552854
research-article2014
VAWXXX10.1177/1077801214552854Violence Against WomenWhitaker
Article
Linking Community Protective Factors To Intimate Partner Violence Perpetration
Violence Against Women 2014, Vol. 20(11) 1338–1359 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1077801214552854 vaw.sagepub.com
M. Pippin Whitaker1
Abstract This study explores how community factors moderate men’s individual risk for physical and psychological intimate partner violence (IPV) perpetration. The sample of 604 male first-semester undergraduate students supports a connection between county-level protective and risk factors, an individual risk factor, and IPV perpetration. For each unit increase in the proportion of women in powerful positions within a county, there was a 71% decrease in the risk that control-seeking respondents would perpetrate physical IPV, controlling for other factors including population density and violent crime. This article presents a multilevel analysis using hierarchical generalized linear modeling and discusses practice and research implications. Keywords contextual factors, intimate partner violence, protective factors
Introduction Intimate partner violence (IPV) is a widespread social and public health problem with numerous well-documented, deleterious physical, psychological, and interpersonal consequences, including depression, suicidal ideation, injury, and death (Black, 2011; Leone, Johnson, Cohan, & Lloyd, 2004; Tjaden & Thoennes, 2000). While widespread, IPV prevalence rates vary significantly nationally (Black et al., 2011). Several risk factors have been proposed to explain the variance in incident rates, including population density, violent crime rate, and race, but it is unclear how these risk factors relate to differing IPV prevalence rates, whether it be through increasing individual 1University
of South Carolina, Columbia, USA
Corresponding Author: M. Pippin Whitaker, College of Social Work, University of South Carolina, Columbia, SC 29208, USA. Email: [email protected]
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risk or producing mitigating circumstances. Very little research has been conducted on potential community-level protective factors that might help explain the variation. The lack of research on community-level protective factors is a gap in knowledge as well as practice. While most IPV prevention interventions and programs are focused on individual-level change (Offenhauer & Buchalter, 2011), a growing number of IPV prevention programs across the country attempt to address risk and protective factors at the community and societal levels (Centers for Disease Control and Prevention, n.d.). Programs’ selection of community-level protective and risk factors is hampered by the lack of theoretical knowledge of risk factors, as well as methodology. As a result of the lack of knowledge about community protective factors, community strategy selection is often based on feasibility and political will, which, while pertinent, are not necessarily indicative of salient factors in an IPV perpetration causal chain.
Conceptualizing How Macro Contexts Influence IPV Until recently, most conceptual work on IPV described individual and relationship factors that predict IPV, rather than the influence of community- and societal-level (macro) contexts (Browning, 2002; Hegarty, Sheehan, & Schonfeld, 1999). Most individual factors center on gendered-related attitudes. While culture strongly influences sexist attitudes (Forbes, Adams-Curtis, & White, 2004; Glick & Fiske, 1996, 2001), the empirical IPV literature has overwhelmingly examined attitudes as an individual attribute. Gender theories broadly predict that sexism increases violence against women, and these ideas have been borne out in empirical associations between sexist attitudes and IPV perpetration (Forbes et al., 2004). Belief in strict gender roles, hostility toward women, and desire for control in relationships are each among the most cited individual-level risk factors for IPV perpetration against women (Connolly, Friedlander, Pepler, Craig, & Laporte, 2010; Forbes et al., 2004; Foshee et al., 2008; Offenhauer & Buchalter, 2011). Furthermore, Forbes et al. (2004) found, in a sample of European American undergraduate students ages 18 to 21 years, that hostility toward women was more prominent than traditional sexism and more strongly associated with IPV. Although based on relatively few empirical studies, the list of macro-contextual factors associated with IPV perpetration is lengthy. Krug and colleagues (Krug, Dahlberg, Mercy, Zwi, & Lozano, 2002) summarized the literature on risk for IPV perpetration, and the list of community- and societal-level factors included: low social capital, negative portrayal of women in the media, poverty, weak community sanctions against IPV perpetrators, historical and societal patterns that glorify violence against women, institutional structures that promote unequal power between men and women, gender-role socialization processes (culture), and social norms supportive of violence. In addition, a longitudinal study of women’s IPV reporting identified social disorganization and neighborhood economic development as factors that promoted IPV (Browning, 2002). At the broadest level, Martin, Vieraitis, and Britto (2006) examined the influence of women’s resources and gendered resource inequalities at the county level on IPV. They found that counties in which women share a greater ratio
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of income, educational attainment, and employment compared with men have lower rates of IPV. Overall, studies of macro contexts in IPV have tended to examine correlative risk factors of violence instead of causally linked multilevel context-to-individual interactions (Sanchez-Hucles & Dutton, 1999). While, as Krug et al. (2002) summarize, there are several macro contexts associated with IPV perpetration, linking these factors to IPV perpetration is complicated. More recent research attempts to develop a better understanding of how context influences IPV perpetration. The level of context, however, tends toward the immediate individual and relationship context more so than the macro context. Foshee and colleagues (2008) conducted a mediation analysis on data from 959 adolescents to understand how sociodemographic variables, such as minority status and socioeconomic status (SES; parental education), influence dating violence perpetration via individual-level contextual factors. They found that the increased risk for dating violence associated with minority status was mediated by communication skills, acceptance of dating abuse, gender stereotyping, and exposure to family violence. In turn, the risk associated with SES was mediated by acceptance of dating abuse, gender stereotyping, and exposure to family violence. Cho (2012) examined race while controlling for employment status, financial security, education, age, and social network. Cho found that race was no longer significant after controlling for the other sociodemographic factors. Cho and Foshee et al. approach the question from different levels, but the findings corroborate each other and enhance our knowledge of how sociodemographic factors influence IPV. While Foshee et al. (2008) suggest that minority status and SES interact with individual attributes to increase risk for IPV, Cho suggests specific sociodemographic aspects of context that explain IPV risk associated with race. In addition to sociodemographic factors, exposure to media that glorify violence against dating partners has also been explored in recent research. Connolly et al. (2010) conducted a longitudinal study of 627 male and female Canadian adolescents who examined the influence of multilevel factors on risk for dating aggression. The researchers found support for a mediation model between violent media exposure, acceptance of dating aggression, and dating violence. The authors note a conceptual implication that violent media influence dating aggression by increasing adolescents’ tolerance of violence in dating relationships. Many macro contexts implicated by Krug et al. (2002) and others are understandably based on their gendered structure (e.g., unequal power). On one level, males and females may physically share the same macro context, yet experience different IPV rates and even greater differences in IPV severity (Black et al., 2011; Centers for Disease Control and Prevention, 2009). Taken with Foshee et al. (2008) and Cho’s (2012) work, this suggests that to understand the influence of macro context in IPV, it is necessary to understand macro context with respect to specific types of IPV associated with gender dynamics, overall attitudes about violence, or economics. Given the gendered nature of at least some IPV, the theory of gender and power, among other structural dialectic theories, posits that gender structures all levels of context; in other words, people experience different contexts according to their gender. The theory of gender and power (Connell, 1987) describes how gendered structures of
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power and resources are historically embedded within society, constituting the gender order. The gender order includes not only the pattern of power relations and gender ideals and definitions of a given society at a given point in time but also how these structures reconstitute a gender order over time. Connell (1987) describes how the sexual division of labor is manifested through an unequal division of unpaid labor. Women typically do not receive remuneration for their disproportionate share of domestic work, and men receive both a greater proportion of paid work as well as a greater wage share. The sexual division of power is manifested through men’s disproportionate access to the means of power and use of force, as well as men’s positions of authority over and relative to women (Connell, 1987). Thus, men occupy most positions of power within medicine, law enforcement, justice, politics, government, academia, science, education, and religion (Padavic & Reskin, 2002). Also, according to Connell, men’s access to the means of force—for example, through law enforcement or the military—adds to the expectation of power among men versus women. Connell’s predictions have received support in the IPV literature. For example, Martin et al. (2006) found strong support for the association between women’s resource inequality (or economic independence) and IPV victimization. According to Farmer and Tiefenthaler’s (2003) economic theories, rates of IPV against women should decline as women’s alternatives improve, that is, as women are more likely to leave abusive partners. The authors conclude that women gain power in relationships when they can make credible threats to leave (Farmer & Tiefenthaler, 2003). While credible threats to leave may be powerful, IPV rates and severity are shown to be at their highest when women have real power to leave a relationship, or after they leave (Dewees & Parker, 2003; Dutton, 1998; Titterington, 2006; Whaley & Messner, 2002). Beyond leaving a partner, women who resist abusive male partners by such tactics as sleeping in another room or refusing orders are 2.3 times more likely to experience further abuse (Goodman, Dutton, Vankos, & Weinfurt, 2005). The findings support that increases in women’s power, independence, and resources coincide with hostility toward women and IPV. The theory of gender and power suggests that community-level economic resources and workforce power are relevant to women’s macro contexts. Capability theory provides a philosophical foundation for conceptualizing macro contexts that empower women. The premise of capability theory is that humans need a set of essential capabilities to obtain desired functioning (Nussbaum, 2000; Sen, 1999). Capability theory asserts that if one is bestowed a right but lacks the ability to exercise that right due to a social or material barrier, then the individual is deprived of a capability. Nussbaum (2000) identifies 10 essential human capabilities based on research on women in India: life; bodily health; bodily integrity; senses, imagination, and thought; emotions; practical reason; affiliation; other species; play; and control over one’s environment (Nussbaum, 2000). Robeyns (2003) makes a major contribution to the application of capability theory to Western cultures by including five additional nuanced capabilities relevant to Western life: mental well-being, domestic work and nonmarket care, mobility, time-autonomy, and religion.
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Nussbaum (2000) distinguishes between internal and combined capabilities. Internal capabilities relate to personal capacities, and combined capabilities result from association with a group. Nussbaum suggests that capability structures are malleable through collective action to influence the actors as well as those affiliated with the group. Formal organizations and groups are examples of combined capabilities in action. For example, the National Organization for Women (NOW) is the largest and among the oldest U.S. feminist organizations and a strong indicator of women’s political association with one another because NOW routinely collaborates with other women’s and feminist organizations to advance women’s status (Sapiro, 1986).
Research Question and Hypotheses There is a substantial gap in our understanding of how theoretically promising macrocontextual factors moderate or mediate individual risk for IPV perpetration. Analysis of associations is inadequate. If the macro context directly protects against perpetration, this is important. More likely, macro context reduces risk factors or impedes the relationship between risk factors and IPV. Given that evidence suggests that relevant macro contexts depend on IPV type, this study focuses on how gendered macro contexts influence IPV associated with men’s gender-influenced risk for IPV as an initial area to explore the role of macro context in IPV. The theory of gender and power implies that gendered structures shape behavior and expectations in relationships. Capability theory suggests that there are multiple domains of women’s power that influence functioning. Based on these theories, this study’s conceptual perspective is that the collective power of a group influences behavior of individuals within the group by shaping perceived options, opportunities, and results. In addition, the power and abilities of a group across multiple domains shape the perceived options, opportunities, and results associated with how individuals behave toward individuals who are within a group. Based on the available evidence and literature, this study asks whether and how macro contexts act as protective factors in the relationship between men’s risk for perpetration and physical or psychological IPV perpetration. This study examines community-level factors that relate to known risk factors or are informed by capability theory to the extent that county estimates are available. In addition, community crime rate and population density are included in the analysis to control for competing explanations in the examination of protective factors. Individual risk factors that parallel gendered macro contexts in the conceptual literature are examined: male dominance, hostility toward women, and desire for control in relationships. Drawing on the research regarding macro-level risk and protective factors and the theoretical frameworks reviewed, this study tests two hypotheses using county as a proxy for macro context. Hypothesis 1: County context will not have a significant direct effect on the likelihood of IPV perpetration. Although IPV rates vary by region, state, county, and neighborhood, the within-group rates are heterogeneous. Take, for example, the
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household rate in a county with a very high incidence rate of IPV. We would expect that some households would have very high rates, while others would have no IPV. Thus, the within-county heterogeneity may be greater than that between counties. Rather, county context is hypothesized (2a-2c) to influence IPV through individual risk, and reports of IPV are, fortunately, not extremely high overall. Thus, the first hypothesis expects no significant direct effect. Hypotheses 2a-2c: The effect of an individual’s level of attitudes toward male dominance (2a), hostile sexism (2b), or control-seeking (2c) on likelihood of IPV perpetration will be significantly weaker among individuals in counties where women overall have more power, independence, and resources, controlling for county crime rate and population density. The county factors are expected to change the effect that each individual-level factor has on the likelihood of IPV perpetration. Furthermore, it is expected that county factors will have the largest effect size in moderating control-seeking compared with male dominance or hostile sexism.
Method This study uses data from a cross-sectional survey of all adult, in-state, degree-seeking undergraduate students at a large, state-funded university in the Southeastern United States to examine relationships among women’s county context, male dominance, hostile sexism, control-seeking, and men’s IPV perpetration against women. The survey took place in the fall of 2009. In addition to attitudes and past-year IPV perpetration, the survey included relationship status, age, and academic level. To assess the influence of women’s county environment on the relationship between men’s risk for IPV and IPV perpetration, the present study examines past-year behaviors among male undergraduate students at the beginning of their first semester in college. This provides insight into home county behavior the year prior to entering college. After obtaining institutional review board approval, an invitation letter, informed consent, and link with an embedded unique identifier to take the survey were sent via campus email after the add/drop deadline, with three weekly (Sunday) reminders. The invitations and reminder emails included first names in the salutation. Invitation letters informed students that they were entered for a chance to win a US$50 gift card with immediate notice of results upon completion.
Measures for Individuals Level of attitudes supporting male dominance was measured with the dominance subscale of the Male Role Norms Inventory–Revised (MRNI-R; Levant et al., 2007). The MRNI-R has been used extensively over the past 15 years (Levant & Richmond, 2007), but the revised scale has better discriminant construct validity, higher reliability (α = .96), and better captures contemporary concepts of masculinity (Levant et al., 2007). The MRNI-R dominance subscale is a seven-item scale with α reliability of .84. The response options were a 6-point Likert-type scale (strongly disagree to strongly
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agree), coded 0 to 5. The male dominance score was computed as the average of all items, resulting in a continuous variable ranging from 0 to 5, with 5 indicating the highest level of attitudes supporting male dominance. Hostile sexism was measured with the Hostile Sexism scale, an 11-item scale that assesses respondent attitudes toward women’s authority and women’s complaints (Glick & Fiske, 1996). The reported scale reliabilities ranged from α = .80 to .92 across six studies (Glick & Fiske, 1996) and had strong evidence of concurrent validity with related measures (Forbes et al., 2004). The Hostile Sexism scale has six response options (strongly disagree to strongly agree) that were scored 0 to 5 (Glick & Fiske, 1996). The score was an average of the items after reverse scoring three items, resulting in a continuous variable ranging from 0 to 5, with 5 indicating the most support for hostile sexism. Control-seeking was measured with five items from the 10-item restrictive subscale of the Dominance Scale (Hamby, 1996). The subscale had an α of .73. Five items were omitted because they assess insecurity (e.g., “I tend to be jealous”) and had factor loadings less than 0.50 (Hamby, 1996). The items had six response options (strongly agree to strongly disagree). The score was an average of the five items resulting in a continuous variable ranging from 0 to 5, with 5 indicating the most control-seeking. Respondents were asked whether they had at least one partner in the past year (yes/no), and whether those “intimate/dating relationship partner(s) in the past year were”: male (yes/no), female (yes/no). Respondents with only female partners were retained in this analysis. IPV perpetration was operationalized as self-reported psychological and physical abuse perpetrated against an intimate relationship partner in the past year. This was measured with the physical assault and psychological aggression subscales of the Revised Conflict Tactics Scale (CTS2) for perpetration with dichotomous response options. Respectively, these subscales have 12 and 8 items and Cronbach’s α of .86 and .79, respectively (Straus, Hamby, Boney-McCoy, & Sugarman, 1996). For this study, IPV was a multinomial variable coded 0 for no physical or psychological abuse, 1 for psychological abuse only, and 2 for physical and psychological abuse. The survey included age in years, academic level (fresher = 1 through senior = 4), and sex (M/F). Respondents aged 18 to 19 years who selected “M” were retained in analyses.
County Measures Resources were measured with four variables derived from the 2010 U.S. Census 5-year estimates for 2006-2010. These four items comprise the index of women’s absolute status (Martin et al., 2006). Martin et al.’s index uses data from the 2000 U.S. Census on women’s median income: the percentage of women aged 25 years and above with a bachelor’s degree, the percentage of women 16 and above in the workforce, and the percentage of women 16 and above in management, professional, and related occupations. For the present study, all percentages were based on 5-year estimates to more closely reflect the county environment at the time of participant’s high
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school graduation (2008) and to minimize the influence of chance fluctuation. Martin et al. found the four items comprised a single factor via principal components analysis (Martin et al., 2006). Resources is a continuous variable theoretically ranging from 0 to 4.0. Independence was operationalized as women’s status relative to men’s and was measured with four items from the relative status of women index (Martin et al., 2006). Martin et al. computed the ratios of women’s levels to men’s to obtain relative status. Again, Martin et al. found the four items comprised a single factor via principal components analysis. For the present study, these variables were computed on a per-county basis using the 2010 U.S. Census data 5-year estimates for 2006-2010. As noted, 5-year estimates better approximate the county environment at the time of graduation (2008) and minimize chance fluctuation. Independence was a continuous variable theoretically ranging from 0 to 1.0. Power was operationalized with six variables to reflect women’s overall capabilities: force, association, law-making, workforce, religion, and reproduction. Each variable represents a concept that entails a level of power and increased combined capability for action. Each variable was coded so that higher levels indicate more power among women at the county aggregate level. The measurement and rationale of each power variable are described below. Force represents the sanctioned access to the use of force and the authority to enforce the law. This is measured by the proportion of police department and sheriff’s office law enforcement positions held by women versus men. The proportion of female-to-male law enforcement positions per county was obtained from the Florida Department of Law Enforcement’s 2007 Criminal Justice Agency Profile (Florida Department of Law Enforcement, 2008b). This was a continuous variable ranging from 0 to 1. Women’s association through interest groups increases women’s political power (Sapiro, 1986). Association was operationalized as the number of NOW members for 2009, per 10,000 county population. Membership per county was obtained from the Florida NOW state registry. Women in elected positions with law-making authority is an important measure of women’s power. Law-making was measured by the average proportion of women to men elected to state-level offices in the Florida Senate and Florida House of Representatives for the 2008 regular session (for senators, it is the 2008-2010 election period). The proportion ranged from 0 to 1, with higher numbers indicating a higher proportion of women in elected office. Workforce was computed as the proportion of women-owned businesses over all businesses within each county in 2007. The 2007 U.S. Survey of Business Owners reports the total number of women-owned businesses and the total number of privately owned businesses per county by owner sex. Workforce was a continuous variable ranging from 0, no women-owned businesses, to 1, all privately owned businesses were women-owned. Religion was based on the level of orthodoxy in a community as a proxy for women’s power and authority in religious organizations. Religion has been implicated in
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IPV in various ways, both as a protective factor and as a risk factor (Hall & Duvall, 2003), and institutional structures that promote unequal power between men and women have been identified as a risk factor for IPV perpetration (Heise, 1998). Data were obtained from the Religious Congregations and Membership Study (Association of Religion Data Archives, 2000). Religion was computed as the proportion of the population who were not members of Catholic, Muslim, Jewish Orthodox, and other Orthodox Christian churches. The population estimate was based on 2000 Census reports per county. Reproduction was operationalized by the number of women-headed households with dependent children above the poverty line as a proportion of all women-headed households with dependent children, as reported by the 2010 U.S. Census. The continuous variable ranged from 0 to 1, with 1 indicating that all women-headed households with children were above poverty.
County Descriptive Variables Violent crime rate and population density were included as potential explanatory variables that have been identified as prominent community-level IPV risk factors (Farmer & Tiefenthaler, 2003; Martin et al., 2006). The violent crime rate per 1,000 population was obtained from the 2007 Florida Department of Law Enforcement Uniform Crime Reports (Florida Department of Law Enforcement, 2008a) and used the county population estimate for 2007 (Office of Economic and Demographic Research, 2008). Violent crimes are murder, forcible sex offenses, robbery, and aggravated assault (Federal Bureau of Investigations, 2004). Population density was obtained by the 2010 Census 5-year average population estimate living in a rural area (according to the Census definition of not urban).
Analyses Analyses were conducted using IBM SPSS Statistics Version 19 for univariate descriptive analyses and screening for normality, analysis of missing data, bivariate analyses comparing the sample with the sample frame, and basic psychometric analyses, including reliability and the standard error of measurement (SEM). Because the hypotheses involve nested data in which individuals are nested within county contexts, HLM 7 was used to conduct a multilevel analysis via hierarchical generalized linear regression using a multinomial model. For two-level analyses, Heck and Thomas (2000) recommend a minimum of 20 county-level observations each with at least 30 individual-level observations for adequate power. There were fewer than 20 counties with 30 observations. To determine appropriate sample size when county-level units are of greatest theoretical interest, Raudenbush and Bryk (2002) recommend a power analysis based on the method of Cochran (1975). The computation is given by,
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nopt
C2 1 − p 2 = * p C1
1
1 − .007 2 = 1* =11.41, .007
where C2/C1 is the relative effect of increasing the county sample size, C2, versus individuals within counties, C1, and ρ is a measure of the amount of within-county variability called the intra-county correlation. There was virtually no cost of sampling counties, but increasing the within-county sample was not feasible for two reasons. The contact list from the University Registrar did not include information on county of origin, and the distribution of university students from each county is uneven and extremely low in several counties. Thus, the ratio could theoretically be set very small and the optimal n would approach 1. Conservatively, the ratio was set to 1, and the optimal n within each county was estimated at 12. There were no counties with 12 to 14 respondents, and so 15 was the minimum. This resulted in a county sample of 23 counties (just over a third of all counties) that were well dispersed across the state.
Results The student sample comprised 604 first-year undergraduate males aged 18 to 19 who had a female relationship partner in the past year and who were from one of the selected counties. The majority (95%) of respondents were 18 (M = 18.1, SD = 0.26). Race was not obtained from the respondents to protect anonymity among minorities in the fresher class, but the males in the sample frame were 72% White, 11% Black, 13% Hispanic, and 4% other races. Sample size includes exclusion of three respondents above 19 years of age, eight respondents missing the second survey page (see below), and seven respondents with improbable response patterns. Improbable responses were identified as the same response on all hostile sexism items, including the reversescored items, and uniform responses on control-seeking and male dominance items.
IPV Perpetration Half the respondents reported perpetrating some form of IPV within the past year, with 15% reporting physical IPV and 35% reporting only psychological IPV (see Table 1). The physical IPV rate was within the range (14%-42%) identified by Straus (2004). The IPV perpetration items were on the second survey page and had a 98.5% completion rate. Investigation of missing data patterns revealed a monotone missing pattern. The discrepant completion rates may have been due to difficulties in progressing between survey pages. Eleven respondents emailed the researcher during data collection regarding difficulty advancing pages. Because the missing rate was moderate and likely due primarily to web survey platform failure, the missing responses were deemed missing at random and were excluded from analyses.
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Table 1. Descriptive Statistics for Individual and County Variables. M (%) Individual-level variables (N = 604) IPV perpetration Psychological only Physical Control-seeking Hostile sexism Male dominance County-level variables (N = 23) Formal power Informal power Independence Resources Violent crime rate Population density
SD
Min–max
(50%) (35%) (15%) 2.48 2.53 1.78
0.89 0.78 1.13
0.0-5.0 0.2-4.6 0.0-5.0
0.47 0.77 0.90 2.03 6.11 0.11
0.32 0.54 0.21 0.83 2.20 0.10
0.0-1.1 0.0-2.1 0.0-0.8 0.0-2.7 2.6-11.2 0.0-0.4
Note. IPV = intimate partner violence.
Attitude Scale Reliabilities Basic reliability statistics for the attitude and perpetration measures support use for this study. The scale reliabilities were within acceptable ranges for research purposes. Cronbach’s α was .91 for male dominance (SEM = 0.33), .83 for hostile sexism (SEM = 0.32), .79 for control-seeking (SEM = 0.41), .80 for physical IPV, and .73 for psychological IPV. The SEM is the scale standard deviation times the square root of 1 minus coefficient α. According to convention, the SEM should be 5% or less of the scale range. Thus, all SEMs were elevated. However, the SEM recommendation is based on scales with range 100. With relatively small-scale ranges, the adequacy of the 5% general rule is somewhat ambiguous, and the rule does not apply to dichotomous scales. The results suggest that male dominance and hostile sexism scores varied moderately in proportion to the scale range. The SEM was elevated for control-seeking, indicating a larger than desired spread in scores.
Sexist Attitudes Overall, reported attitudes supporting male dominance were less prevalent than hostile sexism and control-seeking (see Table 1). The average response on male dominance was 1.78 (SD = 1.13), which indicates an average between disagree and slightly disagree with male dominance statements. Male dominance was positively skewed (skewness = 0.373, SES = .10, p < .001) but with no significant departure from normal kurtosis at the alpha = .01 level (kurtosis = −0.425, SEK = 0.196, p = .031). The variable responded poorly to transformations. Given this and the moderate departure from
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normality, the original variable was retained. At 2.53 (SD = 0.78), hostile sexism’s average response was notably higher than that for male dominance. Hostile sexism’s score indicates an average between slightly disagree and slightly agree. Controlseeking was nearly as high as hostile sexism, with an average of 2.48 (SD = 0.89), indicating an average between slightly disagree and slightly agree with desire for control. Other than male dominance, there were no significant departures from univariate normality. However, the standard deviations for control-seeking and hostile sexism were smaller than expected, given the scale range. Attitude questions about male dominance, hostile sexism, and control-seeking were located on the first survey page and had at least a 99.9% completion rate. Analysis of missing data patterns suggests that data were missing at random. The missing attitude scale items were computed via single imputation using ordinary least-squares regression and a randomly selected residual added to the predicted value. Each full scale was used to predict missing items. When multiple items were missing, pared down models were used with at least 2 of 4 predictors for control-seeking, 7 of 10 for hostile sexism, and 4 of 6 for male dominance.
County-Level Factor Analysis The county indexes for resources, independence, and power were developed using factor analysis by principal components on the full set of 67 counties. Given the small sample, factor analysis was expected to produce modest results. The aim was to verify the single-factor dimensionality for independence and resources found in previous studies and to make an initial attempt to reduce the dimensionality of the power variables. Consistent with Martin et al. (2006), this analysis entered the four variables for resources (women’s median income, education level, percentage in management positions, and employment rate) into a factor analysis by principal components using varimax rotation. All correlations among the four variables were significant and sufficiently large (r = .49-.80, p < .001), and the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.72. Inspection of the anti-image correlation matrix revealed only small correlations. All items loaded onto one factor that accounted for 56% of total variance. For the independence index, variables based on the ratio of women to men on each of the four variables were entered into a factor analysis using varimax rotation. Five of the correlations among the four variables were significant and sufficiently large (r = .30-.63, p < .001-.012), and the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.67. Again, all items loaded onto a single factor accounting for 56% of total variance. The amount of variance explained for both factors was lower than the general cutoff of 60%. However, the aim of this analysis was to verify factor structure in this dataset, which was supported by the results. Given the theoretical interest in these items, comparability to prior work, and likely influence of sample size on the variance explained by these factors, standardized variables were averaged together to create the independence and resource indexes. Six variables to assess a macro construct of women’s power in the community were entered into a factor analysis by principal components using varimax rotation: force,
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law-making, workforce, reproduction, religion, and association. The variables satisfactorily met the criteria for factor analysis, given the small sample of 67 counties. Five correlations among the six variables were significant and sufficiently large (r = .26-.64, p < .001-.049), and the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.58, only slightly below the desirable 0.60. Inspection of the anti-image correlation matrix revealed two moderately elevated correlations (r = .44). This may suggest cross-loading and the need for correlated factors. However, the highest cross-loading in the component matrix was 0.28 (proportion of women-owned businesses with informal power), which is well within acceptable ranges. An oblique (promax) rotation failed to reduce the cross-loading. The factor analysis for the power variables revealed two factors, which were labeled formal power and informal power and accounted for more than 59% of total item variance. Formal power comprised the proportion of women in law enforcement, the proportion of women in elected offices, and the proportion of women-owned businesses. Informal power comprised the proportion of women-headed single-parent households above poverty, the proportion of religious adherents who were not affiliated with Orthodox churches, and the proportion of the population who were members of NOW. Because the raw variables had different ranges and distributions, the standardized variables were averaged together according to the factor loading to create two summated scales.
County Context Of the 23 counties in this study, the average crime rate per 1,000 population was 6.11 (SD = 2.20; see Table 1). The average percentage of the county population living in a rural area, as determined by the U.S. Census, was 11% (SD = 0.10). The average level on the resources index was 2.03 (SD = 0.83, range = 3.91). The average for independence was 0.90 (SD = 0.213, range = 0.75). The average informal power level was 0.77 (SD = 0.539, range = 2.12) and formal power was 0.47 (SD = 0.315, range = 1.05). Given the relatively small sample (N = 23 counties), there were no significant departures from normality, and skewness and kurtosis were within acceptable ranges (skewness = −.691-.748, SES = .481; kurtosis = −.959-1.095, SEK = .935). Note that although kurtosis was elevated for resources based on conventional cutoffs, this was not a significant departure because the Fisher statistic was 1.17.
Tests of Hypotheses Because the outcome variable is multinomial, hierarchical generalized linear modeling was used to test the three study hypotheses. The attitude measures were meancentered, so that predictor effects could be estimated with respect to average attitude levels. To screen for multicollinearity among individual-level (Level 1) independent variables (male dominance, hostile sexism, control-seeking, age residual, and academic level), simple bivariate correlations were examined. There was a moderately high correlation between male dominance and hostile sexism (r = .62, p < .001). To investigate the influence of the correlation, each variable was regressed on the
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remaining independent variables. The largest R2 values were .36 and .38 for male dominance and hostile sexism as dependent variables, respectively. These R2 values were well below the .80 cutoff for multicollinearity (Menard, 2001). All correlations among county variables were below .50; two (r = .44-.45) were significant at the alpha = .05 level, and none was significant at the alpha = .01 level. Hypothesis 1. Results supported the first hypothesis that the county context would not have a significant direct effect on the likelihood of IPV perpetration. This hypothesis was tested with a two-level multinomial model with likelihood of IPV perpetration as the dependent variable at Level 1 and a random coefficient at the county level. Following the procedure for HLM multilevel analysis described in Raudenbush and Bryk (2002), a model with no county predictors was examined for a significant betweengroups effect before proceeding to enter county-level predictors. There was no significant county effect on the likelihood of psychological IPV perpetration (χ2 = 31.66, df = 22,603, p = .083) nor physical IPV perpetration (χ2 = 19.63, df = 22,603, p ≥ .500). Therefore, county-level variables were not entered as predictors of county variability in the likelihood for IPV. Hypothesis 2a. Results did not support the potential link between county context, male dominance, and IPV perpetration. Hypothesis 2a stated that the effect of an individual’s level of attitudes supporting male dominance on likelihood of IPV perpetration would be significantly weaker among individuals in counties where women overall have more power, independence, and resources, controlling for crime rate and population density. The three attitude predictors were entered into the multilevel model at Level 1, each with a random effect at the county level. Male dominance did not have a significant influence on the likelihood of IPV perpetration (psychological β = −.24, SE = 0.12, t ratio = −2.0, p = .057; physical β = −.19, SE = 0.18, t ratio = −1.1, p = .294), and subsequently there was no significant county effect to be explained (see Table 2). Hypothesis 2b. As with male dominance, results did not support the potential link between county context, hostile sexism, and IPV perpetration. Hypothesis 2b stated that the effect of an individual’s level of hostile sexism on likelihood of IPV perpetration would be significantly weaker among individuals in counties where women overall have more power, independence, and resources, controlling for county crime rate and population density. As stated above, the three individual attitude predictors were entered into the multilevel model at Level 1, each with a random effect at the county level. However, in the two-level model, hostile sexism did not have a significant influence on the likelihood of IPV perpetration (psychological β = .29, SE = 0.16, t ratio = 1.80, df = 22, p = .085; physical β = .23, SE = 0.21, t ratio = 1.12, df = 22, p = .273), and subsequently there was no significant county effect to be explained (see Table 2). Hypothesis 2c. Results supported Hypothesis 2c, suggesting a link between county context, control-seeking, and IPV perpetration. Hypothesis 2c stated that the effect of
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Table 2. Multilevel Model, Random Coefficient, and Mixed Effects of County and Individual Factors (N = 604,23). Fixed effect Psychological IPV only Intercept, β0j(1) Intercept, γ00(1) Male dominance, Intercept, γ10(1) β1j(2) Hostile sexism, Intercept, γ20(1) β2j(2) Control-seeking, Intercept, γ30(1) β3j(1) Physical IPV Intercept, β0j(2) Intercept, γ00(2) Male dominance, Intercept, γ10(2) β1j(2) Hostile sexism, Intercept, γ20(2) β2j(2) Control-seeking, Intercept, γ30(2) β3j(2) Resources, γ31(2) Independence, γ32(2) Formal power, γ33(2) Informal power, γ34(2) Crime rate, γ35(2) Population density, γ36(2)
β
SE
t ratio
p value
−0.42 −0.24
0.10 0.12
−4.02 −2.00
research-article2014
VAWXXX10.1177/1077801214552854Violence Against WomenWhitaker
Article
Linking Community Protective Factors To Intimate Partner Violence Perpetration
Violence Against Women 2014, Vol. 20(11) 1338–1359 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1077801214552854 vaw.sagepub.com
M. Pippin Whitaker1
Abstract This study explores how community factors moderate men’s individual risk for physical and psychological intimate partner violence (IPV) perpetration. The sample of 604 male first-semester undergraduate students supports a connection between county-level protective and risk factors, an individual risk factor, and IPV perpetration. For each unit increase in the proportion of women in powerful positions within a county, there was a 71% decrease in the risk that control-seeking respondents would perpetrate physical IPV, controlling for other factors including population density and violent crime. This article presents a multilevel analysis using hierarchical generalized linear modeling and discusses practice and research implications. Keywords contextual factors, intimate partner violence, protective factors
Introduction Intimate partner violence (IPV) is a widespread social and public health problem with numerous well-documented, deleterious physical, psychological, and interpersonal consequences, including depression, suicidal ideation, injury, and death (Black, 2011; Leone, Johnson, Cohan, & Lloyd, 2004; Tjaden & Thoennes, 2000). While widespread, IPV prevalence rates vary significantly nationally (Black et al., 2011). Several risk factors have been proposed to explain the variance in incident rates, including population density, violent crime rate, and race, but it is unclear how these risk factors relate to differing IPV prevalence rates, whether it be through increasing individual 1University
of South Carolina, Columbia, USA
Corresponding Author: M. Pippin Whitaker, College of Social Work, University of South Carolina, Columbia, SC 29208, USA. Email: [email protected]
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risk or producing mitigating circumstances. Very little research has been conducted on potential community-level protective factors that might help explain the variation. The lack of research on community-level protective factors is a gap in knowledge as well as practice. While most IPV prevention interventions and programs are focused on individual-level change (Offenhauer & Buchalter, 2011), a growing number of IPV prevention programs across the country attempt to address risk and protective factors at the community and societal levels (Centers for Disease Control and Prevention, n.d.). Programs’ selection of community-level protective and risk factors is hampered by the lack of theoretical knowledge of risk factors, as well as methodology. As a result of the lack of knowledge about community protective factors, community strategy selection is often based on feasibility and political will, which, while pertinent, are not necessarily indicative of salient factors in an IPV perpetration causal chain.
Conceptualizing How Macro Contexts Influence IPV Until recently, most conceptual work on IPV described individual and relationship factors that predict IPV, rather than the influence of community- and societal-level (macro) contexts (Browning, 2002; Hegarty, Sheehan, & Schonfeld, 1999). Most individual factors center on gendered-related attitudes. While culture strongly influences sexist attitudes (Forbes, Adams-Curtis, & White, 2004; Glick & Fiske, 1996, 2001), the empirical IPV literature has overwhelmingly examined attitudes as an individual attribute. Gender theories broadly predict that sexism increases violence against women, and these ideas have been borne out in empirical associations between sexist attitudes and IPV perpetration (Forbes et al., 2004). Belief in strict gender roles, hostility toward women, and desire for control in relationships are each among the most cited individual-level risk factors for IPV perpetration against women (Connolly, Friedlander, Pepler, Craig, & Laporte, 2010; Forbes et al., 2004; Foshee et al., 2008; Offenhauer & Buchalter, 2011). Furthermore, Forbes et al. (2004) found, in a sample of European American undergraduate students ages 18 to 21 years, that hostility toward women was more prominent than traditional sexism and more strongly associated with IPV. Although based on relatively few empirical studies, the list of macro-contextual factors associated with IPV perpetration is lengthy. Krug and colleagues (Krug, Dahlberg, Mercy, Zwi, & Lozano, 2002) summarized the literature on risk for IPV perpetration, and the list of community- and societal-level factors included: low social capital, negative portrayal of women in the media, poverty, weak community sanctions against IPV perpetrators, historical and societal patterns that glorify violence against women, institutional structures that promote unequal power between men and women, gender-role socialization processes (culture), and social norms supportive of violence. In addition, a longitudinal study of women’s IPV reporting identified social disorganization and neighborhood economic development as factors that promoted IPV (Browning, 2002). At the broadest level, Martin, Vieraitis, and Britto (2006) examined the influence of women’s resources and gendered resource inequalities at the county level on IPV. They found that counties in which women share a greater ratio
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of income, educational attainment, and employment compared with men have lower rates of IPV. Overall, studies of macro contexts in IPV have tended to examine correlative risk factors of violence instead of causally linked multilevel context-to-individual interactions (Sanchez-Hucles & Dutton, 1999). While, as Krug et al. (2002) summarize, there are several macro contexts associated with IPV perpetration, linking these factors to IPV perpetration is complicated. More recent research attempts to develop a better understanding of how context influences IPV perpetration. The level of context, however, tends toward the immediate individual and relationship context more so than the macro context. Foshee and colleagues (2008) conducted a mediation analysis on data from 959 adolescents to understand how sociodemographic variables, such as minority status and socioeconomic status (SES; parental education), influence dating violence perpetration via individual-level contextual factors. They found that the increased risk for dating violence associated with minority status was mediated by communication skills, acceptance of dating abuse, gender stereotyping, and exposure to family violence. In turn, the risk associated with SES was mediated by acceptance of dating abuse, gender stereotyping, and exposure to family violence. Cho (2012) examined race while controlling for employment status, financial security, education, age, and social network. Cho found that race was no longer significant after controlling for the other sociodemographic factors. Cho and Foshee et al. approach the question from different levels, but the findings corroborate each other and enhance our knowledge of how sociodemographic factors influence IPV. While Foshee et al. (2008) suggest that minority status and SES interact with individual attributes to increase risk for IPV, Cho suggests specific sociodemographic aspects of context that explain IPV risk associated with race. In addition to sociodemographic factors, exposure to media that glorify violence against dating partners has also been explored in recent research. Connolly et al. (2010) conducted a longitudinal study of 627 male and female Canadian adolescents who examined the influence of multilevel factors on risk for dating aggression. The researchers found support for a mediation model between violent media exposure, acceptance of dating aggression, and dating violence. The authors note a conceptual implication that violent media influence dating aggression by increasing adolescents’ tolerance of violence in dating relationships. Many macro contexts implicated by Krug et al. (2002) and others are understandably based on their gendered structure (e.g., unequal power). On one level, males and females may physically share the same macro context, yet experience different IPV rates and even greater differences in IPV severity (Black et al., 2011; Centers for Disease Control and Prevention, 2009). Taken with Foshee et al. (2008) and Cho’s (2012) work, this suggests that to understand the influence of macro context in IPV, it is necessary to understand macro context with respect to specific types of IPV associated with gender dynamics, overall attitudes about violence, or economics. Given the gendered nature of at least some IPV, the theory of gender and power, among other structural dialectic theories, posits that gender structures all levels of context; in other words, people experience different contexts according to their gender. The theory of gender and power (Connell, 1987) describes how gendered structures of
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power and resources are historically embedded within society, constituting the gender order. The gender order includes not only the pattern of power relations and gender ideals and definitions of a given society at a given point in time but also how these structures reconstitute a gender order over time. Connell (1987) describes how the sexual division of labor is manifested through an unequal division of unpaid labor. Women typically do not receive remuneration for their disproportionate share of domestic work, and men receive both a greater proportion of paid work as well as a greater wage share. The sexual division of power is manifested through men’s disproportionate access to the means of power and use of force, as well as men’s positions of authority over and relative to women (Connell, 1987). Thus, men occupy most positions of power within medicine, law enforcement, justice, politics, government, academia, science, education, and religion (Padavic & Reskin, 2002). Also, according to Connell, men’s access to the means of force—for example, through law enforcement or the military—adds to the expectation of power among men versus women. Connell’s predictions have received support in the IPV literature. For example, Martin et al. (2006) found strong support for the association between women’s resource inequality (or economic independence) and IPV victimization. According to Farmer and Tiefenthaler’s (2003) economic theories, rates of IPV against women should decline as women’s alternatives improve, that is, as women are more likely to leave abusive partners. The authors conclude that women gain power in relationships when they can make credible threats to leave (Farmer & Tiefenthaler, 2003). While credible threats to leave may be powerful, IPV rates and severity are shown to be at their highest when women have real power to leave a relationship, or after they leave (Dewees & Parker, 2003; Dutton, 1998; Titterington, 2006; Whaley & Messner, 2002). Beyond leaving a partner, women who resist abusive male partners by such tactics as sleeping in another room or refusing orders are 2.3 times more likely to experience further abuse (Goodman, Dutton, Vankos, & Weinfurt, 2005). The findings support that increases in women’s power, independence, and resources coincide with hostility toward women and IPV. The theory of gender and power suggests that community-level economic resources and workforce power are relevant to women’s macro contexts. Capability theory provides a philosophical foundation for conceptualizing macro contexts that empower women. The premise of capability theory is that humans need a set of essential capabilities to obtain desired functioning (Nussbaum, 2000; Sen, 1999). Capability theory asserts that if one is bestowed a right but lacks the ability to exercise that right due to a social or material barrier, then the individual is deprived of a capability. Nussbaum (2000) identifies 10 essential human capabilities based on research on women in India: life; bodily health; bodily integrity; senses, imagination, and thought; emotions; practical reason; affiliation; other species; play; and control over one’s environment (Nussbaum, 2000). Robeyns (2003) makes a major contribution to the application of capability theory to Western cultures by including five additional nuanced capabilities relevant to Western life: mental well-being, domestic work and nonmarket care, mobility, time-autonomy, and religion.
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Nussbaum (2000) distinguishes between internal and combined capabilities. Internal capabilities relate to personal capacities, and combined capabilities result from association with a group. Nussbaum suggests that capability structures are malleable through collective action to influence the actors as well as those affiliated with the group. Formal organizations and groups are examples of combined capabilities in action. For example, the National Organization for Women (NOW) is the largest and among the oldest U.S. feminist organizations and a strong indicator of women’s political association with one another because NOW routinely collaborates with other women’s and feminist organizations to advance women’s status (Sapiro, 1986).
Research Question and Hypotheses There is a substantial gap in our understanding of how theoretically promising macrocontextual factors moderate or mediate individual risk for IPV perpetration. Analysis of associations is inadequate. If the macro context directly protects against perpetration, this is important. More likely, macro context reduces risk factors or impedes the relationship between risk factors and IPV. Given that evidence suggests that relevant macro contexts depend on IPV type, this study focuses on how gendered macro contexts influence IPV associated with men’s gender-influenced risk for IPV as an initial area to explore the role of macro context in IPV. The theory of gender and power implies that gendered structures shape behavior and expectations in relationships. Capability theory suggests that there are multiple domains of women’s power that influence functioning. Based on these theories, this study’s conceptual perspective is that the collective power of a group influences behavior of individuals within the group by shaping perceived options, opportunities, and results. In addition, the power and abilities of a group across multiple domains shape the perceived options, opportunities, and results associated with how individuals behave toward individuals who are within a group. Based on the available evidence and literature, this study asks whether and how macro contexts act as protective factors in the relationship between men’s risk for perpetration and physical or psychological IPV perpetration. This study examines community-level factors that relate to known risk factors or are informed by capability theory to the extent that county estimates are available. In addition, community crime rate and population density are included in the analysis to control for competing explanations in the examination of protective factors. Individual risk factors that parallel gendered macro contexts in the conceptual literature are examined: male dominance, hostility toward women, and desire for control in relationships. Drawing on the research regarding macro-level risk and protective factors and the theoretical frameworks reviewed, this study tests two hypotheses using county as a proxy for macro context. Hypothesis 1: County context will not have a significant direct effect on the likelihood of IPV perpetration. Although IPV rates vary by region, state, county, and neighborhood, the within-group rates are heterogeneous. Take, for example, the
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household rate in a county with a very high incidence rate of IPV. We would expect that some households would have very high rates, while others would have no IPV. Thus, the within-county heterogeneity may be greater than that between counties. Rather, county context is hypothesized (2a-2c) to influence IPV through individual risk, and reports of IPV are, fortunately, not extremely high overall. Thus, the first hypothesis expects no significant direct effect. Hypotheses 2a-2c: The effect of an individual’s level of attitudes toward male dominance (2a), hostile sexism (2b), or control-seeking (2c) on likelihood of IPV perpetration will be significantly weaker among individuals in counties where women overall have more power, independence, and resources, controlling for county crime rate and population density. The county factors are expected to change the effect that each individual-level factor has on the likelihood of IPV perpetration. Furthermore, it is expected that county factors will have the largest effect size in moderating control-seeking compared with male dominance or hostile sexism.
Method This study uses data from a cross-sectional survey of all adult, in-state, degree-seeking undergraduate students at a large, state-funded university in the Southeastern United States to examine relationships among women’s county context, male dominance, hostile sexism, control-seeking, and men’s IPV perpetration against women. The survey took place in the fall of 2009. In addition to attitudes and past-year IPV perpetration, the survey included relationship status, age, and academic level. To assess the influence of women’s county environment on the relationship between men’s risk for IPV and IPV perpetration, the present study examines past-year behaviors among male undergraduate students at the beginning of their first semester in college. This provides insight into home county behavior the year prior to entering college. After obtaining institutional review board approval, an invitation letter, informed consent, and link with an embedded unique identifier to take the survey were sent via campus email after the add/drop deadline, with three weekly (Sunday) reminders. The invitations and reminder emails included first names in the salutation. Invitation letters informed students that they were entered for a chance to win a US$50 gift card with immediate notice of results upon completion.
Measures for Individuals Level of attitudes supporting male dominance was measured with the dominance subscale of the Male Role Norms Inventory–Revised (MRNI-R; Levant et al., 2007). The MRNI-R has been used extensively over the past 15 years (Levant & Richmond, 2007), but the revised scale has better discriminant construct validity, higher reliability (α = .96), and better captures contemporary concepts of masculinity (Levant et al., 2007). The MRNI-R dominance subscale is a seven-item scale with α reliability of .84. The response options were a 6-point Likert-type scale (strongly disagree to strongly
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agree), coded 0 to 5. The male dominance score was computed as the average of all items, resulting in a continuous variable ranging from 0 to 5, with 5 indicating the highest level of attitudes supporting male dominance. Hostile sexism was measured with the Hostile Sexism scale, an 11-item scale that assesses respondent attitudes toward women’s authority and women’s complaints (Glick & Fiske, 1996). The reported scale reliabilities ranged from α = .80 to .92 across six studies (Glick & Fiske, 1996) and had strong evidence of concurrent validity with related measures (Forbes et al., 2004). The Hostile Sexism scale has six response options (strongly disagree to strongly agree) that were scored 0 to 5 (Glick & Fiske, 1996). The score was an average of the items after reverse scoring three items, resulting in a continuous variable ranging from 0 to 5, with 5 indicating the most support for hostile sexism. Control-seeking was measured with five items from the 10-item restrictive subscale of the Dominance Scale (Hamby, 1996). The subscale had an α of .73. Five items were omitted because they assess insecurity (e.g., “I tend to be jealous”) and had factor loadings less than 0.50 (Hamby, 1996). The items had six response options (strongly agree to strongly disagree). The score was an average of the five items resulting in a continuous variable ranging from 0 to 5, with 5 indicating the most control-seeking. Respondents were asked whether they had at least one partner in the past year (yes/no), and whether those “intimate/dating relationship partner(s) in the past year were”: male (yes/no), female (yes/no). Respondents with only female partners were retained in this analysis. IPV perpetration was operationalized as self-reported psychological and physical abuse perpetrated against an intimate relationship partner in the past year. This was measured with the physical assault and psychological aggression subscales of the Revised Conflict Tactics Scale (CTS2) for perpetration with dichotomous response options. Respectively, these subscales have 12 and 8 items and Cronbach’s α of .86 and .79, respectively (Straus, Hamby, Boney-McCoy, & Sugarman, 1996). For this study, IPV was a multinomial variable coded 0 for no physical or psychological abuse, 1 for psychological abuse only, and 2 for physical and psychological abuse. The survey included age in years, academic level (fresher = 1 through senior = 4), and sex (M/F). Respondents aged 18 to 19 years who selected “M” were retained in analyses.
County Measures Resources were measured with four variables derived from the 2010 U.S. Census 5-year estimates for 2006-2010. These four items comprise the index of women’s absolute status (Martin et al., 2006). Martin et al.’s index uses data from the 2000 U.S. Census on women’s median income: the percentage of women aged 25 years and above with a bachelor’s degree, the percentage of women 16 and above in the workforce, and the percentage of women 16 and above in management, professional, and related occupations. For the present study, all percentages were based on 5-year estimates to more closely reflect the county environment at the time of participant’s high
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school graduation (2008) and to minimize the influence of chance fluctuation. Martin et al. found the four items comprised a single factor via principal components analysis (Martin et al., 2006). Resources is a continuous variable theoretically ranging from 0 to 4.0. Independence was operationalized as women’s status relative to men’s and was measured with four items from the relative status of women index (Martin et al., 2006). Martin et al. computed the ratios of women’s levels to men’s to obtain relative status. Again, Martin et al. found the four items comprised a single factor via principal components analysis. For the present study, these variables were computed on a per-county basis using the 2010 U.S. Census data 5-year estimates for 2006-2010. As noted, 5-year estimates better approximate the county environment at the time of graduation (2008) and minimize chance fluctuation. Independence was a continuous variable theoretically ranging from 0 to 1.0. Power was operationalized with six variables to reflect women’s overall capabilities: force, association, law-making, workforce, religion, and reproduction. Each variable represents a concept that entails a level of power and increased combined capability for action. Each variable was coded so that higher levels indicate more power among women at the county aggregate level. The measurement and rationale of each power variable are described below. Force represents the sanctioned access to the use of force and the authority to enforce the law. This is measured by the proportion of police department and sheriff’s office law enforcement positions held by women versus men. The proportion of female-to-male law enforcement positions per county was obtained from the Florida Department of Law Enforcement’s 2007 Criminal Justice Agency Profile (Florida Department of Law Enforcement, 2008b). This was a continuous variable ranging from 0 to 1. Women’s association through interest groups increases women’s political power (Sapiro, 1986). Association was operationalized as the number of NOW members for 2009, per 10,000 county population. Membership per county was obtained from the Florida NOW state registry. Women in elected positions with law-making authority is an important measure of women’s power. Law-making was measured by the average proportion of women to men elected to state-level offices in the Florida Senate and Florida House of Representatives for the 2008 regular session (for senators, it is the 2008-2010 election period). The proportion ranged from 0 to 1, with higher numbers indicating a higher proportion of women in elected office. Workforce was computed as the proportion of women-owned businesses over all businesses within each county in 2007. The 2007 U.S. Survey of Business Owners reports the total number of women-owned businesses and the total number of privately owned businesses per county by owner sex. Workforce was a continuous variable ranging from 0, no women-owned businesses, to 1, all privately owned businesses were women-owned. Religion was based on the level of orthodoxy in a community as a proxy for women’s power and authority in religious organizations. Religion has been implicated in
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IPV in various ways, both as a protective factor and as a risk factor (Hall & Duvall, 2003), and institutional structures that promote unequal power between men and women have been identified as a risk factor for IPV perpetration (Heise, 1998). Data were obtained from the Religious Congregations and Membership Study (Association of Religion Data Archives, 2000). Religion was computed as the proportion of the population who were not members of Catholic, Muslim, Jewish Orthodox, and other Orthodox Christian churches. The population estimate was based on 2000 Census reports per county. Reproduction was operationalized by the number of women-headed households with dependent children above the poverty line as a proportion of all women-headed households with dependent children, as reported by the 2010 U.S. Census. The continuous variable ranged from 0 to 1, with 1 indicating that all women-headed households with children were above poverty.
County Descriptive Variables Violent crime rate and population density were included as potential explanatory variables that have been identified as prominent community-level IPV risk factors (Farmer & Tiefenthaler, 2003; Martin et al., 2006). The violent crime rate per 1,000 population was obtained from the 2007 Florida Department of Law Enforcement Uniform Crime Reports (Florida Department of Law Enforcement, 2008a) and used the county population estimate for 2007 (Office of Economic and Demographic Research, 2008). Violent crimes are murder, forcible sex offenses, robbery, and aggravated assault (Federal Bureau of Investigations, 2004). Population density was obtained by the 2010 Census 5-year average population estimate living in a rural area (according to the Census definition of not urban).
Analyses Analyses were conducted using IBM SPSS Statistics Version 19 for univariate descriptive analyses and screening for normality, analysis of missing data, bivariate analyses comparing the sample with the sample frame, and basic psychometric analyses, including reliability and the standard error of measurement (SEM). Because the hypotheses involve nested data in which individuals are nested within county contexts, HLM 7 was used to conduct a multilevel analysis via hierarchical generalized linear regression using a multinomial model. For two-level analyses, Heck and Thomas (2000) recommend a minimum of 20 county-level observations each with at least 30 individual-level observations for adequate power. There were fewer than 20 counties with 30 observations. To determine appropriate sample size when county-level units are of greatest theoretical interest, Raudenbush and Bryk (2002) recommend a power analysis based on the method of Cochran (1975). The computation is given by,
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nopt
C2 1 − p 2 = * p C1
1
1 − .007 2 = 1* =11.41, .007
where C2/C1 is the relative effect of increasing the county sample size, C2, versus individuals within counties, C1, and ρ is a measure of the amount of within-county variability called the intra-county correlation. There was virtually no cost of sampling counties, but increasing the within-county sample was not feasible for two reasons. The contact list from the University Registrar did not include information on county of origin, and the distribution of university students from each county is uneven and extremely low in several counties. Thus, the ratio could theoretically be set very small and the optimal n would approach 1. Conservatively, the ratio was set to 1, and the optimal n within each county was estimated at 12. There were no counties with 12 to 14 respondents, and so 15 was the minimum. This resulted in a county sample of 23 counties (just over a third of all counties) that were well dispersed across the state.
Results The student sample comprised 604 first-year undergraduate males aged 18 to 19 who had a female relationship partner in the past year and who were from one of the selected counties. The majority (95%) of respondents were 18 (M = 18.1, SD = 0.26). Race was not obtained from the respondents to protect anonymity among minorities in the fresher class, but the males in the sample frame were 72% White, 11% Black, 13% Hispanic, and 4% other races. Sample size includes exclusion of three respondents above 19 years of age, eight respondents missing the second survey page (see below), and seven respondents with improbable response patterns. Improbable responses were identified as the same response on all hostile sexism items, including the reversescored items, and uniform responses on control-seeking and male dominance items.
IPV Perpetration Half the respondents reported perpetrating some form of IPV within the past year, with 15% reporting physical IPV and 35% reporting only psychological IPV (see Table 1). The physical IPV rate was within the range (14%-42%) identified by Straus (2004). The IPV perpetration items were on the second survey page and had a 98.5% completion rate. Investigation of missing data patterns revealed a monotone missing pattern. The discrepant completion rates may have been due to difficulties in progressing between survey pages. Eleven respondents emailed the researcher during data collection regarding difficulty advancing pages. Because the missing rate was moderate and likely due primarily to web survey platform failure, the missing responses were deemed missing at random and were excluded from analyses.
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Table 1. Descriptive Statistics for Individual and County Variables. M (%) Individual-level variables (N = 604) IPV perpetration Psychological only Physical Control-seeking Hostile sexism Male dominance County-level variables (N = 23) Formal power Informal power Independence Resources Violent crime rate Population density
SD
Min–max
(50%) (35%) (15%) 2.48 2.53 1.78
0.89 0.78 1.13
0.0-5.0 0.2-4.6 0.0-5.0
0.47 0.77 0.90 2.03 6.11 0.11
0.32 0.54 0.21 0.83 2.20 0.10
0.0-1.1 0.0-2.1 0.0-0.8 0.0-2.7 2.6-11.2 0.0-0.4
Note. IPV = intimate partner violence.
Attitude Scale Reliabilities Basic reliability statistics for the attitude and perpetration measures support use for this study. The scale reliabilities were within acceptable ranges for research purposes. Cronbach’s α was .91 for male dominance (SEM = 0.33), .83 for hostile sexism (SEM = 0.32), .79 for control-seeking (SEM = 0.41), .80 for physical IPV, and .73 for psychological IPV. The SEM is the scale standard deviation times the square root of 1 minus coefficient α. According to convention, the SEM should be 5% or less of the scale range. Thus, all SEMs were elevated. However, the SEM recommendation is based on scales with range 100. With relatively small-scale ranges, the adequacy of the 5% general rule is somewhat ambiguous, and the rule does not apply to dichotomous scales. The results suggest that male dominance and hostile sexism scores varied moderately in proportion to the scale range. The SEM was elevated for control-seeking, indicating a larger than desired spread in scores.
Sexist Attitudes Overall, reported attitudes supporting male dominance were less prevalent than hostile sexism and control-seeking (see Table 1). The average response on male dominance was 1.78 (SD = 1.13), which indicates an average between disagree and slightly disagree with male dominance statements. Male dominance was positively skewed (skewness = 0.373, SES = .10, p < .001) but with no significant departure from normal kurtosis at the alpha = .01 level (kurtosis = −0.425, SEK = 0.196, p = .031). The variable responded poorly to transformations. Given this and the moderate departure from
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normality, the original variable was retained. At 2.53 (SD = 0.78), hostile sexism’s average response was notably higher than that for male dominance. Hostile sexism’s score indicates an average between slightly disagree and slightly agree. Controlseeking was nearly as high as hostile sexism, with an average of 2.48 (SD = 0.89), indicating an average between slightly disagree and slightly agree with desire for control. Other than male dominance, there were no significant departures from univariate normality. However, the standard deviations for control-seeking and hostile sexism were smaller than expected, given the scale range. Attitude questions about male dominance, hostile sexism, and control-seeking were located on the first survey page and had at least a 99.9% completion rate. Analysis of missing data patterns suggests that data were missing at random. The missing attitude scale items were computed via single imputation using ordinary least-squares regression and a randomly selected residual added to the predicted value. Each full scale was used to predict missing items. When multiple items were missing, pared down models were used with at least 2 of 4 predictors for control-seeking, 7 of 10 for hostile sexism, and 4 of 6 for male dominance.
County-Level Factor Analysis The county indexes for resources, independence, and power were developed using factor analysis by principal components on the full set of 67 counties. Given the small sample, factor analysis was expected to produce modest results. The aim was to verify the single-factor dimensionality for independence and resources found in previous studies and to make an initial attempt to reduce the dimensionality of the power variables. Consistent with Martin et al. (2006), this analysis entered the four variables for resources (women’s median income, education level, percentage in management positions, and employment rate) into a factor analysis by principal components using varimax rotation. All correlations among the four variables were significant and sufficiently large (r = .49-.80, p < .001), and the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.72. Inspection of the anti-image correlation matrix revealed only small correlations. All items loaded onto one factor that accounted for 56% of total variance. For the independence index, variables based on the ratio of women to men on each of the four variables were entered into a factor analysis using varimax rotation. Five of the correlations among the four variables were significant and sufficiently large (r = .30-.63, p < .001-.012), and the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.67. Again, all items loaded onto a single factor accounting for 56% of total variance. The amount of variance explained for both factors was lower than the general cutoff of 60%. However, the aim of this analysis was to verify factor structure in this dataset, which was supported by the results. Given the theoretical interest in these items, comparability to prior work, and likely influence of sample size on the variance explained by these factors, standardized variables were averaged together to create the independence and resource indexes. Six variables to assess a macro construct of women’s power in the community were entered into a factor analysis by principal components using varimax rotation: force,
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law-making, workforce, reproduction, religion, and association. The variables satisfactorily met the criteria for factor analysis, given the small sample of 67 counties. Five correlations among the six variables were significant and sufficiently large (r = .26-.64, p < .001-.049), and the Kaiser–Meyer–Olkin measure of sampling adequacy was 0.58, only slightly below the desirable 0.60. Inspection of the anti-image correlation matrix revealed two moderately elevated correlations (r = .44). This may suggest cross-loading and the need for correlated factors. However, the highest cross-loading in the component matrix was 0.28 (proportion of women-owned businesses with informal power), which is well within acceptable ranges. An oblique (promax) rotation failed to reduce the cross-loading. The factor analysis for the power variables revealed two factors, which were labeled formal power and informal power and accounted for more than 59% of total item variance. Formal power comprised the proportion of women in law enforcement, the proportion of women in elected offices, and the proportion of women-owned businesses. Informal power comprised the proportion of women-headed single-parent households above poverty, the proportion of religious adherents who were not affiliated with Orthodox churches, and the proportion of the population who were members of NOW. Because the raw variables had different ranges and distributions, the standardized variables were averaged together according to the factor loading to create two summated scales.
County Context Of the 23 counties in this study, the average crime rate per 1,000 population was 6.11 (SD = 2.20; see Table 1). The average percentage of the county population living in a rural area, as determined by the U.S. Census, was 11% (SD = 0.10). The average level on the resources index was 2.03 (SD = 0.83, range = 3.91). The average for independence was 0.90 (SD = 0.213, range = 0.75). The average informal power level was 0.77 (SD = 0.539, range = 2.12) and formal power was 0.47 (SD = 0.315, range = 1.05). Given the relatively small sample (N = 23 counties), there were no significant departures from normality, and skewness and kurtosis were within acceptable ranges (skewness = −.691-.748, SES = .481; kurtosis = −.959-1.095, SEK = .935). Note that although kurtosis was elevated for resources based on conventional cutoffs, this was not a significant departure because the Fisher statistic was 1.17.
Tests of Hypotheses Because the outcome variable is multinomial, hierarchical generalized linear modeling was used to test the three study hypotheses. The attitude measures were meancentered, so that predictor effects could be estimated with respect to average attitude levels. To screen for multicollinearity among individual-level (Level 1) independent variables (male dominance, hostile sexism, control-seeking, age residual, and academic level), simple bivariate correlations were examined. There was a moderately high correlation between male dominance and hostile sexism (r = .62, p < .001). To investigate the influence of the correlation, each variable was regressed on the
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remaining independent variables. The largest R2 values were .36 and .38 for male dominance and hostile sexism as dependent variables, respectively. These R2 values were well below the .80 cutoff for multicollinearity (Menard, 2001). All correlations among county variables were below .50; two (r = .44-.45) were significant at the alpha = .05 level, and none was significant at the alpha = .01 level. Hypothesis 1. Results supported the first hypothesis that the county context would not have a significant direct effect on the likelihood of IPV perpetration. This hypothesis was tested with a two-level multinomial model with likelihood of IPV perpetration as the dependent variable at Level 1 and a random coefficient at the county level. Following the procedure for HLM multilevel analysis described in Raudenbush and Bryk (2002), a model with no county predictors was examined for a significant betweengroups effect before proceeding to enter county-level predictors. There was no significant county effect on the likelihood of psychological IPV perpetration (χ2 = 31.66, df = 22,603, p = .083) nor physical IPV perpetration (χ2 = 19.63, df = 22,603, p ≥ .500). Therefore, county-level variables were not entered as predictors of county variability in the likelihood for IPV. Hypothesis 2a. Results did not support the potential link between county context, male dominance, and IPV perpetration. Hypothesis 2a stated that the effect of an individual’s level of attitudes supporting male dominance on likelihood of IPV perpetration would be significantly weaker among individuals in counties where women overall have more power, independence, and resources, controlling for crime rate and population density. The three attitude predictors were entered into the multilevel model at Level 1, each with a random effect at the county level. Male dominance did not have a significant influence on the likelihood of IPV perpetration (psychological β = −.24, SE = 0.12, t ratio = −2.0, p = .057; physical β = −.19, SE = 0.18, t ratio = −1.1, p = .294), and subsequently there was no significant county effect to be explained (see Table 2). Hypothesis 2b. As with male dominance, results did not support the potential link between county context, hostile sexism, and IPV perpetration. Hypothesis 2b stated that the effect of an individual’s level of hostile sexism on likelihood of IPV perpetration would be significantly weaker among individuals in counties where women overall have more power, independence, and resources, controlling for county crime rate and population density. As stated above, the three individual attitude predictors were entered into the multilevel model at Level 1, each with a random effect at the county level. However, in the two-level model, hostile sexism did not have a significant influence on the likelihood of IPV perpetration (psychological β = .29, SE = 0.16, t ratio = 1.80, df = 22, p = .085; physical β = .23, SE = 0.21, t ratio = 1.12, df = 22, p = .273), and subsequently there was no significant county effect to be explained (see Table 2). Hypothesis 2c. Results supported Hypothesis 2c, suggesting a link between county context, control-seeking, and IPV perpetration. Hypothesis 2c stated that the effect of
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Table 2. Multilevel Model, Random Coefficient, and Mixed Effects of County and Individual Factors (N = 604,23). Fixed effect Psychological IPV only Intercept, β0j(1) Intercept, γ00(1) Male dominance, Intercept, γ10(1) β1j(2) Hostile sexism, Intercept, γ20(1) β2j(2) Control-seeking, Intercept, γ30(1) β3j(1) Physical IPV Intercept, β0j(2) Intercept, γ00(2) Male dominance, Intercept, γ10(2) β1j(2) Hostile sexism, Intercept, γ20(2) β2j(2) Control-seeking, Intercept, γ30(2) β3j(2) Resources, γ31(2) Independence, γ32(2) Formal power, γ33(2) Informal power, γ34(2) Crime rate, γ35(2) Population density, γ36(2)
β
SE
t ratio
p value
−0.42 −0.24
0.10 0.12
−4.02 −2.00
