palermo, T., & Bredenkamp, C. (2011). Estimates and determinants of
sexual violence against women in the Democratic Republic of Congo. American
Journal of Public Health, 101, 1060-1067.
Rutstein, S. O., & Johnson, K. (2004). The DHS wealth index (DHS Comparative
Studies No. 6). Retrieved from https://dhsprogram.com/pubs/pdf/CR6/CR6.pdf
Sonke Gender Justice & Promundo-US. (2012). Gender relations, sexual violence,
and the effects of conflict on women and men in North Kivu, Eastern Democratic
Republic of Congo. Retrieved from http://www.genderjustice.org.za/
Stith, S. M., Smith, D. B., Penn, C. E., Ward, D. B., & Tritt, D. (2004). Intimate partner physical abuse perpetration and victimization risk factors: A meta-analytic
review. Aggression and Violent Behavior, 10, 65-98.
Straus, M. A., Hamby, S. L., Boney-McCoy, S., & Sugarman, D. B. (1996). The
Revised Conflict Tactics Scales (CTS2) development and preliminary psychometric data. Journal of Family Issues, 17, 283-316.
U.S. Department of State. (2011). Country reports on human rights practices for
2011: Democratic Republic of the Congo. Retrieved from http://www.state.gov/j/
drl/rls/hrrpt/2011humanrightsreport/index.htm?dlid=186183
Vyas, S. S., & Watts, C. (2009). How does economic empowerment affect women’s
risk of intimate partner violence in low and middle income countries? A systematic review of published evidence. Journal of International Development, 21,
577-602.
Watts, C., & Zimmerman, C. (2002). Violence against women: Global scope and
magnitude. The Lancet, 359, 1232-1237.
White, J. W., Smith, P. H., Koss, M. P., & Figueredo, A. J. (2000). Intimate partner
aggression—What have we learned? Psychological Bulletin, 126, 690-696.
World Bank. (2009). The costs of violence. Retrieved from http://siteresources.worldbank.org/EXTSOCIALDEVELOPMENT/Resources/244362-1239390842422/
6012763-1239905793229/costs_of_violence.pdf
World Health Organization. (2001). Putting women’s safety first: Ethical and safety
recommendations for research on domestic violence against women. Geneva,
Switzerland: Author.
Author Biography
Sarah Myers Tlapek is a PhD candidate in social work at the Brown School at
Washington University in St. Louis who lived and worked in Africa’s Great Lakes
region for 5 years between 2003 and 2013. Her research interests include domestic
violence, women’s empowerment, and mental health in post-conflict settings.
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