A meta-analysis of childhood maltreatment and intimate partner violence perpetration

Journal Pre-proof A meta-analysis of childhood maltreatment and intimate partner violence perpetration

Sen Li, Fengqing Zhao, Guoliang Yu PII:

S1359-1789(18)30360-4

DOI:

https://doi.org/10.1016/j.avb.2019.101362

Reference:

AVB 101362

To appear in:

Aggression and Violent Behavior

Received date:

27 December 2018

Revised date:

27 October 2019

Accepted date:

11 December 2019

Please cite this article as: S. Li, F. Zhao and G. Yu, A meta-analysis of childhood maltreatment and intimate partner violence perpetration, Aggression and Violent Behavior(2018), https://doi.org/10.1016/j.avb.2019.101362

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© 2018 Published by Elsevier.

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A meta-analysis of childhood maltreatment and intimate partner violence perpetration Sen Lia

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School of Education, Renmin University of China b

School of Education, Zhengzhou University

Institute of Psychology, Renmin University of China

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Author Note

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c

Fengqing Zhaob Guoliang Yu c

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Beijing, China, 100872, Email: [email protected].

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Sen Li, School of Education, Renmin University of China, No. 59 Zhongguancun Street, Haidian District,

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Fengqing Zhao, School of Education, Zhengzhou University, No.100 Science Avenue, Zhengzhou City, Henan Province, China, 450001. Email: [email protected].

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Guoliang Yu, Corresponding author, Institute of Psychology, Renmin University of China, No. 59

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Zhongguancun Street, Haidian District, Beijing, China, 100872, Tel:86-13901335243, Email: [email protected]. ORCID: 0000-0002-7263-6783.

Acknowledgement

The present research was supported by the Outstanding Innovative Talents Cultivation Funded Programs 2018 of Renmin Univertity of China and National Natural Science Foundation of China (81571337).

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A meta-analysis of childhood maltreatment and intimate partner violence perpetration Abstract Intimate partner violence (IPV) perpetration is a serious public health concern. It is necessary to understand and identify the antecedents of IPV perpetration. This article aimed to report a meta-analysis of the relationship between childhood maltreatment (CM) and IPV perpetration, and explore the moderating effects of gender and

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marital status. Examination of the literature containing quantitative measurements of both CM and IPV

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perpetration produced a sample of 87 effect sizes (N = 32,544) for review. Results based on random-effects model indicated a significant positive relationship between total CM and IPV perpetration (r = .16, p < .001).

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Further subgroup analyses revealed that all three types of CM (childhood physical abuse, psychological abuse,

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and sexual abuse) were positively related to IPV perpetration (r = .17, p < .001; r = .13, p < .001; r = .13, p

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< .001 respectively). Moreover, the moderation analyses revealed that the association between CM and IPV

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perpetration was stronger for males than for females (Q = 15.73, p < .001). However, this relation is not moderated by marital status (Q = .16, p = .692). In conclusion, there is an association between CM and IPV

Key Words

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perpetration, and it is moderated by gender.

Childhood maltreatment; Intimate partner violence perpetration; Dating violence; Marital violence; Intergenerational transmission of violence

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Introduction Intimate partner violence (IPV) perpetration, defined as the behavior within an intimate relationship that

causes physical, sexual, or psychological harm (Breiding, Basile, Smith, Black, & Mahendra, 2015), is a serious public health concern (Garcia-Moreno, Jansen, Ellsberg, Heise, & Watts, 2006). It affects people across age, sex, socio-economic status, and ethnicity (Popescu, Dewan, & Rusu, 2010; Vasquez, 2015). To be specific, IPV

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perpetration not only elevates victims’ risks of post-traumatic stress disorder (PTSD) (Iverson, Dardis, &

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Pogoda, 2017), depression (Costa & Gomes, 2018; Godoy-Ruiz, Toner, Mason, Vidal, & McKenzie, 2015), and suicidal behaviors (Peltzer & Pengpid, 2017), but also makes perpetrators experience more guilt, depression,

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fear, and anxiety (Bevan & Higgins, 2002; Dutton, 2000; Kernsmith, 2006). Given the high human and

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monetary toll resulting from IPV perpetration, it is important to understand and identify the antecedents of IPV

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perpetration.

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Based on the intergenerational transmission of violence theory (Kalmuss, 1984), the relationship between experiencing childhood maltreatment (CM) and IPV perpetration has been the focus of a fair amount of research

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attention in these years (e.g., Bell & Higgins, 2015; Kwong, Bartholomew, Henderson, & Trinke, 2003; Reyes et al., 2015; Smyth, Gardner, Marks, & Moore, 2017). However, several issues connecting CM and IPV perpetration remain ambiguous. First, there are no conclusive estimates regarding the extent to which CM is linked to IPV perpetration. Some null findings even reported that children experiencing CM did not grow up to be violent adults (Hotaling & Sugarman, 1990; Lavoie et al., 2002). Second, existing research mostly targeted only one CM type or the total CM effects (e.g., Smith-Marek et al., 2015; Steel, Watkins, & DiLillo, 2017; Trabold, Swogger, Walsh, & Cerulli, 2015; Zurbriggen, Gobin, & Freyd, 2010). There is not an integrated picture of how various CM types (i.e., childhood physical abuse, psychological abuse, sexual abuse, and neglect) link to IPV perpetration specifically. Third, the inconsistencies indicate that there may be some key moderators 3

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(e.g., gender and marital status) in the relation between CM and IPV perpetration to be explored. While it is difficult to figure out these questions from individual studies, collecting these studies can provide useful information through a meta-analysis. In addition, by attending to gender and marital status distribution of the samples, a meta-analysis can examine the moderating roles of gender and marital status efficiently (compare with, for instance, conducting another study involving multiple CM types with females and

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males, dating partners and marital couples). More critically, because a meta-analysis is based on multiple studies,

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it typically involves a large number of participants from a more diverse background. Therefore, a more definitive conclusion can be drawn from it than from a single study. Based on these strengths, it is possible that

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our understanding of the intergenerational transmission of violence could be enhanced by current meta-analysis.

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2. Background

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2.1. The relationship between CM and IPV perpetration

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Childhood maltreatment, defined as violent acts including physical abuse, psychological abuse, sexual abuse, and neglect against children (Pinheiro, 2006), is one of the most commonly studied risk factors for IPV

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perpetration (Bell & Higgins, 2015; Edwards, Dixon, Gidycz, & Desai, 2014; Hou, Yu, Fang, & Epstein, 2016; Smyth et al., 2017). Actually, the phenomenon that experiencing maltreatment in childhood heightens the likelihood of IPV is labeled as the “intergenerational transmission of violence” (Kalmuss, 1984). In this study, however, we only focus on IPV perpetration in the context of an intimate partner relationship. There are two theories accounting for this phenomenon. The first one is the General Aggression Model (GAM) (Anderson & Bushman, 2002), which provides a parsimonious account of why aggression occurs in terms of three levels: personal and situational factors, internal states, and outcomes of appraisal and decision-making processes. According to GAM, people with CM experience tend to normalize the utility of violence and form aggressive scripts. These interpretational and behavioral scripts will further influence individual’s preparedness for IPV 4

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perpetration. Namely, when interacting with their intimate partners, those with CM experience are more likely to use violence to deal with stressors and frustrations, as they regard it acceptable (Wareham, Boots, & Chavez, 2009). The second one is the Developmental Traumatology Model (DeBellis & Putnam, 1994), which posits that one direct consequence of CM is the increased risk of posttraumatic stress disorder (PTSD), identified by avoidance, hyperarousal, and mistrust of others. Individuals with PTSD symptoms tend to believe that others

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will hurt or humiliate them deliberately. Therefore, they become more irritable and hostile, which may in turn

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lead to high levels of aggression, including IPV perpetration (Taft, Watkins, Stafford, Street, & Monson, 2011). In this sense, CM is related to IPV perpetration through the mediating roles of trauma-related symptoms.

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A good number of empirical studies have provided evidence in support of the association between CM and

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IPV perpetration, with samples ranging from adolescents to adults (Berthelot et al., 2014; Edwards et al., 2014;

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Godbout et al., 2017). Overall, individuals with CM experiences are especially at risk for relationship-based

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difficulties (Wekerle & Avgoustis, 2003) and have a two- to threefold increase in risk of IPV perpetration (Whitfield, Anda, Dube, & Felitti, 2003). In addition, all forms of CM, including childhood physical abuse,

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psychological abuse, sexual abuse, and neglect, are associated with subsequent IPV perpetration (Bell & Higgins, 2015; Berzenski & Yates, 2010; Brassard, Darveau, Péloquin, Lussier, & Shaver, 2014; Herrenkohl et al., 2004; McClure & Parmenter, 2017). These cross-sectional findings are confirmed by longitudinal studies as well. In a 20-year longitudinal study, Ehrensaft et al. (2003) suggested that CM is a very strong risk predictor of adult IPV perpetration, which strongly supported the intergenerational transmission of violence phenomenon. However, it should be noted that some studies, on the contrary, failed to establish such a relationship between CM and subsequent IPV perpetration (Hotaling & Sugarman, 1990; Lavoie et al., 2002; MacEwen & Barling, 1988). For instance, using the data from the National Family Violence Survey, Hotaling and Sugarman (1990) found no link between CM experience and IPV perpetration after controlling for other risk factors. 5

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Therefore, a meta-analysis is in need to clarify this inconsistency and explore the possible moderating variables which may influence these discrepant conclusions. 2.2. Differential effects of various CM types One aspect of the complexity in the intergenerational transmission of violence lies in the multiple dimensions of CM. Despite the commonalities in the impacts of all CM types, unique effects of each CM type

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may exist (Briere & Runtz, 1990; Moran, Vuchinich, & Hall, 2004). For instance, the strength of associations

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between CM and substance use (Moran et al., 2004), emotion dysregulation (Burns, Jackson, & Harding, 2010), as well as psychological difficulties (Gauthier, Stollak, Messe, & Aronoff, 1996) unexceptionally varied by CM

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types. In addition, Briere and Runtz (1990) demonstrated specific outcomes by different CM types, with

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psychological abuse linked to low self-esteem, physical abuse to aggressive behaviors, and sexual abuse to

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maladaptive sexual behaviors. More importantly, although all forms of CM experience are related to IPV

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perpetration positively (Bell & Higgins, 2015; Herrenkohl et al., 2004; McClure & Parmenter, 2017; Trabold et al., 2015), there are some evidence showing differential effects by CM types (e.g., Berzenski & Yates, 2010;

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Renner & Whitney, 2012). For example, Berzenski and Yates (2010) found that childhood psychological abuse predicted the IPV perpetration above the contribution of other CM forms. Thus, it is of great importance to examine differential effects of each CM type separately. 2.3. Potential moderators: Gender and Marital status Gender. When comes to the gender differences in the relation between CM and IPV perpetration, there are two conflicting viewpoints. A heavily emphasized opinion is associated with feminist theory, which suggests that the effects of CM on IPV perpetration are stronger for males than females (Walker, 1989). The reason is that compared to females, males are usually stronger, more powerful, and have more authority in the intimate relationships (Anderson, 2002; Caldwell, Swan, & Woodbrown, 2012). They are more likely to externalize their 6

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suffering in childhood (Friedrich, Urquiza, & Beilke, 1986) and socialize to be aggressive (Sugarman & Frankel, 1996). A good number of studies have published results supporting men as the main perpetrators of IPV (Edwards et al., 2014; Widom, Czaja, & Dutton, 2014). Some of them even found that the association between CM and IPV perpetration operated only for males but not females (e.g. Edwards et al., 2014; Simons, Burt, & Simons, 2008).

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However, some empirical studies disputed this opinion and indicated an equal possibility of

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intergenerational transmission of violence for females and males (Magdol, Moffitt, Caspi, & Silva, 1998; White & Widom, 2003). These findings are based on family violence theory, which demonstrates that just as

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maltreated men are more likely to perpetrate violence toward their intimate partners (Brassard et al., 2014; Taft,

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Schumm, Marshall, Panuzio, & Holtzworth-Munroe, 2008), maltreated women are inclined to commit IPV as

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well (Gay, Harding, Jackson, Burns, & Baker, 2013; Palazzolo, Roberto, & Babin, 2010). In a sex-specific

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model, Fang and Corso (2008) found direct effects of childhood physical abuse/ neglect on IPV perpetration for females only. It is necessary to identify the role of gender in the association between CM and IPV perpetration,

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so as to better understand the intergenerational transmission of violence phenomenon. Marital status. There are also two opposing views regarding to the moderating effect of marital status. Some researchers claim that dating violence and marital violence are two different areas of research, because each of them has its own unique features (Shorey, Cornelius, & Bell, 2008; Smith-Marek et al., 2015). Specifically, in dating violence, the partners are relatively younger. They are more immature and naive with intimate relationships (Smith & Donnelly, 2001). Interpersonal violence is more common and even peaks at this stage (Carlson, 1987; Nocentini, Menesini, & Pastorelli, 2010). In contrast, in marital violence, the couples typically have children and strong economic ties. They are less accessible to alternative relationships than dating partners (Shorey et al., 2008), which makes their intimate relationships more secure. Marital couples may work 7

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harder to maintain their intimate relationships and reduce the incidences of IPV. Therefore, the effects of CM on IPV perpetration would be stronger for dating partners than for marital couples. Conversely, some other researchers have studied the relationship between CM and IPV perpetration without differentiating dating violence and marital violence (e.g., Kwong et al., 2003; Novak, Smith, & Sandberg, 2015; Palazzolo et al., 2010). They claim that despite of the difference, there are also many

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similarities between dating violence and marital violence (Carlson, 1987; Shorey et al., 2008), which indicate

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the possibility of IPV continuity. In addition, children with CM experience are inclined to develop aggressive scripts. Once these scripts established, they are relatively stable and may persist whole in life (Rowell

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Huesmann, 1988). Some longitudinal studies supported this opinion, with results showing moderate stability of

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IPV over time (Fritz & Smith Slep, 2009; O’Leary & Smith Slep, 2003). A meta-analysis is in need to clarify

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2.4. Previous meta-analyses

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this inconsistency.

Two previous meta-analytic reviews have examined the effects of CM on IPV perpetration. Specifically,

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Stith et al. (2000) conducted a meta-analysis of the relation between witnessing or experiencing family violence in childhood and perpetrating violence in marital relationships. They also discussed the differential effects of gender. Fifteen years later, Smith-Marek et al. (2015) included more recent literature to meta-analyze this association. To go further, they explored the moderating role of the gender of abusive parents in the relation between CM and marital violence. It can be seen that these two meta-analyses only focused on the overall effects of CM and merely demonstrated its effect on marital violence. Neither the differential effects of specific CM types, nor the moderating effect of marital status was explored in these studies. Moreover, there are some methodological problems of the prior meta-analyses, which may ultimately impact the estimates. For instance, the publication bias issue. Smith-Marek et al. (2015) examined possible 8

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publication bias with fail-safe N and trim-and-fill tests. However, both these two methods are of limited utility for a number of reasons. First, the fail-safe N is based on significance tests that combine p-values across studies, and it focuses on the question of statistical significance rather than substantial significance. That is, it asks how many hidden studies are required to make the effect not statistically significant, rather than how many hidden studies are required to reduce the effect to the point that it is not of substantive importance (Borenstein, Hedges,

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Higgins, & Rothstein, 2011). Second, the Trim and Fill method depends strongly on the assumptions of the

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model for why studies are missing, and the algorithm for detecting asymmetry, which can be influenced by one or two aberrant studies (Borenstein et al., 2011). For Stith et al. (2000), they even did not perform this test at all.

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Meanwhile, they did not provide confidence intervals or demonstrate whether they had used random effects

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models or not. All these methodological problems have undermined the quality of the research.

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Therefore, adhering to most of the best practices in meta-analysis (e.g., using random effects models,

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providing confidence intervals, and examining publication bias), our meta-analysis aimed to address two extensions to previous work: first, we directly examined the differential effects of various CM types on IPV

2.5. Current study

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perpetration; second, we considered more moderators in addition to gender (e.g., marital status).

The current meta-analysis is centered on the following questions to provide an integrate picture of intergenerational transmission of violence. (1) Is there a relationship between CM and IPV perpetration? (2) Which CM type is more closely associated with IPV perpetration? (3) Are there any gender differences in the association between CM and IPV perpetration? (4) Are there any differences in this relation between married and unmarried ones? 3. Methods 9

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3.1. Literature search Four methods were used to search for relevant studies. First, we retrieved articles through a detailed search of PsycINFO, PsycArticles, EBSCO-ERIC, Medline, Google Scholar, and ProQuest Dissertations & Theses. The following three groups of key words were used: (1) childhood maltreatment, childhood abuse, childhood violence, and adverse childhood experience; (2) intimate partner aggression, intimate partner violence, intimate

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partner abuse, dating aggression, dating violence, dating abuse, spouse aggression, spouse violence, spouse

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abuse, and IPV perpetration; (3) intergenerational transmission of violence and cycle of violence. Second, we searched the in-press or online-first articles. Third, several dissertations, unpublished studies, and conference

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papers were obtained by contacting the authors personally. Finally, we also searched the reference lists of the

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retrieved articles manually to identify additional relevant publications. The literature search encompassed

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3.2. Inclusion and exclusion criteria

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articles published up to October 23, 2019.

Our search resulted in 4,869 records for screening to identify eligible studies. After examining the titles and

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abstracts of all the reference and discarding obviously irrelevant ones, 385 articles were identified as relevant. The 385 articles were included in or excluded from our meta-analysis based on the following criteria: (a) the studies needed to be empirical and quantitative (i.e., review, theoretical, and qualitative studies were excluded); (b) pearson correlation coefficients were provided; otherwise, sufficient information from which an effect size could be derived needed to be available; (c) studies had to be published in English; (d) studies reported IPV without differentiating results by perpetration were excluded; (e) studies reported the relation between witnessing interparental violence and IPV perpetration were excluded; (f) the CM data obtained from official record but not self-reported experiences were excluded; (g) studies violating the assumption of independent samples were excluded; (h) studies focused on same-sex partners or couples were rejected. In the end, 63 studies 10

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producing 87 effect sizes met the criteria for inclusion, see Fig. 1. 3.3. Coding Studies that met the inclusion criteria were coded for sample and study characteristics (e.g., gender, marital status, average age, sample sizes, method, CM types, CM measures, IPV perpetration measures, and publication status). The first author developed a coding manual which specified the coding categories and possible codes to

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be used for each study. Following the coding manual, the first author and another doctoral student coded all

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information contained in 63 studies (see Table 1). An agreement was reached in 95% of two coders’ coding. All

3.4. Multiple dependent results from a single study

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disagreements were resolved through discussion between the coders.

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First, we calculated the overall effect size of CM on IPV perpetration. The analyzing method followed the

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popularly adopted suggestion from Cooper, Hedges, and Valentine (2009). To be specific, when a study included

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multiple independent effect sizes, that is, correlation coefficients from separate independent samples, each effect size was coded separately. When a study reported multiple dependent effect sizes, namely, multiple correlation

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coefficients from the same sample, (e.g., a study reported correlation coefficients between physical abuse and IPV perpetration as well as psychological abuse and IPV perpetration with the same participants), simultaneously including them in a single meta-analysis can pose calculation problems of lower error variance estimate and inflating significant tests (Cooper, et al., 2009). Therefore, we averaged the effect sizes of every sample to calculate the overall effect size of CM and IPV perpetration (as mentioned in Stith et al., 2000). Second, we also conducted subgroup analyses of different types of CM. In this case, separate effects for each form of maltreatment were extracted from a specific study. 3.5. Statistical methods All analyses were completed in Comprehensive Meta-Analysis version 3 (Borenstein, Hedges, Higgins, & 11

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Rothstein, 2014). First, we meta-analyzed the magnitude of the effect size between overall CM and IPV perpetration. Second, we conducted subgroup analyses for different CM types. Finally, we explored the moderating effects of gender, marital status, and other study-related variables. Cohen’s (1992) suggested criteria were used to interpret the mean effect sizes, which demonstrated that r < .01 means trivial, r = .10 means small, r = .30 means medium, and r = .50 means large.

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What’s more, publication bias is prevalent. Studies with significant or large effects are more likely to be

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published than those with nonsignificant or low effects, which can distort meta-analyses and systematic reviews (Liu & Campbell, 2017). Following the standard practice for testing publication bias in meta-analysis, we

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applied three methods: (a) visually examining funnel plots of effect size standard errors to check possible bias. It

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was plotted with effect size on the horizontal axis and the precision of the study on the vertical axis. The points

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represent the given set of studies in this meta-analysis. The asymmetry among studies indicates the presence of

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publication bias (Batz-Barbarich, Tay, Kuykendall, & Cheung, 2018); (b) calculating fail-safe N, which is the number of null effect sizes that would have to be added to the sample to bring down the average effect sizes to

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become nonsignificant (Rosenthal, 1991). If it emerged that only a few studies are needed to nullify the effect, then we would be concerned that the true effect was zero. However, if it turned out that we need a large number of studies to nullify the effect, there would be less reason for concern (Borenstein et al., 2011); (c) applying the p-curve technique to examine publication bias (Simonsohn, Nelson, & Simmons, 2014). P-curve is the distribution of statistically significant (p < .05) p values in a meta-analysis, which can be used to assess selection bias in publication process. Its expected distribution is right-skewed: indicating more low significant p values than high significant p values. We conducted p-curve analyses to tests the skew of the p-value distribution and to confirm the above findings are not a result of publication bias. These analyses were conducted with applications from www.p-curve.com. 12

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4. Results 4.1. Sample description The data set included 87 independent effect sizes between total CM and IPV perpetration from 32,544 participants. From these, 83 came from published articles and 4 from unpublished articles; 15 came from prospective studies and 72 from retrospective studies; 25 were from married participants and 39 from unmarried

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ones; 26 independent effect sizes came from females and 37 from males. The sample sizes ranged from 44 to

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1,861 participants, and the average age of samples ranged from 18 to 41.83 years. 4.2. Effect sizes of CM and IPV perpetration

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The Q statistics were significant in our sample and I2 (66.99%) were over 30%, suggesting that the high

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level of heterogeneity was due to real differences among the selected samples as opposes to sampling error

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(Higgins, Thompson, Deeks, & Altman, 2003), which warranted the use of random effect model. For the 87

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independent samples between CM and IPV perpetration, the average effect size was .16 for the random-effects with the 95% Confidence Interval (CI) from .14 to .18 (see Table 2). According to Cohen (1992), this was a

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small effect size. Please see Fig. 2 for further information. 4.3. Subgroup analyses

Given that only two studies examined the effect of childhood neglect on IPV perpetration, it could not be meta-analyzed. Therefore, we conducted subgroup analyses of three CM types (i.e., childhood physical abuse, psychological abuse, and sexual abuse). Specifically, the average effect size between childhood physical abuse and IPV perpetration was .17 with the 95% CI from .14 to .20; the average effect size between childhood psychological abuse and IPV perpetration was .13 with the 95% CI from .07 to .19; the average effect size between childhood sexual abuse and IPV perpetration was .13 with the 95% CI from .07 to .18 (see also Table 2). Although the sample of participants who experience three CM types was not independent, we ran a 13

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between-category test of homogeneity to examine trends (see Stith et al., 2000). No significant differences were found between various CM types (Q = 2.31, p = .316). 4.4. Moderation analyses Gender as a moderator. Table 3 indicated that the effect size of CM on IPV perpetration was stronger for males (r = .20, 95% CI = .16 to .24, p < .001) than for females (r = .11, 95% CI = .09 to .14, p < .001). These

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two effect sizes had significant difference (Q = 15.73, p < .001).

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Marital status as a moderator. Moderation analyses revealed that the correlations between CM and IPV perpetration were positive both among married (r = .15, 95% CI = .12 to .18, p < .001) and unmarried ones (r

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= .14, 95% CI = .11 to .17, p < .001). The two effects were not significantly different (Q = .16, p = .692) (see

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Table 3).

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Study-related moderators. Moderation analyses were also performed based on the method and instrument

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types (different measures of CM and IPV). Testing results demonstrated that the effect sizes had no significant difference (Q = .63, p = .426) between retrospective (r = .17, 95% CI = .14 to .19, p < .001) and prospective

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studies (r = .15, 95% CI = .11 to .18, p < .001). There was also no significant difference among various CM measures (Q = 0.18, p = .671), (CTQ: r = .15, 95% CI = .11 to .19, p < .001; CTS: r = .24, 95% CI = .13 to .35, p < .001; self-designed measures: r = .17, 95% CI = .14 to .20, p < .001; other scales: r = .12, 95% CI = .09 to .15, p < .001) or different IPV measures (Q = 1.91, p = .385), (CTS: r = .15, 95% CI = .13 to .18, p < .001; self-designed measures: r = .22, 95% CI = .13 to .31, p < .001; other scales: r = .15, 95% CI = .10 to .20, p < .001). Therefore, none of these study-related variables significantly influenced effect sizes. 4.5. Publication bias analysis None of the analyses suggested publication bias. The funnel plot was symmetrical (see Fig. 3), suggesting that the sampling error is random, and this meta-analysis did not prioritize consideration of statistically 14

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significant effects over nonsignificant effects. The fail-safe N (ko = 4,358) was sufficiently large, which means that we needed a large number of studies (i.e., 4,358) to nullify the effect. Therefore, we do not need to concern that the true effect was zero. In addition, the p-curve showed significant right skew and no flatter than 33% (Fig. 4), suggesting that there are more lower significant p values (e.g., p < .001) than marginally significant p values (e.g., p = .049). Thus, extreme p-hacking was unlikely to occur.

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5. Discussion

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The overall question addressed in this meta-analysis concerned the association between CM and IPV perpetration. Consist with previous meta-analyses (Smith-Marer et al., 2015; Stith et al., 2000), a significant

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relationship was found between total CM and IPV perpetration (r = .16, p < .001). This result supported the

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intergenerational transmission of violence hypothesis. Namely, there is indeed a tendency that people who

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experienced maltreatment in childhood are more likely to perpetrate violence in their own relationships.

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However, what is noteworthy is that the effect sizes of CM on IPV perpetration in our study are relatively small according to Cohen’s standard (Cohen, 1992). It indicates that CM does play an important role in the

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development of IPV perpetration, but it is not a decisive one. Drawing from GAM (Anderson & Bushman, 2002), although the aggression is in part predicted upon learnt experience about violence, it is affected by other factors (e.g., contextual cues) as well. As a long-term effect of CM, IPV perpetration is fairly complicated and multi-determined. Some other influencing factors, like familial or social variables may alleviate the negative influence of CM (Cascio et al., 2017). For instance, despite the negative life events, individuals with high resilience can also do well in their lives (Walklate, 2011). What’s more, we further explored the impact of various CM types on IPV perpetration. The results showed that all three types of CM (i.e., childhood physical abuse, psychological abuse, and sexual abuse) were positively related to IPV perpetration (r = .17, .13, and .13 respectively), and no significant differences were 15

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found among various CM types. On the surface, it is seemingly that various CM types influence IPV perpetration similarly. We suggest that, however, the current study is unable to compare the relative strength of various CM types precisely due to the possibilities of co-occurrence of various CM types. Children who are victims of one form of CM are more likely to experience other forms of CM as well (Clemmons, DiLillo, Martinez, DeGue, & Jeffcott, 2003; Scher, Forde, McQuaid, & Stein, 2004). For example, in a retrospective

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study of over 17,000 participants, researchers found that victims of childhood sexual abuse were also inclined to

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experience childhood psychological abuse and childhood physical abuse (Dong, Anda, Dube, Giles, & Felitti, 2003). Moreover, childhood psychological abuse is identified as an underlying component of all other forms of

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CM (Wekerle et al., 2009). The overlap of CM types makes it difficult to identify the true difference of various

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CM forms on IPV perpetration.

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Furthermore, one of the most controversial questions in intergenerational transmission of violence research

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continues to be the gender issue (Edwards et al., 2014; Gay et al., 2013; Widom et al., 2014). In current meta-analysis, we found that the relation between CM and IPV perpetration was stronger for males than for

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females. Namely, compared to females who experienced CM, males with such experiences are more likely to perpetrate IPV. We suggest that this gender asymmetry result is due to the following three reasons. Firstly, in current meta-analysis, we included not only representative samples, but also clinical samples. According to Johnson (1995), studies with representative samples tend to study “common couple violence”, which is not gendered. Conversely, if data were from clinical samples, the IPV would be overwhelmingly perpetrated by men against women as a form of “terroristic control”. Secondly, as mentioned in feminist theory (Walker, 1989), males are usually much stronger than females. They are socialized to be aggressive or to use violence to settle arguments or conflicts (Sugarman & Frankel, 1996). Thus, they are more inclined to externalize their pain in childhood (Friedrich et al., 1986) and perpetrate IPV more frequently than do females. Third, males often take 16

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the initiative to inflict an injury to attain power or control in relationships (Johnson, 2006). By contrast, female IPV perpetration is mainly a result of IPV victimization. That is, in most cases, females perpetrate IPV just for defending or protecting themselves, and they are less likely to attack their partners unless it is quite necessary (Belknap & Melton, 2005). Indeed, a great number of studies have shown the gender differences in distinct forms of IPV perpetration (Mchugh, Rakowski, & Swiderski, 2013). As Conflict Tactics Scale (CTS) did not

of

consider contextual factors, such as who initiated the violence, who was injured, and whether the violence was

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in self-defense (Dobash & Dobash, 2004), more specific measurement tools are needed to clarify this issue. Finally, we explored the moderating effect of marital status on the relationship between CM and IPV

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perpetration. The results showed no significant difference of CM on dating violence and marital violence. It is in

re

line with GAM (Anderson & Bushman, 2002). According to this model, individuals with CM experience are

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more likely to develop aggressive scripts (Rowell Huesmann, 1988), form insecure attachment styles (Godbout

na

et al., 2017), and even create aggressive personalities through observational learning (Bushman & Anderson, 2002). Once established, they are stable predictors of IPV perpetration and difficult to change (Huesmann, Eron,

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Lefkowitz, & Walder, 1984). For instance, a longitudinal study examining changes in IPV found that violent behaviors in the dating context often continued into marital relationships, suggesting the continuity of IPV perpetration (O’Leary et al., 1989). What’s more, researchers have noted many similarities between dating and marital relationships. For example, individuals in both relationships tend to spend much time together on a wide range of different activities, exchange substantial personal information between the partners, and have high levels of emotional investment and involvement (Carlson, 1987). Dating violence can be viewed as a precursor to marital violence (Carlson, 1987; Smith & Donnelly, 2001). 6. Limitations and future directions There are several limitations for this work. Firstly, as with any meta-analysis, our findings are limited by 17

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the available evidence. In particular, we found that only 2 studies have addressed the relation between childhood neglect and IPV perpetration, and it was not included in the subgroup analyses. The limited evidence may reduce the power of the analyses and stability of the results. Future work is needed to illuminate the relationship between various CM types and IPV perpetration and to obtain more precise effect size estimates. Secondly, most studies in our meta-analysis were cross-sectional in design and based on retrospective reports. The reports of

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CM may be biased by later IPV perpetration, given that memories of past events are often distorted by later

ro

experiences (Hardt & Rutter, 2004). It could also be the case that IPV perpetrators reported more CM experience. Therefore, causality as implied in current study cannot be firmly established. Extensive longitudinal research

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needs to be done to tease apart issues of causation. Lastly, the effect sizes of CM on IPV perpetration in current

re

meta-analysis were relatively small. Drawing from GAM, there may be many other important influencing

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factors besides gender moderating the relation between CM and IPV perpetration. Future studies should explore

7. conclusion

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more protecting factors (e.g., resilience) in reducing this adverse influence of CM on IPV perpetration.

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The current meta-analysis found a significant relationship between total CM and IPV perpetration, which supported the intergenerational transmission of violence hypothesis. Subgroup analyses further revealed that all three types of CM (childhood physical abuse, psychological abuse, and sexual abuse) were positively related to IPV perpetration. Moreover, the moderation analyses revealed that the relation between CM and IPV perpetration was moderated by gender but not marital status.

18

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Abuse, 8(2), 204–219. Stith, S. M., Rosen, K. H., Middleton, K. A., Busch, A. L., Lundeberg, K., & Carlton, R. P. (2000). The intergenerational transmission of spouse abuse: A meta-analysis. Journal of Marriage and Family, 62(3), 640–654. Sugarman, D. B., & Frankel, S. L. (1996). Patriarchal ideology and wife-assault: A meta-analytic

Swogger, M. T., Walsh, Z., Kosson, D. S., Cashman-Brown, S., & Caine, E. D. (2012). Self-reported childhood

ro

*

of

review. Journal of Family Violence, 11(1), 13–40.

physical abuse and perpetration of intimate partner violence: The moderating role of psychopathic

Taft, C. T., Schumm, J. A., Marshall, A. D., Panuzio, J., & Holtzworth-Munroe, A. (2008). Family-of-origin

re

*

-p

traits. Criminal Justice and Behavior, 39(7), 910–922.

lP

maltreatment, posttraumatic stress disorder symptoms, social information processing deficits, and

na

relationship abuse perpetration. Journal of Abnormal Psychology, 117(3), 637–646. Taft, C. T., Watkins, L. E., Stafford, J., Street, A. E., & Monson, C. M. (2011). Posttraumatic stress disorder and

Jo ur

intimate relationship problems: A meta-analysis. Journal of Consulting and Clinical Psychology, 79(1), 22–33. *

Trabold, N., Swogger, M. T., Walsh, Z., & Cerulli, C. (2015). Childhood sexual abuse and the perpetration of violence: The moderating role of gender. Journal of Aggression, Maltreatment & Trauma, 24(4), 381–399.

Vasquez, M. (2015). Individual and interpersonal risk factors for physical intimate partner violence perpetration by biological sex and ethnicity. Violence and Victims, 30(1), 97–119. Walker, L. E. (1989). Psychology and violence against women. American Psychologist. 44(4), 659–702. Walklate,

S.

(2011).

Reframing

criminal

victimization:

resilience. Theoretical Criminology, 15(2), 179–194. 31

Finding

a

place

for

vulnerability

and

Journal Pre-proof

*

Wang, M. C., Horne, S. G., Holdford, R., & Henning, K. R. (2008). Family of origin violence predictors of IPV by two types of male offenders. Journal of Aggression, Maltreatment & Trauma, 17(2), 156–174.

Wareham, J., Boots, D. P., & Chavez, J. M. (2009). A test of social learning and intergenerational transmission among batterers. Journal of Criminal Justice, 37(2), 163–173. Wekerle, C., & Avgoustis, E. (2003). Child maltreatment, adolescent dating, and adolescent dating violence. In P.

of

Florsheim (Eds.), Adolescent romantic relations and sexual behavior: Theory, research, and practical

ro

implications (pp. 213–241). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers. Wekerle, C., Leung, E., Wall, A. M., MacMillan, H., Boyle, M., Trocme, N., & Waechter, R. (2009). The

Wekerle, C., Wolfe, D. A., Hawkins, D. L., Pittman, A. L., Glickman, A., & Lovald, B. E. (2001). Childhood

lP

*

re

youth. Child Abuse & Neglect, 33(1), 45–58.

-p

contribution of childhood emotional abuse to teen dating violence among child protective services-involved

na

maltreatment, posttraumatic stress symptomatology, and adolescent dating violence: Considering the value of adolescent perceptions of abuse and a trauma mediational model. Development and Psychopathology,

*

Jo ur

13(4), 847–871.

White, H. R., & Chen, P. H. (2002). Problem drinking and intimate partner violence. Journal of Studies on Alcohol, 63(2), 205–214.

White, H. R., & Widom, C. S. (2003). Intimate partner violence among abused and neglected children in young adulthood: The mediating effects of early aggression, antisocial personality, hostility and alcohol problems. Aggressive Behavior: Official Journal of the International Society for Research on Aggression, 29(4), 332–345. Whitfield, C. L., Anda, R. F., Dube, S. R., & Felitti, V. J. (2003). Violent childhood experiences and the risk of intimate partner violence in adults: Assessment in a large health maintenance organization. Journal of 32

Journal Pre-proof

Interpersonal Violence, 18(2), 166–185. Widom, C. S., Czaja, S., & Dutton, M. A. (2014). Child abuse and neglect and intimate partner violence victimization and perpetration: A prospective investigation. Child Abuse & Neglect, 38(4), 650–663. *

Yount, K. M., James-Hawkins, L., Cheong, Y. F., & Naved, R. T. (2018). Men’s perpetration of partner violence in Bangladesh: Community gender norms and violence in childhood. Psychology of Men &

ro

Zurbriggen, E. L., Gobin, R. L., & Freyd, J. J. (2010). Childhood emotional abuse predicts late adolescent sexual aggression perpetration and victimization. Journal of Aggression, Maltreatment & Trauma, 19(2),

na

lP

re

-p

204–223.

Jo ur

*

of

Masculinity, 19(1), 117–130.

33

Journal Pre-proof

12,570 records through research

11,996 records excluded: 

7,701 records were duplicated.



3,906 titles not further investigated.



389 abstracts rejected.

Full articles roughly read (574) 368 articles excluded: 189 no relevant articles.



142 studies reported no or insufficient statistics.



14 studies were not written in English.



23 were not quantitative studies.

ro

of



-p

Full-text assessed for eligibility (206)

re

143 full articles excluded: 

68 studies investigate the relationship between interparental violence and IPV perpetration. 67 studies only reported violence or violence victimization.



3 studies focused on lesbian.



1 experimental study.



4 articles used the same data.

(n = 63)

Jo ur

Articles included in meta-analysis

na

lP



Fig.1. Flowchart for the included studies in the meta-analysis.

34

Journal Pre-proof

Statistics for each study

-0.913 4.626 3.972 2.442 0.699 1.917 5.139 5.073 1.648 1.298 1.131 1.573 3.470 7.579 -0.697 1.854 0.873 0.968 3.901 2.022 4.082 1.800 2.670 3.808 3.356 2.738 4.883 1.997 2.216 1.779 2.241 4.398 3.583 4.292 7.630 2.403 0.982 5.976 2.072 1.572 3.890 3.022 2.888 0.929 1.430 1.959 4.731 2.232 2.332 2.469 0.991 0.890 3.360 0.985 0.504 0.773 4.762 3.713 1.634 3.429 3.767 2.583 3.781 1.973 3.378 1.367 -0.264 1.929 1.116 1.081 2.932 2.864 1.507 1.360 3.296 2.375 3.942 4.749 8.995 1.652 3.342 4.553 1.994 2.403 3.825 4.678 0.404 15.095

Jo ur

0.361 0.000 0.000 0.015 0.485 0.055 0.000 0.000 0.099 0.194 0.258 0.116 0.001 0.000 0.486 0.064 0.383 0.333 0.000 0.043 0.000 0.072 0.008 0.000 0.001 0.006 0.000 0.046 0.027 0.075 0.025 0.000 0.000 0.000 0.000 0.016 0.326 0.000 0.038 0.116 0.000 0.003 0.004 0.353 0.153 0.050 0.000 0.026 0.020 0.014 0.322 0.373 0.001 0.325 0.614 0.440 0.000 0.000 0.102 0.001 0.000 0.010 0.000 0.049 0.001 0.172 0.792 0.054 0.265 0.280 0.003 0.004 0.132 0.174 0.001 0.018 0.000 0.000 0.000 0.099 0.001 0.000 0.046 0.016 0.000 0.000 0.686 0.000

of

Z-Value p-Value

ro

0.054 0.294 0.419 0.283 0.330 0.352 0.204 0.442 0.206 0.469 0.216 0.317 0.568 0.577 0.135 0.249 0.224 0.266 0.201 0.195 0.169 0.207 0.446 0.355 0.369 0.350 0.370 0.215 0.293 0.264 0.445 0.518 0.245 0.175 0.309 0.302 0.207 0.289 0.240 0.211 0.135 0.115 0.307 0.273 0.318 0.242 0.667 0.285 0.314 0.324 0.254 0.340 0.126 0.192 0.231 0.161 0.385 0.252 0.190 0.280 0.246 0.197 0.240 0.166 0.140 0.274 0.096 0.470 0.389 0.167 0.263 0.435 0.454 0.438 0.551 0.329 0.344 0.524 0.552 0.152 0.189 0.577 0.346 0.216 0.312 0.169 0.174 0.180

Correlation and 95% CI

-p

-0.147 0.122 0.150 0.032 -0.161 -0.004 0.093 0.207 -0.018 -0.103 -0.059 -0.036 0.177 0.369 -0.279 -0.007 -0.087 -0.092 0.067 0.003 0.060 -0.009 0.073 0.118 0.101 0.061 0.164 0.002 0.019 -0.013 0.032 0.216 0.073 0.066 0.187 0.032 -0.070 0.149 0.007 -0.024 0.045 0.025 0.061 -0.100 -0.051 -0.000 0.322 0.019 0.028 0.039 -0.085 -0.132 0.033 -0.064 -0.138 -0.071 0.168 0.079 -0.017 0.078 0.079 0.027 0.078 0.001 0.037 -0.050 -0.126 -0.004 -0.112 -0.049 0.053 0.087 -0.064 -0.085 0.156 0.033 0.120 0.237 0.379 -0.013 0.050 0.256 0.003 0.022 0.104 0.070 -0.115 0.140

na

-0.047 0.210 0.290 0.160 0.090 0.180 0.149 0.330 0.095 0.200 0.080 0.145 0.390 0.480 -0.075 0.123 0.070 0.090 0.135 0.100 0.115 0.100 0.270 0.240 0.240 0.210 0.270 0.110 0.159 0.128 0.250 0.377 0.160 0.121 0.249 0.170 0.070 0.220 0.125 0.095 0.090 0.070 0.187 0.090 0.138 0.123 0.515 0.155 0.175 0.185 0.087 0.110 0.080 0.065 0.048 0.046 0.280 0.167 0.087 0.181 0.164 0.113 0.160 0.084 0.089 0.115 -0.015 0.248 0.148 0.060 0.160 0.270 0.210 0.190 0.370 0.185 0.235 0.390 0.470 0.070 0.120 0.430 0.180 0.120 0.210 0.120 0.030 0.160

Upper limit

re

Correlation

Alexander (1991) Baker (2008) Barnett (1995) Bell (2015) Bennett (1994) Berthelot (2014) Berzenski (2010) Bevan (2002) Brassard (2014) Caesar (1988) Call (2009) Capaldi (1998) Corvo (2006) Cui (2010) Dutra (2012) Edwards (2014) Faulkner (2014) Feiring (2009) Fergusson (2008) Gay(2013) Godbout (2017) Grasley(2002) Gratz (2009) a Gratz (2009) b Hamberger (1991) Hastings (1988) Herrenkohl (2004) a Herrenkohl (2004) b Hou (2016) a Hou (2016) b Hughes (2007) Jin (2007) Kendra (2012) Kwong (2003) Lackey (2003) Langhinrichsen-Rohling (1995) a Langhinrichsen-Rohling (1995) b Lavoie (2002) MacEwen (1988) a MacEwen (1988) b Mair (2012) a Mair (2012) b Maldonado (2015) Maneta (2012) a Maneta (2012) b McClure (2017) Murphy(1993) Murphy(2000) Novak(2015) a Novak(2015) b Palazzolo (2010) a Palazzolo (2010) b Reyes (2015) Riggs (1996) a Riggs (1996) b Riggs (2010) Rosen (2001) Rosen (2002) Schafer (2004) a Schafer (2004) b Schafer (2004) c Schafer (2004) d Schafer (2004) e Schafer (2004) f Seltzer (2014) Shaw(2009) a Shaw(2009) b Simonelli (2002) a Simonelli (2002) b Simons (1995) a Simons (1995) b Smyth (2017) Steel (2017) a Steel (2017) b Swogger (2012) Taft (2008) Trabold (2015) Wang (2008) a Wang (2008) b Wekerle (2001) a Wekerle (2001) b Wekerle (2001) c Wekerle (2001) d White (2001) a White (2001) b Yount (2018) Zurbriggen (2010)

Lower limit

lP

Study name

-1.00

-0.50

0.00

Favours A

Fig. 2. Meta-analysis of overall CM and IPV perpetration.

Meta Analysis

35

0.50 Favours B

1.00

Journal Pre-proof Funnel Plot of Standard Error by Fisher's Z 0.00

0.10

0.15

0.20 -1.0

-0.5

0.0

0.5

1.0

of

-1.5

Fisher's Z

lP

re

-p

ro

Fig. 3. Funnel plot for publication bias.

na

-2.0

Jo ur

Standard Error

0.05

36

1.5

2.0

lP

re

-p

ro

of

Journal Pre-proof

Jo ur

na

Fig.4. P-curve for meta-analysis of CM and IPV perpetration.

37

Journal Pre-proof Author(Year)

Publication

Marital

Gender

Method

IV measure

DV measure

r

Sample

status

status

Alexander (1991)

Y

unmarried

both

retrospectively

CTS

CTS

-0.047

380

Baker (2008)

Y

unmarried

both

retrospectively

self-designed

CTS

0.21

474

Barnett (1995)

Y

married

male

retrospectively

self-designed

CTS

0.29

180

Bell (2015)

Y

married

female

retrospectively

CTQ

CTS

0.16

232

Bennett (1994)

Y

married

male

retrospectively

self-designed

self-designed

0.09

63

Berthelot (2014)

Y

married

both

retrospectively

ETI–SR; CCMSA

CTS

0.18

114

Berzenski (2010)

Y

unmarried

both

retrospectively

CMIS

CTS

0.149

1,175

Bevan (2002)

Y

married

male

retrospectively

CCMS-A

ABI

0.33

222

Brassard (2014)

Y

married

male

retrospectively

self-designed

CTS

Caesar (1988)

Y

married

male

retrospectively

self-designed

CTS

Call (2009)

N

unmarried

male

retrospectively

self-designed

CTS

Capaldi (1998)

Y

unmarried

male

prospectively

CTS

CTS

e

Corvo (2006)

Y

married

male

retrospectively

CTS

Cui (2010)

Y

unmarried

both

retrospectively

Dutra (2012)

Y

both

female

retrospectively

Edwards (2014)

Y

unmarried

male

retrospectively

Faulkner (2014)

Y

unmarried

both

prospectively

Feiring (2009)

Y

unmarried

both

prospectively

Fergusson (2008)

Y

both

both

Gay (2013)

Y

unmarried

Godbout (2017)

Y

unmarried

Grasley (2002)

N

Gratz (2009) a

size

r P

o r p

0.095

302

0.2

44

0.08

202

0.145

119

CTS

0.39

74

self-designed

self-designed

0.48

213

self-designed

CTS

-0.075

89

CSVQ; ETISP-SF

CTS

0.123

228

CTQ

CADRI

0.07

158

A checklist

CIRQ

0.09

118

prospectively

self-designed

CTS

0.135

828

female

retrospectively

CTQ

CTS

0.10

409

both

prospectively

self-designed

CTS

0.115

1,252

unmarried

both

prospectively

CTQ

CADRI

0.10

325

Y

unmarried

male

retrospectively

self-designed

API

0.27

96

Gratz (2009) b

Y

unmarried

female

retrospectively

self-designed

API

0.24

245

Hamberger (1991)

Y

married

male

retrospectively

self-designed

CTS

0.24

191

Hastings (1988)

Y

both

male

retrospectively

self-designed

CTS

0.21

168

l a

o J

n r u

38

f o

Journal Pre-proof Herrenkohl (2004) a

Y

unmarried

both

prospectively

self-designed

self-designed

0.27

314

Herrenkohl (2004) b

Y

unmarried

both

prospectively

self-designed

self-designed

0.11

330

Hou (2016) a

Y

married

female

retrospectively

self-designed

CTS

0.159

194

Hou (2016) b

Y

married

male

retrospectively

self-designed

CTS

0.128

194

Hughes (2007)

Y

both

female

retrospectively

CTS

CTS

0.25

80

Jin (2007)

Y

married

male

retrospectively

CTQ

CTS

0.377

126

Kendra (2012)

Y

unmarried

female

retrospectively

CTS

CTS

0.16

496

Kwong (2003)

Y

both

both

retrospectively

self-designed

CTS

0.121

1,249

Lackey (2003)

Y

both

both

prospectively

self-designed

CTS

0.249

903

Langhinrichsen-Rohli

Y

married

male

retrospectively

self-designed

CTS

0.17

199

Y

married

female

retrospectively

self-designed

CTS

Lavoie (2002)

Y

unmarried

male

prospectively

self-designed

MacEwen (1988) a

Y

married

male

prospectively

MacEwen (1988) b

Y

married

female

prospectively

Mair (2012) a

Y

married

male

retrospectively

Mair (2012) b

Y

married

female

retrospectively

Maldonado (2015)

Y

unmarried

both

retrospectively

Maneta (2012) a

Y

both

male

retrospectively

Maneta (2012) b

Y

both

female

McClure (2017)

Y

unmarried

Murphy (1993)

Y

Murphy (2000)

Langhinrichsen-Rohli

o r p

e

ng (1995) a

0.07

199

self-designed

0.22

717

self-designed

CTS

0.125

275

self-designed

CTS

0.095

275

ACE

CTS

0.09

1,861

ng (1995) b

l a

n r u

CTS

0.07

1,861

CTQ

CTS

0.187

236

CTQ

CTS

0.09

109

retrospectively

CTQ

CTS

0.138

109

both

retrospectively

CTQ

CADRI

0.123

254

both

male

retrospectively

self-designed

CTS

0.515

72

Y

unmarried

both

retrospectively

CTS

CTS

0.155

207

Novak (2015) a

Y

both

male

retrospectively

self-designed

CTS

0.175

177

Novak (2015) b

Y

both

female

retrospectively

self-designed

CTS

0.185

177

Palazzolo (2010) a

Y

unmarried

female

retrospectively

self-designed

CTS

0.087

132

Palazzolo (2010) b

Y

unmarried

female

retrospectively

self-designed

CTS

0.11

68

o J

ACE

r P

39

f o

Journal Pre-proof Reyes (2015)

Y

unmarried

both

prospectively

self-designed

SDDVP

0.08

1,759

Riggs (1996) a

Y

unmarried

female

retrospectively

FVQ

CTS

0.065

232

Riggs (1996) b

Y

unmarried

male

retrospectively

FVQ

CTS

0.048

113

Riggs (2010)

Y

unmarried

both

retrospectively

CTQ

CTS

0.046

285

Rosen (2001)

Y

unmarried

both

retrospectively

CTS

CTS

0.28

277

Rosen (2002)

Y

married

male

retrospectively

CTQ

CTS

0.167

488

Schafer (2004) a

Y

both

female

retrospectively

M/FCHAB

CTS

0.087

354

Schafer (2004) b

Y

both

male

retrospectively

M/FCHAB

CTS

0.181

354

Schafer (2004) c

Y

both

female

retrospectively

M/FCHAB

CTS

0.164

521

Schafer (2004) d

Y

both

male

retrospectively

M/FCHAB

CTS

0.113

521

Schafer (2004) e

Y

both

female

retrospectively

M/FCHAB

CTS

Schafer (2004) f

Y

both

male

retrospectively

M/FCHAB

CTS

Seltzer (2014)

Y

married

both

retrospectively

self-designed

CTS

Shaw (2009) a

N

unmarried

female

retrospectively

EASE-PI

CTS

Shaw (2009) b

N

unmarried

female

retrospectively

EASE-PI

Simonelli (2002) a

Y

unmarried

male

retrospectively

SNFI

Simonelli (2002) b

Y

unmarried

female

retrospectively

Simons (1995) a

Y

married

female

retrospectively

Simons (1995) b

Y

married

male

Harsh

o J

Scale

retrospectively

Harsh

e

0.16

552

0.084

552

0.089

1,436

0.115

143

CTS

-0.015

313

CTS

0.248

61

CTS

0.148

59

Discipline

self-designed

0.06

327

Discipline

self-designed

0.16

333

l a

n r u SNFI

o r p

r P

Scale

Smyth (2017)

Y

both

both

retrospectively

CTQ

CTS

0.27

110

Steel (2017) a

Y

unmarried

male

retrospectively

CTQ

CTS

0.21

53

Steel (2017) b

Y

unmarried

female

retrospectively

CTQ

CTS

0.19

53

Swogger (2012)

Y

married

male

retrospectively

ACE

self-designed

0.37

75

Taft (2008)

Y

married

male

retrospectively

CTS

CTS

0.185

164

Trabold (2015)

Y

both

both

retrospectively

CTQ

CTS

0.235

274

Wang (2008) a

Y

both

male

retrospectively

CTS

CTS

0.39

136

40

f o

Journal Pre-proof Wang (2008) b

Y

both

male

retrospectively

CTS

CTS

0.47

314

Wekerle (2001) a

Y

unmarried

male

retrospectively

CTQ

CIRQ

0.07

558

Wekerle (2001) b

Y

unmarried

female

retrospectively

CTQ

CIRQ

0.12

771

Wekerle (2001) c

Y

unmarried

male

retrospectively

CTQ

CIRQ

0.43

101

Wekerle (2001) d

Y

unmarried

female

retrospectively

CTQ

CIRQ

0.18

123

White (2001) a

Y

both

female

prospectively

self-designed

CTS

0.12

400

White (2001) b

Y

both

male

prospectively

self-designed

CTS

0.21

325

Yount (2018)

Y

married

male

retrospectively

CTQ

CTS

0.12

1,508

Zurbriggen (2010)

Y

unmarried

both

retrospectively

BBTS

SES

0.03

f o

o r p 184

e

Note. CMIS = The Childhood Maltreatment Interview Schedule; ETI-SR = Early Trauma Inventory, Self-Report version; CCMSA = Comprehensive Child Maltreatment Scale for Adults; CIRQ = Conflict in Relationships Questionnaire; API = The Abuse-Perpetration Inventory; CADRI = Conflict in Adolescent Dating Relationships Inventory; SNFI = Scale of Negative Family

r P

Interactions; FVQ = Family Violence Questionnaire; BBTS = Brief Betrayal Trauma Survey; SES = Sexual Experiences Survey; SDDVP = Safe Dates Dating Violence Perpetration scale; ACE = Adverse Childhood Experiences; EASE-PI = Exposure to Abusive and Supportive Environments Parenting Inventory; CSVQ = The Childhood Sexual Victimization Questionnaire; ETISP-SF

l a

= The Early Trauma Inventory Self Report-Short Form; ABI = The Abusive Behavior Inventory; M/FCHAB = Straus’s scale.

n r u

o J

41

Journal Pre-proof

Table 2. Effect sizes for the relationship between CM and IPV perpetration. Variable

k

Overall CM and IPV perpetration

a

87

Physical abuse

50

Psychological abuse

16

Sexual abuse Note.

14

95% CI

.16

***

[.14, .18]

.17

***

[.14, .20]

.13

***

[.07, .19]

.13

***

[.07, .18]

P < .001

For overall effect size, each study was the unit analysis, but subgroups were not independent.

Table 3.

Variable

k

Mean r

Male

37

.20***

Female

26

.11***

25 39

Method Retrospectively

CTS Self-designed Others IPV measures CTS Self-designed Others

15.73

.000

.16

.692

.63

.426

.18

.671

1.91

.385

[.09, .14]

[.12, .18] [.11, .17]

.17***

[.14, .19]

***

[.11, .18]

20

.15***

[.11, .19]

10

***

[.13, .35]

35

.17***

[.14, .20]

22

.12

***

[.09, .15]

66

.15***

[.13, .18]

.22

***

[.13, .31]

.15

***

[.10, .20]

72 15

na

CTQ

[.16, .24]

***

.14

Jo ur

CM measures

p

.15***

lP

Unmarried

Prospectively

re

Marital status Married

Qb

-p

Gender

95% CI

ro

Moderation analyses of the effect sizes.

of

a

***

Mean r

8

13

.15

.24

Note. k = number of effect sizes; r = point estimate of the effect size; CI = confidence interval; Qb = heterogeneity of between-group differences with k-1 degrees of freedom. ***

p < .001

42

Journal Pre-proof Highlights There is a significant positive relationship between total CM and IPV perpetration. All three types of CM (childhood physical abuse, psychological abuse, and sexual abuse) are positively related to IPV perpetration.

Jo ur

na

lP

re

-p

ro

of

The association between CM and IPV perpetration is stronger for males than for females.

43

Figure 1

Figure 2

Figure 3

Figure 4