A Meta-Analytic Review of Social Learning Theory and Teen Dating Violence Perpetration

Abstract

Objectives

The purpose of this study is to examine social learning theory (SLT) and teen dating violence (TDV) perpetration. This study aims to determine which predictors have the largest effect sizes, whether they vary for males and females, and whether they differ based on research design and sample characteristics.

Methods

This study uses hierarchal meta-analytic methods to examine both within- and between-dataset differences in relationships between a variety of SLT predictors and TDV outcomes. Both bivariate and multivariate effect sizes are computed for a sample of n = 1,157 effect sizes nested in n = 116 studies that used n = 88 unique datasets.

Results

Findings indicate that a variety of SLT predictors can explain TDV perpetration. Predictors with the largest effect sizes include anticipated benefits of TDV (Bivariate: r = .254; multivariate: r = .308) and peer TDV perpetration (Bivariate: r = .279; multivariate: r = .205). While most predictors show similar effect sizes for males and females, peer TDV perpetration appears to be a stronger influence for males. Several significant moderators are found.

Conclusions

SLT should continue to provide a theoretical framework for TDV research and practice. Future research should examine same-sex teen relationships and sexual minorities.

Keywords

Juvenile delinquency gender cognitive theories criminological Theory quantitative research research Methods

Teen dating violence (TDV), which is defined as physical, sexual, psychological, and cyber violence that occurs in adolescent dating relationships (Centers for Disease Control and Prevention 2019), is a pervasive problem in adolescence. While most violence in adolescence is perpetrated by males, TDV differs, as males and females have similar rates of involvement (Puzzanchera 2020). Estimates of the extent of TDV vary, as there is no standard method of measurement. Nationally representative data consistently report about 8% - 12% of high school students being involved in TDV (Centers for Disease Control and Prevention 2019; Puzzanchera 2020) but estimates of individual studies range from 1% to 61% (Wincentak et al. 2017). However, sex differences in TDV perpetration do exist, with males more likely to perpetrate serious physical and sexual forms of violence and females more likely to perpetrate psychological and minor physical forms of violence (Wincentak et al. 2017). TDV is often bi-directional, with participants serving as both perpetrator and victim (Giordano et al. 2010).

Adults may perceive teenage relationships to be fleeting and insignificant, but dating is considered an important part of social life for many teens. Both positive and negative behaviors learned during these formative relationships have been found to be influential in adult relationships (Collins 2003). One of the behaviors that may be carried into adulthood is using violence towards partners during conflict. While adolescents generally “age out” of other types of violent behavior, perpetrating violence against romantic partners in adolescence is likely to be continued in adult relationships (Leadbeater et al. 2014). Since TDV perpetration is often experienced alongside victimization, negative outcomes might include physical and mental health problems, poor academic achievement, and suicide ideation (Exner-Cortens, Eckenrode, and Rothman 2013).

TDV is usually explained using risk factors in a variety of ecological domains, including the individual, family, school, and community (Garthe, Sullivan, and McDaniel 2017; Park and Kim 2018; Spencer et al. 2021). General theories of crime have also been found to be useful in explaining TDV perpetration. While some studies have found that these risk factors and theories are predictive of TDV for both males and females, sex differences have also been found (Reyes et al. 2019). Social learning theory (Akers 1998) is commonly used to explain intimate partner violence in adulthood (Cochran et al. 2016) and has increasingly been used to explain dating violence in adolescence as well. While each of the four dimensions of differential association, differential reinforcement, imitation, and definitions have been found to explain TDV (Foshee, Bauman, and Fletcher Linder 1999), they have had varying success depending on which dimension is used and whether others are controlled for in the same model. Therefore, comparative research is needed to determine whether each dimension is equally important in explaining TDV or whether one has a greater influence than the others. Additionally, some studies have found sex differences in the relationship between social learning theory and TDV perpetration, which is imperative to explore since this issue affects male and female adolescents equally. Because prior research is mixed, a meta-analysis that “takes stock” of the prior research is needed. This study uses hierarchal meta-analytic methods to explore the four dimensions of social learning theory, sex differences, and TDV perpetration.

Literature Review

Theoretical Framework

The main premise of Akers’ (1973, 1998) social learning theory is that criminal behavior is learned through social interactions. Building on Sutherland’s (1939) differential association perspective, Akers suggested four dimensions of social learning that could help explain crime. Differential association asserts that attitudes and methods for criminal behavior are learned through social interactions with primary (peers, family) and secondary (school, church, other activities) sources. Whether or not individuals adapt deviant behaviors depends on the frequency and duration of exposure, as well as how close the individual is to the one committing the behavior. Definitions are the individuals’ personal perceptions on whether the behavior is morally acceptable. If definitions are approved of or if they provide justification for deviance, these behaviors are more likely to occur. Differential reinforcement asserts that criminal behavior is more likely to continue when it is rewarded, either by tangible rewards or avoidance of discomfort, rather than punishment. Imitation simply states that criminal behaviors are learned by modeling. If an individual is close with and respects the one modeling the behavior, it is more likely to occur.

SLT is one of the most tested criminological theories and is empirically supported for a variety of offending behaviors, including substance use, property crime, and violence, in both adolescent and adult samples (Akers and Jensen 2006). However, tests of social learning theory tend to examine one dimension of the theory instead of all four (Cochran et al. 2017). When a relationship is found between one of the four dimensions and offending, it is inferred that social learning theory can explain offending, even though the full theory was not tested. Additionally, these dimensions have had differing success in explaining offending, with differential association having the most empirical support and imitation having the least (Pratt et al., 2010). Previous meta-analyses have found that findings depend on crime type, demographics of sample, location, and research design (Kruis et al. 2020; Pratt et al. 2010).

Empirical Status of SLT on TDV

SLT provides a framework in which to study risk factors for TDV perpetration. Simply put, adolescents who are exposed to relationship violence in family or peer settings will learn how to perpetrate these acts themselves (Sellers, Cochran, and Branch 2005). The four dimensions of SLT have been applied to TDV with varying success. Differential association has been operationalized by asking teens about their peers’ TDV behaviors as well as their peers’ general violent behaviors. Prior findings are mixed, and some studies have found that those with peers that engage in TDV and violence against same-sex peers (Ali, Swahn, and Hamburger 2011; Foshee et al. 2013) were more likely to perpetrate TDV themselves. It is important to note that these results are plagued with potential measurement issues, as adolescents that are asked about their friends’ behaviors may project their own behaviors onto their friends (Bauman and Ennett 1996). However, this may not be an issue, since scholars have noted that it is perceptions of peer behavior, not actual peer behavior, that helps explain dating violence (Shorey et al. 2018).

Differential reinforcement has been measured using perceived costs and benefits of committing TDV, as well as expected parental and societal reactions of TDV perpetration. If someone is rewarded for perpetrating TDV (positive reinforcement) or avoids consequences (negative reinforcement) the act is more likely. These rewards are usually in the form of positive reactions from peers, parents, or society, and consequences include disapproval from ones’ social group, damaged reputation, criminal justice system involvement, and conflict within the relationship (Akers 1998). Prior research has found that those who perceive that they will benefit are more likely to perpetrate TDV (Archer, Fernandez Fuertes, and Thanzami 2010), and these effects are stronger than the potential for negative consequences (Fernandez-Fuertes et al. 2019). Parents are powerful socialization agents during the teenage years, and verbal support for aggressive solutions to problems, as well as modeling violent solutions of their own, have been found to influence adolescent violence (Akers and Jennings 2009). Perceptions of acceptance have been found to be more influential than the actual beliefs of the parent, and adolescents who perceive that their parents would support them if they perpetrated violence against a partner during a conflict are more likely to perpetrate partner violence in adolescence (Miller et al. 2009), but not young adulthood (Kim, Macy, and Wretman 2021). Some research has found that parental beliefs were not predictive of TDV perpetration over time, and other parental learning mechanisms, such as modeling, may be more influential (Garthe, Sullivan, and Farrell 2018).

Definitions of TDV are often measured as the beliefs surrounding gendered violence, including violence being an acceptable solution for problems in a relationship, patriarchal gender role beliefs, and sexist attitudes (Reyes et al. 2016). Acceptability of male-perpetrated violence has been found to be a stronger predictor of TDV than female-perpetrated violence, since violence perpetrated by women tends to be more socially accepted and has less serious consequences (Molidor and Tolman 1998). While males perpetrating violence against female romantic partners is stigmatized, females committing acts of violence such as slapping or hitting are romanticized in the media (O’Keefe 1997). Acceptance of violence by males against females, but not females against males, explained cyber dating violence perpetration in a sample of pre-teens (Peskin et al. 2017). However, other research has found that attitudes about gendered violence were not predictive of TDV perpetration once other explanations, such as acceptance of general aggression, childhood adversity, and negative affect were controlled for (Daff, McEwan, and Luebbers 2020; Kidman and Kohler 2020).

Family risk factors have been examined extensively in research on TDV, and recent meta-analyses found that witnessing family violence was a risk factor for TDV perpetration (Park and Kim 2018; Spencer et al. 2021). The imitation dimension of Akers’ SLT (1973, 1998) is consistent with intergenerational transmission of violence in the family. If an adolescent witnesses someone close to them engaging in a behavior, they may start to model that behavior. Simply put, children who grow up around relationships characterized by violence may think that abuse is a normal feature of romantic relationships. The effect of imitation on behavior may depend on whether the father or mother is imitated. Since mothers are usually the primary caregiver, her behaviors have been found to have a greater influence than those of fathers (Doherty and Feeney 2004; Temple et al. 2013b). Similarly, violence may be modeled after parent-child relationships as well, with those being victims of child abuse themselves more likely to perpetrate TDV (Beckmann 2020; Spencer et al. 2021). Along with family violence, witnessing violence in the community has also been found to be a risk factor for TDV perpetration (Black et al. 2015).

SLT, TDV, and Sex Differences

Akers (1999) noted that girls and boys are socialized differently, and social structure and social learning can explain the higher crime rates for males. He posited that girls are socialized to be more conforming, whereas males are socialized to be more individualistic, with associations with deviant peer groups being more accepted. Although Akers (1999) called for future research to examine sex, social structure, and social learning, he was criticized by feminist scholars for not explicitly outlining the how social learning mechanisms may work different for males and females (Morash 1999). TDV researchers who used SLT as a framework have examined sex differences in the four dimensions. While sex differences have been found for each, some empirical tests have found that social learning mechanisms explained offending similarly for males and females (Foshee, Bauman, and Fletcher Linder 1999).

Since males are more likely to have deviant peer groups than females (Chapple, Vaske, and Worthen 2014), differential association suggests that males should be more likely to perpetrate TDV. However, most estimates have found that females perpetrate TDV at equal or higher rates relative to males (Wincentak et al. 2017). In one study, having pro-social friends reduced TDV perpetration for females but did not have a protective effect for males (Foshee et al. 2013). In another, perceiving that peers engaged in TDV increased involvement for males, but not females (Shorey et al. 2018). However, Pusch and Reisig (2021) found that peer involvement in general delinquency had a significant effect on TDV perpetration for females but not males. Not all research examining peer involvement and TDV have shown sex differences, with some finding similar effects for males and females (Miller et al. 2009).

Along with differential association, differential reinforcement mechanisms have shown sex differences. Female-to-male TDV perpetration is seen as more accepted by adolescents than male-to-female TDV (Reeves and Orpinas 2012), and thus the social costs may be greater for males than for females. Additionally, females have been found to receive greater benefits for perpetrating TDV (Archer, Fernández-Fuentes, and Thanzami 2010). For example, prior research has found that females received benefits in the form of an improved relationship for perpetrating sexual TDV while males did not (Pöllänen et al. 2021). However, not all examinations of differential reinforcement have found gender differences, with benefits being equally predictive of TDV for both males and females (Archer et al. 2010).

Although beliefs that violence is acceptable has been found to explain TDV perpetration for males and females, sex differences have been detected. For example, while beliefs that general violence is acceptable predicted TDV for males, only acceptance of relationship violence (Daff et al. 2020), and more specifically, female-perpetrated relationship violence (De La Rue et al. 2017) predicted TDV for females. Beliefs surrounding gender norms and equity have been found to influence male TDV perpetration. Males who had sexist attitudes and adhered to traditional or patriarchal gender norms were more likely to perpetrate TDV (Cava et al. 2020; Rostad et al. 2019), especially sexual forms of violence (Shen, Chiu, and Gao 2012). Generally, these beliefs were important for explaining TDV victimization for females, but had no relationship with perpetration (Boyce, Deardorff, and Minnis 2020). However, not all tests of definitions that are favorable towards violence have found sex differences, with attitudes accepting of female, but not male, violence predictive of physical TDV perpetration regardless of sex (Niolon et al. 2015).

Finally, empirical tests of the imitation proposition of SLT have been found to have sex differences as well, but again, findings are mixed. For example, Pusch and Reisig (2021) found that witnessing family violence was predictive of TDV perpetration for males only. This may be due to females being more sensitive to the harms caused by interparental violence, whereas males may see it being instrumental to the aggressor (Kinsfogel and Grych 2004). This may differ by type of TDV, as witnessing parental conflict has been found to be more influential for psychological TDV perpetration for males and physical TDV perpetration for females (Fosco et al. 2016). However, other research has found that witnessing parental violence only predicted TDV perpetration for girls (Jouriles et al. 2012; Ruel et al. 2020).

Current Focus

Despite the large volume of research that has examined SLT and TDV perpetration, there are still unanswered questions. Most research has examined one of the four dimensions of SLT rather than comparing them to see if they are equally important in predicting TDV. Additionally, it is unclear if there are sex differences in the relationships between each of these dimensions and TDV perpetration. Since the consequences of TDV are numerous and can be lasting, it is imperative that this phenomenon is more fully understood. Meta-analyses are useful when there are inconsistent findings of a relationship between two variables, as an average effect size can be computed. Additionally, they can aid in theoretical development. Most criminological theories do not have standardized propositions to be tested, and scholars have conceptualized and operationalized theoretical components in a variety of ways (Pratt and Cullen, 2000; Pratt, 2010). Meta-analyses allow scholars to examine these differences together. For example, meta-analyses undertaken on the relationships between ADHD and deviance, as well as maternal cigarette use and deviance (Pratt et al., 2002, 2006) sparked additional theoretical debates and inspired research that moved the field forward.

While scholars have completed several meta-analyses on TDV (Garthe et al. 2017; Park and Kim 2018; Spencer et al. 2021; Wincentak et al. 2017), they have limitations which the current study aims to address. First, prior meta-analyses on TDV do not use criminological theory, but rather, examine risk factors in different domains, such as individual, community, and societal. While it is useful to specify which risk factors may be linked with TDV perpetration, it does not necessarily tell us why associations with family and peers may increase risk. Social learning theory can fill this gap and explain the processes linking risk factors and TDV perpetration. Second, prior meta-analyses do not include separate effect sizes for males and females. Since males and females are equally at risk for TDV perpetration and there is no agreement as to whether SLT processes are sex-neutral, this is an important area to explore. Prior meta-analyses examining adolescent offending and victimization have found sex differences in predictors (Pusch and Holtfreter 2021; Scott and Brown 2018) but did not specifically examine TDV perpetration. In order to fill this gap in the literature, this study will calculate separate effect sizes for males and females in addition to overall effect sizes. Third, this study improves upon the standard meta-analytic techniques by using a hierarchal design that examines mean effect sizes both within- and between- datasets. Using a traditional meta-analytic design can mask important moderating differences, while the hierarchal design can determine if effect sizes differ according to characteristics of the sample and research design. Ultimately, this study aims to answer three related questions: 1. Which variables related to each SLT domain have the largest effect size when predicting TDV perpetration? 2. Are there sex differences in SLT predictors? 3. Do sample characteristics and research design moderate these relationships?

Methods

Sample of Studies

The search for relevant studies occurred between March 2021 and May 2021 and involved two steps. First, the following electronic databases were searched: Academic Search Premier, Criminal Justice Abstracts, Family Studies Abstracts, National Criminal Justice Reference System, Proquest Theses and Dissertations Global, PsychArticles, PsychInfo, SocINDEX, Sociologial Abstracts, and Women's Studies International. Searches were restricted to abstracts and used the following Boolean search terms: “teen OR juvenile OR adolescent OR youth” AND “dating OR intimate OR partner” AND “violence OR abuse OR assault” AND “differential + association OR “deviant + peers OR delinquent + peers OR differential + reinforcement OR reward* OR cost* OR benefit* OR imitation OR witness OR intergenerational OR definition* OR attitude* OR belief* OR value* OR norm* OR social + learning.” Search materials were limited to peer-reviewed articles, technical reports, theses, dissertations, and conference papers, although the final sample only included peer-reviewed journal articles, theses, and dissertations. Next, the reference lists of previous meta-analyses that examined TDV (Garthe et al. 2017; Johnson et al. 2017; Hébert et al. 2019; Park and Kim 2018; Spencer et al. 2021; Wincentak et al. 2017; Zych et al. 2021) were examined to see if any relevant studies were missed in the first search. In total, 1,516 unique items were identified from the first strategy, and an additional 24 were found during the search of reference pages. From these, 1,223 were eliminated when it became clear by the title and/or abstract that it did not fit the criteria for inclusion. 317 full articles were accessed. From these, 81 were eliminated because the study did not include an independent variable consistent with social learning theory. 36 were eliminated because the dependent variable measured victimization, a mixed measure of victimization and perpetration, or a program evaluation, rather than TDV perpetration. 25 were eliminated because the study used an adult or mixed youth and adult sample. 59 were eliminated because the study used a methodology inconsistent with inclusion, including latent class analysis and group-based trajectory modeling, or did not contain enough information to standardize beta coefficients. In total, 116 studies were retained for coding which used 88 unique datasets. A flowchart of the search is depicted in Figure 1. A list of included studies is included in Table 1.

Figure 1.

Flowchart of Literature Search

Table 1.

Details of Included Studies

Authors	Year	Country	Male n	Female n	Mean Age	TDV Type	Pub.
Ali et al.	2011	USA	1,383	1,505	Grades 7–12	Physical	Yes
Antonio et al.	2012	Brazil	32	11	15.4	Physical	Yes
Archer et al.⁷	2010	Spain	251	350	17.1	Mixed	Yes
Arriaga & Foshee¹²	2004	USA	246	280	Grades 8–9	Mixed	Yes
Beckmann	2020	Germany	4,351	-	Grade 9	Physical Psych	Yes
Black et al.	2015	USA	69	106	14.1	Physical	Yes
Boivin et al.	2012	Canada	508	751	16.2	Physical	Yes
Boyce et al.	2020	USA	149	147	13.8	Mixed	Yes
Brendgen et al.	2002	Canada	336	-	16.5	Physical	Yes
Calvete et al.⁸	2018	Spain	409	452	13.8	Physical	Yes
Cava et al.	2020	Spain	229	263	15.2	Cyber	Yes
Chapple	2003	USA	432	450	Grades 9–11	Physical	Yes
Clarey et al.¹¹	2010	Mexico	75	129	Range 15–18	Mixed	Yes
Clarke	2018	USA	69	101	Range 12–18	Mixed Psych	No
Courtain & Glowacz	2021	Belgium	312	702	18.9	Physical Psych Sexual	Yes
Daff et al.	2020	Australia	147	123	15.9	Mixed	Yes
De Puy et al.	2014	Switzerland	71	52	17.8	Physical Psych	Yes
Ellis et al.	2013	Canada	241	348	15.1	Mixed	Yes
Enosh	2007	Israel	167	179	16.5	Sexual	Yes
Espinoza et al.¹¹	2012	Mexico	75	129	15.5	Physical Psych	Yes
Fawson¹³	2012	USA	413	424	15.7	Mixed	No
Fawson¹³	2015	USA	248	238	15.7	Mixed	Yes
Fawson et al.	2018	USA	-	727	15.7	Physical Psych Sexual	Yes
Fernandez-Fuentes et al.⁷	2019	Spain	248	345	17	Physical Psych Sexual	Yes
Fernandez-Gonzalez et al.⁸	2019	Spain	402	451	14.7	Mixed	Yes
Flisher et al.⁶	2007	South Africa	236	360	15.4	Physical	Yes
Fosco et al.³	2015	USA	93	143	16.4	Physical Psych	Yes
Foshee et al.¹²	1999	USA	704	710	13.6	Physical	Yes
Foshee et al.¹²	2001	USA	576	610	Grades 8–9	Mixed	Yes
Foshee et al.¹²	2005	USA	529	689	13.8	Physical	Yes
Foshee et al.¹	2010	USA	851	815	Grades 6–8	Physical	Yes
Foshee et al.¹	2011	USA	1,369	1,538	Grades 6–8	Physical	Yes
Foshee et al.¹	2013	USA	1,706	1,706	14.2	Physical	Yes
Foshee et al.¹	2014	USA	613	514	Grades 6–8	Physical	Yes
Foshee et al.¹	2015	USA	-	2,198	Grades 6–8	Physical	Yes
Foshee et al.⁹	2016	USA	144	255	13.6	Physical	Yes
Friedlander et al.	2013	Canada	246	238	15.1	Physical	Yes
Garrido & Taussig	2013	USA	14	27	13.6	Mixed	Yes
Garthe et al.	2014	USA	671	728	Grades 6–8	Mixed	No
Gilbert et al.	2020	Nigeria	1,188	1,131	19.9	Mixed	Yes
Giordano et al.⁴	2015	USA	467	488	Grades 7–11	Physical	Yes
Gomez	2010	Brazil	247	254	19.2	Physical	No
Grasley	2002	Canada	204	246	15.4	Mixed	No
Grych & Kinsfogel³	2010	USA	188	203	15.6	Physical Psych	Yes
Hall	2001	USA	147	83	15.7	Psych	No
Hare et al.	2009	USA	27	48	18.3	Physical	Yes
Hawkins	2001	Canada	82	150	15.3	Physical Psych	No
Hollins	2008	USA	257	-	16.3	Mixed	No
Hopper	2011	USA	89	97	17.6	Physical Psych	No
Hunter	2009	USA	110	62	16.3	Mixed	No
Josephson & Proulx	2008	Canada	143	147	13.4	Physical	Yes
Jouriles et al.⁵	2011	USA	47	48	15.8	Mixed	Yes
Jouriles et al.⁵	2012	USA	43	45	15.9	Physical	Yes
Kaufman⁴	2014	USA	443	507	Grades 7–11	Physical	No
Kidman & Kohler	2020	Malawi	1,045	1,044	14	Mixed	Yes
Kim et al.	2009	USA	89	80	16	Mixed Physical Psych Sexual	Yes
Kinsfogel³	2001	USA	186	205	15.7	Physical Psych	No
Kinsfogel & Grych³	2004	USA	186	205	15.7	Physical	Yes
						Psych
Krauss et al.⁵	2020	USA	77	70	15.9	Physical Psych Sexual	Yes
Latzman et al.	2015	USA	214	203	Grades 6–7	Psych	Yes
Lavoie et al.	2002	Canada	717	-	16.5	Physical	Yes
Lichter & McCloskey	2004	USA	107	105	16.7	Physical	Yes
Linder	2002	USA	64	75	19	Psych	No
Malik et al.	1997	USA	281	426	Range 14–17	Physical	Yes
McCauley et al.	2013	USA	1,699	-	Grades 9–12	Mixed Psych	Yes
McDonnell et al.	2010	USA	122	229	14.2	Mixed	Yes
Miller et al.²	2009	USA	1,694	1,130	Grade 6	Physical	Yes
Miller et al.	2020	USA	866	-	Range 13–19	Mixed	Yes
Mitchell	1994	USA	85	102	Grades 9–12	Physical Psych	No
Moretti et al.	2006	Canada	439	63	15.4	Physical	Yes
Morris et al.	2015	USA	226	235	18	Mixed	Yes
Mueller et al.	2013	USA	38	44	15.8	Mixed	Yes
Nardi-Rodriguez et al.	2019	Spain	212	267	15.5	Psych	Yes
Niolon et al.	2015	USA	772	848	Grades 6–8	Mixed	Yes
O’Keefe	1997	USA	385	554	16.9	Physical	Yes
Peitzmeier et al.	2020	Global	1,007	-	Range 15–19	Mixed	Yes
Peskin et al.	2017	USA	235	186	Grade 6	Cyber	Yes
Pflieger & Vazsonyi	2006	USA	404	403	16.4	Mixed	Yes
Pollanen et al.	2021	South Africa	931	1,268	13.7	Sexual	Yes
Price	2002	Canada	138	-	16.3	Psych	No
Price et al.¹³	1999	Canada	444	379	14.5	Physical Psych Sexual	Yes
Pusch & Reisig	2021	USA	2,502	2,687	16.4	Physical	Yes
Rankin-Williams et al.	2020	USA	131	148	16.1	Physical Sexual	Yes
Reed et al.	2021	USA	321	382	16.4	Cyber	Yes
Reyes & Foshee¹²	2013	USA	459	-	Grades 8–9	Physical Sexual	Yes
Reyes et al.¹²	2015	USA	976	989	Grades 8–9	Physical	Yes
Reyes et al.¹²	2016	USA	577	-	13.9	Physical	Yes
Reyes et al.⁹	2017	USA	89	121	13.9	Mixed Psych	Yes
Richards & Branch⁴	2012	USA	475	495	15.5	Physical	Yes
Richards et al.⁴	2014	USA	-	346	15.7	Physical Psych	Yes
Rivera	2008	USA	63	73	Grades 10–12	Mixed	No
Rivera-Rivera et al.	2007	Mexico	3,373	4,587	Range 11–24	Mixed Physical Psych	Yes
Rostad et al.	2019	USA	770	924	15.4	Physical Psych Sexual	Yes
Rothman et al.	2011	USA	202	254	19.1	Mixed	Yes
Ruel et al.	2020	Canada	928	1,636	15.3	Physical	Yes
Russell et al.⁶	2014	South Africa	238	-	Grade 8	Physical Sexual	Yes
Schwartz	2003	Canada	47	43	16.7	Physical Psych Sexual	No
Schwartz et al.	1997	USA	122	106	16.8	Physical	Yes
Sears et al.¹³	2007	Canada	324	309	14.6	Physical Psych Sexual	Yes
Shamu et al.	2016	South Africa	1,616	-	Grade 8	Mixed	Yes
Shen et al.	2012	China	478	498	15.9	Mixed Physical Psych Sexual	Yes
Sherer	2009	Israel	591	766	16.2	Mixed	Yes
Shorey et al.¹⁰	2018	USA	458	584	18	Physical	Yes
Shorey et al.¹⁰	2019	USA	459	583	15.1	Physical Psych	Yes
Simon et al.²	2010	USA	1,575	1,235	Grade 6	Physical	Yes
Simons et al.	1998	USA	113	-	13.5	Physical	Yes
Stepheau-Watson	2003	USA	196	280	13.1	Physical	No
Stults et al.	2015	USA	520	-	18.5	Mixed	Yes
Taylor	2018	USA	121	87	17.9	Cyber Physical Psych Sexual	No
Temple et al.¹⁰	2013	USA	403	514	15.1	Physical Psych	Yes
Temple et al.¹⁰	2013	USA	459	583	15.1	Physical	Yes
Van Ouytsel et al.	2018	Belgium	135	331	18	Cyber	Yes
Ward et al.	2006	USA	142	169	15	Physical	Yes
Wesche et al.	2019	USA	152	129	17.4	Mixed	Yes
Williams et al.	2008	Canada	348	273	15.4	Physical	Yes
Ybarra & Rohling	2019	USA	462	426	16.7	Physical Psych Sexual	Yes

Note: Pub = published study, Mixed = TDV that combines more than 1 outcome.

Datasets used in more than 1 study: ¹North Carolina Chapel Hill, ²MVPP, ³Upper Midwest Urban High Schools, ⁴TARS, ⁵Teens & Friends, ⁶SACENDU, ⁷Salamanca High Schools, ⁸Bizkaia High School, ⁹North Carolina Families, ¹⁰Dating it Safe, ¹¹Monterrey High Schools, ¹²Safe Dates, ¹³Canadian High Schools.

Criteria for Inclusion

Prior to the literature search, several criteria of inclusion were developed based on best meta-analytic practices. Because a goal of meta-analyses is to examine the full body of literature on a topic, the criteria were kept as broad as possible, with attempts made to include rather than exclude studies (Glass 2015; Turanovic and Pratt 2021). First, the study had to use quantitative methods and examine the relationship between a social learning predictor variable and dating violence perpetration. Both bivariate (correlation coefficient) and multivariate (regression coefficient) effect sizes were coded. However, it is problematic to include both bivariate and multivariate effect sizes in the same meta-analysis, as bivariate effect sizes do not include covariates and are often inflated compared to multivariate statistics (Turanovic and Pratt 2021). To guard against this, bivariate and multivariate effect sizes were analyzed separately, and covariates were included as moderating analyses. Next, the sample had to be composed of adolescents. There is a disagreement on what age range TDV encompasses, with some previous meta-analyses using the more conservative range of 13–18 (Wincentak et al. 2017), some including those as young as 10 (Garthe et al. 2017) and some including those as old as 21 (Zych et al. 2021). To remain as inclusive as possible while still retaining the focus on teenage relationships, studies were included that used a sample with a mean age that was greater than 13 and less than 20. However, college-only samples were excluded, as contextual factors that may explain college relationship violence may differ from those in middle and high school (Kaukinen 2014). Next, the dependent variable had to be a form of dating violence perpetration. Studies that only examined victimization or a mixed measure of perpetration and victimization were eliminated. The third criteria was that the study had to contain an independent variable consistent with one of the four dimensions of social learning theory. There were no limits on time period or geographical area, and studies from 1994 to 2021 were included. 84% of the included studies were published, while 16% were not.

Predictor Domains

Independent variables consistent with SLT were separated into four predictor domains.

Imitation

The imitation domain includes several variables consistent with witnessing and experiencing violence in the home and in the community. Witnessing interpersonal violence includes intimate partner violence in the home that does not differentiate between violence perpetrated by the father and the mother. Some studies did differentiate between the two, and these were coded as witnessing interpersonal violence perpetration by father and witnessing interpersonal violence perpetration by mother. Experienced family violence includes physical and/or sexual violence inflicted on the respondent by someone in the immediate family. Exposure to family violence includes effect sizes for studies that did not differentiate between witnessing and experiencing violence in the home. Exposure to community violence includes witnessing both IPV and other types of violence in the community and/or school.

Differential association

Effect sizes in this domain focus on the social network. Peer TDV perpetration includes effect sizes for associations with peers that perpetrate TDV. Peer delinquency includes effect sizes for associations with peers that commit general acts of delinquency.

Differential reinforcement

The predictor variable social acceptability of TDV captures perceptions of peer, family, and/or societal acceptance of TDV perpetration. An insufficient number of effect sizes were available to create separate predictor variables for each, so they were aggregated. Anticipated rewards for TDV captures beliefs that TDV perpetration will result in positive benefits. Several studies included effect sizes for negative costs associated with perpetrating TDV. These were reverse-coded and included.

Definitions

This domain includes predictor variables consistent with attitudes and beliefs surrounding TDV. Acceptance of TDV captures beliefs that violence is an acceptable method of dealing with conflict in relationships. Some studies contain separate measures for male and female violence, thus acceptance of male TDV and acceptance of female TDV are also included. Acceptance of violence captures beliefs that violence is an acceptable way of dealing with conflicts in general. Gender role beliefs captures acceptance of gender inequality, such as traditional and patriarchal beliefs as well as sexist attitudes.

Moderating Variables

Each study was coded for several moderating variables. These include sample characteristics, research design characteristics, and covariates that are commonly used to explain TDV. Studies were coded for whether they used a general sample (the majority used school samples) or a high-risk sample (justice-involved, homeless, or maltreated youth). Study location was coded and indicates whether the study occurred in North America (USA and Canada) or Non-North America. TDV outcomes were separated into several types. Physical TDV includes non-sexual violence including hitting, punching, and kicking. Psychological TDV includes non-physical acts such as emotional and verbal abuse, stalking, manipulation, and controlling behaviors. Sexual TDV includes sexual contact without consent. Cyber TDV includes abuse using the internet, such as put downs on social media, controlling online behaviors, and using smartphones to keep track of locations. A measure of mixed TDV was included for studies that combined several types of TDV. Studies were coded for whether they used two popular scales to measure TDV: any version of the Conflict Tactics Scale (CTS) and the Conflict in Adolescent Dating Relationships Inventory (CADRI). Those that did not use one of these two scales were coded as “other.”

Studies were also coded for differences in research design, including whether they were published in a peer-reviewed academic journal or unpublished, whether they used a cross-sectional or longitudinal design, whether the dependent variable was measured as a dichotomous, a count, or a continuous variable, and whether the length of time between the independent and dependent variable was less than a year or a year or more. Multivariate effect sizes vary widely depending on what controls are included in the model. Multivariate studies were coded for whether they include more than one variable consistent with SLT and for whether they controlled for each of the following: self-control, alcohol use, general offending, TDV victimization, family socio-economic status, parental supervision, parental attachment, and negative affect.

Effect Size Estimates

Effect sizes derived from individual studies represent the strength of the relationship between SLT predictor variables and TDV. Individual studies used a variety of statistical methods including correlation coefficients, standardized regression coefficients, and odds ratios. These were converted to r correlations and then Fisher's z using standard formulas (Borenstein et al. 2021; Peterson and Brown 2005; Pratt et al. 2014), consistent with common meta-analytic practices. Unstandardized regression coefficients were standardized using the formula provided by Rosenthal (1986). While some caution against combining regression coefficients from OLS and non-linear models, others argue that limiting the studies included to only linear or non-linear models would introduce more bias (Borenstein et al. 2021). To address this concern, the current meta-analysis includes effect sizes from linear, negative binomial, and logistic models and includes the measurement of the dependent variable (continuous, count, dichotomous) as a moderating variable. Variance for Fisher's z of bivariate models was calculated using the formula provided by Lipsey and Wilson (2001). If the multivariate effect size did not contain a standard error to compute the variance, these were converted using p-values or confidence intervals (Pratt et al. 2014). Eleven percent of the multivariate effect sizes did not contain enough information to calculate the standard error. To prevent eliminating these effect sizes from analysis entirely, these missing standard errors were imputed using mean substitution (Pigott 2001)¹.

Analytic Strategy

An assumption of meta-analyses is that each effect size is independent of each other (Lipsey and Wilson 2001). However, most individual studies contain multiple models, which results in multiple effect sizes derived from each study. Additionally, some studies use the same dataset, meaning that multiple effect sizes are derived from each sample. Traditional meta-analytic methods involve taking a mean effect size from each study and/or dataset, so that each sample is only represented once in the analysis. However, there are two caveats to this method. It reduces statistical power and masks important moderating differences for effect sizes from the same study (Assink and Wibbelink 2016; Pratt et al. 2010). To correct this issue, a mixed-effect, multi-level meta-analytic design was used. This method codes each effect size separately and then assesses variance between the sampling variance of individual effect sizes (level 1), effect sizes within the same dataset (level 2), and between datasets (level 3). Analyses were completed using the metaphor package in the R environment (Viechtbauer 2010). First, mean effect sizes were estimated for the entire sample and for males and females separately for each of the predictor variables. Next, likelihood-ratio test were completed to determine if there was significant variance at level 2 (effect sizes within a dataset) and level 3 (between datasets) and to determine the percentage of variance at each level. If there was significant variance at level 2 or 3 or less than 75% variance at level one, moderator analyses were completed.

A concern when estimating meta-analyses is publication bias, where published research is not representative of the population of completed studies (Rothstein, Sutton, and Borenstein 2016). It is likely that studies with null findings are less likely than those with significant findings to be published. Because published studies are more likely to be detected and included in a meta-analysis, this may artificially inflate the actual mean effect size of a meta-analysis (Borenstein et al. 2021). Trim-and-fill funnel plots (Duval and Tweedie 2000) were estimated for each predictor variable to correct this issue. This method imputes studies that may be missing to make the funnel plots more symmetrical. When funnel plots are filled with imputed studies on the left side, it means that studies included may be biased upwards, and when studies are imputed on the right side, it means that included studies may be biased downwards.

Results

Sample Composition

As stated earlier, 88 unique samples were included in analyses. Seventy-eight percent of the samples were derived from North America, 9% from Europe, 3% from South and Latin America, 5% from Africa, 3% from Asia, and 1% each from Australia and those that included more than one country. Most of these samples used general youth samples, and 11% used high risk samples of justice-involved, homeless, or maltreated youth. Most of the samples included both males and female respondents, 13% were male-only, and a single study contained only female respondents. For the 98 individual studies that contained multivariate effect sizes, 34% included measures of social learning from more than one domain. Forty-three percent of these studies contained a control variable consistent with another criminological perspective, including self-control, general strain, social bonds, and risky lifestyles. Approximately 40% of these studies only controlled for demographics.

Bivariate Mean Effect Sizes

Overall, 642 bivariate effect sizes from 61 studies using 49 unique datasets were included in the analysis. For the mixed-sex effect sizes, the relationships between SLT and TDV were significant for each predictor variable except general violence. The largest effect sizes were for peer TDV perpetration (mz = .279, p <.001) and anticipated rewards of TDV (mz = .254, p <.001). The male-only and female-only mean bivariate effect sizes were similar, and every effect size was significant except for witnessing IPV perpetrated by either the mother or the father. The only sex difference was that gender roles were not significant for female-only effect sizes. The largest effect sizes for male-only samples were peer TDV perpetration (mz = .234, p <.01) and acceptance of male TDV perpetration (mz = .241, p <.001). The largest effect sizes for the female only samples were peer TDV perpetration (mz = .230, p <.001) and acceptance of TDV perpetration (mz = .229, p <.001). Mean bivariate effect sizes are presented in Table 2.

Table 2.

Bivariate Effects.

Predictor	#Dataset	#E.S.	Mean z	95% C.I.	%Var.Level 1	Level 2Var.	% Var.Level 2	Level 3Var.	% Var.Level 3
Mixed Sample
Witness IPV	24	72	.133***	[.900 - .186]	100	.000	0	.000	0
Witness Father IPV	7	20	.161*	[.039 - .283]	100	.000	0	.000	0
Witness Mother IPV	7	20	.162*	[.400 - .284]	100	.000	0	.000	0
Experienced Family Violence	13	51	.165***	[.099 - .230]	100	.000	0	.000	0
Exposure to Community Violence	7	22	.243***	[.138 - .348]	100	.000	0	.000	0
Peer TDV Perpetration	7	57	.279***	[.181 - .377]	89.54	.000	0	.006	10.46
Peer Delinquency	7	14	.199**	[.072 - .325]	89.01	.000	0	.004	10.99
Social Acceptability of TDV	4	25	.162*	[.012 - .038]	84.55	.000	0	.005	15.45
Anticipated Rewards of TDV	3	21	.254***	[.153 - .356]	100	.000	0	.000	0
Acceptance of TDV	16	69	.198***	[.143 - .254]	100	.000	0	.000	0
Acceptance of Male TDV	12	107	.222***	[.120 - .324]	67.67	.000	0	.022***	32.33
Acceptance of Female TDV	11	102	.156***	[.103 - .209]	96.25	.000	0	.002	3.75
Acceptance of Violence	4	5	.180	[-.122 - .483]	100	.000	0	.000	0
Gender Role Beliefs	12	57	.099***	[.044 - .155]	100	.000	0	.000	0
Male Sample
Witness IPV	13	24	.156**	[.053 - .26]	100	.000	0	.000	0
Witness Father IPV	5	9	.132	[-.075 - .34]	100	.000	0	.000	0
Witness Mother IPV	5	9	.168	[-.031 - .368]	100	.000	0	.000	0
Experienced Family Violence	9	22	.196**	[.082 - .31]	100	.000	0	.000	0
Peer TDV Perpetration	3	25	.234**	[.089 - .378]	88.50	.000	0	.007	11.50
Peer Delinquency	5	9	.160*	[.012 - .309]	93.86	.000	0	.002	6.14
Acceptance of Male TDV	9	55	.241***	[.176 - .306]	98.88	.000	0	.001	1.12
Acceptance of Female TDV	8	42	.146***	[.076 - .215]	99.34	.000	0	.001	1.66
Gender Role Beliefs	9	26	.136**	[.05 - .223]	100	.000	0	.000	0
Female Sample
Witness IPV	8	17	.177**	[.049 - .305]	100	.000	0	.000	0
Witness Father IPV	5	9	.166	[-.029 - .36]	100	.000	0	.000	0
Witness Mother IPV	5	8	.145	[-.057 - .346]	100	.000	0	.000	0
Experienced Family Violence	5	16	.144*	[.005 - .283]	100	.000	0	.000
Peer DV Perpetration	3	24	.230***	[.134 - .326]	100	.000	0	.000	0
Acceptance of TDV	5	15	.229***	[.107 - .350]	100	.000	0	.000	0
Acceptance of Male TDV	9	42	.112***	[.054 - .171]	100	.000	0	.000	0
Acceptance of Female TDV	9	51	.184***	[.122 - .246]	100	.000	0	.000	0
Gender Role Beliefs	7	21	.066	[-.037 - .169]	100	.000	0	.000	0

Note: #E.S. = number of effect sizes, z = Fisher's z.

*p <.05, **p <.01, ***p <.001.

Multivariate Mean Effect Sizes

Overall, 515 multivariate effect sizes from 98 studies using 75 datasets were included. For the mixed-sex samples, findings were similar to the mean bivariate effect sizes in that most of the relationships between SLT predictors and TDV were significant. Witnessing father IPV, peer delinquency, and acceptance of general violence did not reach significance. The largest effect sizes were the same as the bivariate analyses and included anticipated rewards of TDV (mz = .308, p <.001) and peer TDV perpetration (mz = .205, p <.01). The male-only and female-only samples also had similar findings in both the bivariate and multivariate analyses. For the male-only samples, each predictor was significant except social acceptability of TDV, peer delinquency, and acceptance of male TDV. The largest effect sizes were for peer TDV perpetration (mz = .395, p <.001) and acceptance of TDV (mz = .213, p <.05). For the female only effect sizes, all predictors were significant except peer TDV perpetration, social acceptability of TDV, and acceptance of male TDV. The largest effect sizes for the female-only samples were smaller than those of males, with acceptance of TDV (mz = .158, p <.05) and acceptance of female TDV (mz = .156, p <.05) being the largest. Mean multivariate effect sizes are presented in Table 3.

Table 3.

Multivariate Effects

Predictor	#Dataset	#E.S.	Mean z	95% C.I.	%Var.Level 1	Level 2Var.	% Var.Level 2	Level 3Var.	% Var.Level 3
Mixed Sample
Witness IPV	20	63	.149***	[.09 - .209]	32.09	.003**	19.76	.008**	48.14
Witness Father IPV	4	6	.154	[-.042 - .349]	32.43	.000	0	.017	67.57
Witness Mother IPV	4	5	.099*	[.031 - .168]	72.31	.000	0	.001	27.69
Experienced Family Violence	17	72	.137***	[.081 - .193]	35.85	.003***	21.41	.006*	42.74
Exposure to Community Violence	8	40	.103**	[.03 - .177]	55.28	.000	0	.006*	44.72
Exposure to Family Violence	5	21	.113*	[.000 - .226]	29.42	.000	0	.011***	70.58
Peer TDV Perpetration	7	25	.205**	[.071 - .339]	23.44	.002***	0	.014***	76.56
Delinquent Peers	8	23	.104	[-.018 - .225]	30.2	.000	0	.016**	69.8
Social Acceptability of TDV	11	44	.165*	[.026 - .35]	2.9	.000	0	.044***	97.1
Anticipated Rewards of TDV	3	19	.308***	[.021 - .415]	87.51	.006	12.49	.000	0
Acceptance of TDV	23	76	.168**	[.07 - .266]	26.13	.000	0	.027***	73.87
Acceptance of Male TDV	7	29	.034*	[.002 - .066]	100	.000	0	.000	0
Acceptance of Female TDV	8	33	.105*	[.021 - .189]	36.72	.000	0	.006**	63.28
Acceptance of Violence	3	4	.071	[-.281 - .36]	100	.000	0	.000	0
Gender Role Beliefs	19	55	.09***	[.047 - .132]	49.38	.000	0	.003*	50.62
Male Sample
Witness IPV	8	20	.163***	[.110 - .215]	100	.000	0	.000	0
Experienced Family Violence	12	26	.115**	[.031 - .199]	35.29	.002*	15.83	.008	48.88
Peer TDV Perpetration	4	11	.395***	[.233 - .556]	100	.000	0	.000	0
Delinquent Peers	5	13	.183	[-.054 - .420]	30.78	.000	0	.027**	69.22
Social Acceptability of TDV	3	9	.220	[-.052 - .491]	12.65	.000	0	.056	87.35
Acceptance of TDV	12	31	.213*	[.048 - .378]	54.72	.000	0	.027*	45.28
Acceptance of Male TDV	4	11	.184	[-.092 - .460]	15.63	.000	0	.032*	84.36
Acceptance of Female TDV	6	13	.004	[-.031 - .040]	100	.000	0	.000	0
Gender Role Beliefs	11	23	.091*	[.019 - .162]	46.90	.000	0	.004*	53.10
Female Sample
Witness IPV	7	12	.128**	[.044 - .213]	21.97	.001	12.40	.006	65.63
Experienced Family Violence	5	18	.092*	[.015 - .168]	32.38	.005*	40.83	.003	26.79
Peer TDV Perpetration	4	5	.235	[-.061 - .532]	100	.000	0	.000	0
Social Acceptability of TDV	5	9	.226	[-.213 - .664]	5.52	.000	0	.108	94.48
Acceptance of TDV	7	14	.158*	[.019 - .297]	100	.000	0	.000	0
Acceptance of Male TDV	3	8	.009	[-.039 - .057]	98.74	.000	0	.000	1.26
Acceptance of Female TDV	6	12	.156*	[.013 - .299]	19.66	.000	0	.011**	80.34
Gender Role Beliefs	9	15	.081***	[.062 - .100]	100	.000	0	.000	0

Note: #E.S. = number of effect sizes, z = Fisher's z.

*p <.05, **p <.01, ***p <.001.

Moderating Analyses

Each moderator for predictor variables with sufficient variance was tested using an omnibus test (Assink and Wibbelink 2016). This was done to determine if the relationships between SLT predictors and TDV depended on sample characteristics, research design, and which controls were included in multivariate models. For the bivariate effect sizes, these moderators were largely unimportant. The only predictor that showed a significant difference was acceptance of male TDV, where the relationship between the predictor and TDV was dependent on whether a cross-sectional or longitudinal design was used, as well as which TDV scale was used. Many more significant moderators were detected for the multivariate effect sizes. In some cases, whether the model controlled for alternative social learning dimensions as well as other criminological theories such as social bonds and risky lifestyles determined whether the relationships between SLT predictors and TDV were significant. Several other moderators were significant for some of the predictor variables, including whether the sample was a general or high-risk sample, how TDV was measured, type of TDV, and geographical location. It appears that while moderators did not seem to matter for the bivariate relationships, research and sample characteristics, as well as covariates included in the models, were important when examining the multivariate relationships between SLT and TDV. Moderator analyses are presented in Table 4.

Table 4.

Moderating Analyses

Moderator	F (df1, df2)	p
Bivariate Mixed Sample
Acceptance of Male TDV
Longitudinal or Cross-sectional Research Design	F (1, 78) = 9.380**	.003
TDV Scale	F (2, 77) = 3.567*	.032
Multivariate Mixed Sample
Witness IPV
Longitudinal or Cross-sectional Research Design	F (1, 61) = 4.467*	.039
Control for Social Learning Theory	F (1, 61) = 12.238***	<.001
Control for TDV Victimization	F (1, 61) = 9.380**	.003
Control for Parental Attachment	F (1, 61) = 4.969*	.030
Victim of Family Violence
Sample Risk	F (1, 70) = 4.203*	.044
Control for Alcohol Use	F (1, 70) = 7.281**	.009
Control for Parental Attachment	F (1, 70) = 21.921***	<.001
Victim of Community Violence
Control for TDV Victimization	F (1, 38) = 6.162*	.018
Control for Socio-economic Status	F (1, 38) = 6.162*	.018
Exposure to Family Violence
Control for Self-control	F (1, 19) = 4.933*	.039
Peer TDV Perpetration
Follow-up Time	F (1, 25) = 5.581*	.023
Social Acceptability of TDV
Geographical Location	F (1, 42) = 8.608**	.005
Longitudinal or Cross-sectional Design	F (1, 42) = 5.285*	.027
TDV Outcome Type	F (4, 39) = 5.497***	<.001
Follow-up Time	F (1, 42) = 4.457*	.041
Acceptance of TDV
Follow-up Time	F (1, 76) = 4.037*	.048
Control for Social Learning Theory	F (1, 76) = 5.210*	.025
Gender Role Beliefs
TDV Outcome Type	F (4, 50) = 3.603*	.012
Control for Negative Affect	F (1, 53) = 6.219*	.016
Multivariate Male Sample
Victim of Family Violence
Sample Risk	F (1, 24) = 7.666*	.011
Delinquent Peers
Measurement of DV	F (1, 10) = 8.968**	.006
Gender Role Beliefs
TDV Outcome Type	F (4, 18) = 4.193*	.014
Control for Negative Affect	F (1, 21) = 4.414*	.048
Multivariate Female Sample
Victim of Family Violence
Longitudinal or Cross-sectional Research Design	F (1, 16) = 13.080**	.002
Control for Social Learning Theory	F (1, 16) = 13.080**	.002
Control for Alcohol Use	F (1, 16) = 6.793*	.019
Control for Parental Attachment	F (1, 16) = 15.339***	<.001
Social Acceptability of TDV
TDV Outcome Type	F (3, 5) = 26.489**	.002
Acceptance of Female TDV
Control for Negative Affect	F (1, 10) = 15.750**	.003

*p <.05, **p <.01, ***p <.001

Publication Bias

To determine the effects of publication bias, funnel plots were estimated for each of the predictor variables. For the bivariate mixed-sex sample effect sizes, funnel plots estimated for witnessing father IPV, experiencing family violence, exposure to community violence, social acceptability of TDV, acceptance of female TDV, and gender role beliefs showed missing studies on the left side. The remaining funnel plots did not show publication bias. For the male-only bivariate effect sizes, the funnel plots estimated for witnessing IPV, witnessing mother IPV, experiencing family violence, acceptance of male TDV and acceptance of female TDV showed studies missing on the left side. The remaining did not show publication bias. For female-only bivariate effect sizes, the funnel plots estimated for witnessing mother IPV, experiencing family violence, and gender role beliefs showed studies missing on the left side. Peer TDV perpetration, acceptance of violence, and acceptance of female TDV showed studies missing on the right side. The remaining predictor variables did not show publication bias.

For the multivariate mixed-sex sample effect sizes, funnel plots estimated for experiencing family violence, exposure to family violence, peer TDV perpetration, peer delinquency, acceptance of male TDV, and acceptance of female TDV showed studies missing on the left side. Anticipated rewards of TDV showed studies missing on the right side, and the remaining predictor variables did not show evidence of publication bias. For male-only multivariate effect sizes, funnel plots estimated for experiencing family violence, peer delinquency, acceptance of male TDV and gender role beliefs showed studies missing on the left side. Peer TDV perpetration and social acceptability of TDV showed studies missing on the right side, and the remaining predictor variables did not show publication bias. For female-only multivariate effect sizes, funnel plots estimated for witnessing IPV, experiencing family violence, peer TDV perpetration, acceptance of TDV, and acceptance of female TDV showed studies missing on the left side. Social acceptability of TDV showed studies missing on the right side, and the remaining predictor variables did not show evidence of publication bias. Due to space constraints, the funnel plots are not included here, but may be requested from the author.

Discussion

Teen dating violence is a pervasive problem in adolescence that has deleterious effects that last into adulthood, including continued violence against intimate partners (Leadbeater et al. 2014). TDV is commonly explained using social learning theory, however, prior research on TDV, SLT, and sex has been mixed. Because of these discrepancies, a meta-analysis was needed to “take stock” of the previous literature and generate an average effect size for predictors consistent with each of SLT's four dimensions. While there are other meta-analyses that examine TDV, most of these focus on family and peer risk factors more generally, rather than a specific criminological perspective. This study used hierarchal meta-analytic methods to answer three questions: 1. Which variables related to each SLT dimension have the largest effect size when predicting TDV perpetration? 2. Are there sex differences in SLT predictors? 3. Do sample characteristics and research design moderate these relationships?

To address the first research question, this study found that at least one predictor related to each social learning dimension significantly predicted TDV. Having peers that perpetrated TDV predicted TDV perpetration for most of the models and had among the largest effect sizes. Although the intergenerational transmission of violence hypothesis is widely accepted (Kwong et al. 2003), in this study, peer associations were more important than witnessing intimate partner violence between parents, which had smaller, and often non-significant, effect sizes. Acceptance of using violence to address relationships problems also predicted TDV in many of the models and had larger effect sizes than the predictors that measured violence more generally.

To address the second research question, most of the predictors of TDV perpetration did not vary between boys and girls. However, several sex differences were detected. Gender role beliefs were not significant for females for the bivariate effect sizes. However, they were for the multivariate. Similarly, peer TDV perpetration was not significant for females for the multivariate effect sizes like they were for males, and acceptance of female TDV perpetration was not significant for males in the multivariate model. Given that these predictors were all significant for each sex in at least one of the models, these findings support the use of SLT to predict TDV for both males and females. To address the third research question, this study found that every moderator tested was significant for at least one predictor variable, suggesting that the sample and methodology can influence findings. Implications for theory, research, and practice are discussed below.

Implications for Theory

SLT is purported to be a general theory of crime, or one that explains a variety of crime types for a variety of sample characteristics. While most research using SLT examines adult relationship violence, the findings of this current study suggest that SLT can also explain violence in teen relationships. This justifies the use of SLT despite differences in sample age, as good theories should be able to explain outcomes regardless of variations in sample characteristics and measurement (Shadish, Cook, & Campbell, 2002). When establishing causal relationships between predictors of crime and outcomes, it is vital that measures have construct validity, or are measuring what we think they are measuring (Sullivan & McGloin, 2014). Since most theoretical concepts in criminology are not standardized and have been conceptualized and measured in a variety of ways, a meta-analytic methods are useful in examining these discrepancies and can aid in theoretical development. One way of determining whether social learning theory can be used to explain TDV is by using a pattern matching approach (Trochim, 1989). Social learning theory assumes that four dimensions: differential association, differential reinforcement, beliefs, and imitation can explain offending (Akers 1973, 1998). One can then take the findings from the meta-analytic data to form an observed pattern, which can then be matched to the theoretical pattern of the four dimensions. While significant relationships support each of the dimensions, findings were dependent on measurement. For example, for the differential association dimension, peers who also perpetrated TDV was a stronger predictor than merely having delinquent peers. Similarly, for the belief dimension, effect sizes that captured the support for using gendered violence, rather than violence in general, better predicted TDV perpetration. This suggests that while the dimensions of SLT match the pattern of these meta-analytic data, the causal mechanisms of the theory might only work when the measurements of these dimensions specifically include TDV, rather than violence in general. While this provides some support for using learning theories to explain TDV, it is the predictors that specifically capture gendered violence, rather than general offending, that best explain TDV.

The generality-specificity debate of whether explanations of offending are different for males and females or sex-neutral has long been contested amongst criminologists. In the current study, similar mean effect sizes for males and females support similar rather than different effects of SLT mechanisms on TDV. This is consistent with other examinations of social learning theory, delinquency, and youth populations (Heimer and De Coster 1999). Relatedly, a meta-analysis on a risk assessment tool based on SLT found that the tool equally predicted recidivism for adolescent males and females, further supporting sex-neutral effects of SLT (Pusch and Holtfreter 2018). One explanation for this finding may be that sex differences of offending are more pronounced in adult, rather than adolescent, samples. Some contextual risk factors are unique to the experiences of adult women (Daly 1992), and previous research has found that sex differences of explanations of offending are more apparent in adult rather than adolescent samples (Jung et al. 2017; Neff and Waite 2007). However, the current study shows several sex differences. For example, peer TDV perpetration is significantly related to TDV for males, but not females in the multivariate studies. This is consistent with findings that peer effects seem to be a better predictor of delinquency for adolescent boys than adolescent girls (Piquero et al. 2005). Another difference that came to light for the bivariate effect sizes is that traditional gender role beliefs were important in predicting TDV for males, but not females. It is likely that females holding these beliefs also believe that it is unacceptable for females to be violent, as it goes against traditional gender stereotypes, therefore making it less likely that they would perpetrate TDV.

Implications for Research

A unique feature of meta-analyses is that they not only “take stock” of the research that has already been completed, but they also point to areas where more research is necessary (Pratt 2010). Significant moderator analyses in the current study reveal that SLT has differing success in explaining TDV depending on research and sample characteristics, and future research should focus on examining these nuances. For example, many of the significant moderators in the multivariate analyses pertain to what was included as a control in the individual studies. Scholars examining TDV in the future should be cognizant on how their research methodology and what they include as controls affect their outcomes.

While this meta-analysis offers some insight into social learning theory and TDV, it is important to note that studies that examine TDV tend to assume heterosexual relationships and a cis-gendered sample. A recent systematic analysis on IPV victimization found that less than 10% of studies included those who were LGBTQ + or sexual minority (Laskey, Bates, and Taylor 2019). This is concerning, as gender and sexual minority individuals have been found to be at greater risker for intimate partner violence (Walls et al. 2019; Whitton et al. 2019). There is a particular lack of research that examines youth populations. In the limited number of studies that have examined this topic, sexual and gender minority youth have been found to be more likely to experience dating violence, possibly due to discrimination and stress that may make them more vulnerable to victimization and perpetration of violence. Bisexual individuals have found to have a particularly high risk (Kiekens et al. 2021; Martin-Storey, Pollitt, and Baams 2021). Unfortunately, most of the individual studies included in this meta-analysis either do not include those in same-sex relationships, or do not control for it. Therefore, the limited number of same-sex studies made it impossible to differentiate between opposite-sex and same-sex relationships in the analyses. Additionally, studies often did not include if they were measuring biological sex or gender identity and assumed that respondents were cis-gendered. None of the individual studies included separate analyses for those that did not fit into the male/female binary, presumably due to the low prevalence of sexual minority gender identities in general youth samples. Future research should examine these nuances in adolescent populations, as previous research has focused on violence among LGTBQ + and sexual minority adult populations.

Implications for Practice

Findings from the current meta-analysis support using TDV prevention programs that use SLT as a theoretical framework. Currently, prevention programs are targeted in early adolescence, before dating is normative, and implemented universally in school settings. Programs such as Safe Dates and Dating Matters use a multi-pronged approach and address a variety of risk and protective factors to promote healthy relationships. Both programs include a social learning component to achieve program goals that include changing gender role norms and beliefs surrounding dating violence, and teaching prosocial ways of dealing with conflict in relationships (Foshee et al. 1998; Tharp 2012). Although social learning components were not able to be isolated, meta-analyses on the effectiveness of TDV prevention and intervention programs found that they were successful at changing attitudes related to TDV. However, mean effect sizes for the effectiveness of these programs at changing behavior were much smaller (De La Rue et al. 2017; Lee and Wong 2020). While findings from the current study support using SLT as a framework for prevention programs, some differences in effect sizes and significant moderator analyses suggest that there may be some nuances for effectiveness of SLT-based programs. While most TDV prevention programs are universally implemented, effectiveness varies by sex, with some studies finding better results for males, and some finding better results for females (Debnam and Temple 2021; Wolfe et al. 2009). The current study finds stronger peer effects for males than females, suggesting that sex specific TDV prevention programs that put a greater emphasis on peer relationships for males could be implemented. The current study also found that the relationship between being a victim of family violence and TDV perpetration differed depending on the risk level of the sample. This suggests that prevention programs focusing specifically on family violence and targeted high-risk populations might also be beneficial.

Limitations

Despite contributions to theory, research, and practice, this study has limitations. While many significant predictors in this meta-analysis suggest that SLT is valuable for explaining TDV perpetration, these predictors do not represent a complete test of the theory. It is possible that some of the predictors used span more than one element of SLT or other criminological perspectives entirely. Additionally, these predictors emphasize the learning that is brought into relationships, rather than the dynamics within the relationship themselves, and future research should focus on the integration of both social learning and contextual factors (Giordano et al. 2010). Of course, a meta-analysis is only as valid as the individual studies themselves. Including unpublished work means that studies of varying rigor are included in the meta-analysis, as these unpublished works have not been through the peer-review process. However, since a goal of a meta-analysis is to “take stock” of the entire body of research and be inclusive as possible, including unpublished work is still preferrable to leaving it out (Turanovic and Pratt 2021).

A related concern with the validity of individual studies concerns reporting violence perpetration. Since acts of violence committed by girls against their male partners may not seem as serious, and therefore more socially accepted than violence perpetrated by males against females, this may lead to inaccurate reporting of TDV involvement. Males may be hesitant to report participation in violence due to the stigmatization of violence against women, and girls may be more attentive to what constitutes interpersonal violence, and therefore report a wider range of behaviors (Hamby and Jackson 2010; Wincentak et al. 2017). Although self-reports of violence by adolescents are usually found to be relatively accurate (Huizinga and Elliot 1986), the potential for over-and under-reporting means that findings should be interpreted with this in mind.

Conclusions

Dating violence committed in adolescence is more than a fleeting teenage issue. It has real consequences that last well past the teen years and sets the stage for future adult relationships. The current meta-analysis supports the use of predictors derived from the four dimensions of SLT to explain dating violence perpetration in adolescence, and suggests that SLT elements are useful as a prevention strategy. While there is an ongoing debate about whether general criminological perspectives can explain offending for both males and females, this study supports sex-neutral effects of learning mechanisms. Hopefully, findings from the current study aid in the development of policies and programs that could help to reduce TDV perpetration rate.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Natasha Pusch

Notes

Author Biography

Natasha Pusch is an Assistant Professor in the Department of Sociology, Anthropology, and Social Work at Texas Tech University. She received her PhD in Criminology and Criminal Justice from Arizona State University. Her research interests include gender differences in adolescent offending and victimization, school violence, and teen dating violence. Her research has been published in the Journal of Youth and Adolescence, Criminal Justice and Behavior, and Youth and Society, among others.

References

Akers

Ronald L

. 1973. Deviant Behavior: A Social Learning Approach. Belmont, CA: Wadsworth.

Akers

Ronald L

. 1998. Social Learning and Social Structure: A General Theory of Crime and Deviance. Boston, MA: Northeastern University Press.

Akers

Ronald L

. 1999. “Social Learning and Social Structure: Reply to Sampson, Morash, and Krohn.” Theoretical Criminology 3:477–93.

Akers

Ronald L.

Jennings

Wes G.

. 2009 “The Social Learning Theory of Crime and Deviance.” Pp. 103–20 in Handbook of Criminology and Deviance, edited by Krohn

Lizotte

Penly Hall

. New York, NY: Springer.

Akers

Ronald L.

Jensen

Gary F.

. 2006 “The Empirical Status of Social Learning Theory of Crime and Deviance: The Past, Present, and Future.” Pp. 37–76 in Taking Stock: The Status of Criminological Theory, edited by Cullen

F. T.

Wright

J. P.

Blevins

K. R.

. New Brunswick, NJ: Transaction Publishers.

Ali

Bina

Swahn

Monica

Hamburger

Merle

. 2011. “Attitudes Affecting Physical Dating Violence Perpetration and Victimization: Findings from Adolescents in a High-Risk Urban Community.” Violence and Victims 26:669–83.

Antônio

Tiago

Koller

Silvia H.

Hokoda

Audrey

. 2012. “Peer Influences on the Dating Aggression Process Among Brazilian Street Youth: A Brief Report.” Journal of Interpersonal Violence 27:1579–92.

Archer

John

Fernández-Fuertes

Andrés A.

Thanzami

Van Lal

. 2010. “Does Cost–Benefit Analysis or Self-Control Predict Involvement in Two Forms of Aggression?” Aggressive Behavior 36:292–304.

Arriaga

Ximena B.

Foshee

Vangie A.

. 2004. “Adolescent Dating Violence: Do Adolescents Follow in Their Friends’, or Their Parents’, Footsteps?” Journal of Interpersonal Violence 19:162–84.

10.

Assink

Mark

Wibbelink

Carlijn JM

. 2016. “Fitting Three-Level Meta-Analytic Models in R: A Step-by-Step Tutorial.” The Quantitative Methods for Psychology 12:154–74.

11.

Bauman

K. E.

Ennett

S. T.

. 1996. “On the Importance of Peer Influence for Adolescent Drug Use: Commonly Neglected Considerations.” Addiction 91:185–98.

12.

Beckmann

Laura

. 2020. “Exposure to Family Violence and Adolescent Aggression in Multiple Social Contexts: Classroom Social Resources as Moderators.” Journal of Family Violence 35:471–84.

13.

Black

Beverly M.

Chido

Lisa M.

Preble

Kathleen M.

Weisz

Arlene N.

Yoon

Jina S.

Delaney-Black

Virginia

Kernsmith

Poco

Lewandowski

Linda

. 2015. “Violence Exposure and Teen Dating Violence Among African American Youth.” Journal of Interpersonal Violence 30:2174–95.

14.

Boivin

Sophie

Lavoie

Francine

Hébert

Martine

Gagne

Marie-Helene

. 2021. “Past Victimizations and Dating Violence Perpetration in Adolescence: The Mediating Role of Emotional Distress and Hostility.” Journal of Interpersonal Violence 27:662–84.

15.

Borenstein

Michael

Hedges

Larry V.

Higgins

Julian PT

Rothstein

Hannah R.

. 2021. Introduction to Meta-Analysis. Hoboken, NJ: John Wiley & Sons.

16.

Boyce

Sabrina C.

Deardorff

Julianna

Minnis

Alexandra M.

. 2020. “Relationship Factors Associated with Early Adolescent Dating Violence Victimization and Perpetration Among Latinx Youth in an Agricultural Community.” Journal of Interpersonal Violence. 37:11–12.

17.

Brendgen

Mara

Vitaro

Frank

Tremblay

Richard E.

Wanner

Brigitte

. 2002. “Parent and Peer Effects on Delinquency-Related Violence and Dating Violence: A Test of Two Mediational Models.” Social Development 11:225–44.

18.

Calvete

Esther

Fernández-González

Liria

Orue

Izaskun

Little

Todd D.

. 2018. “Exposure to Family Violence and Dating Violence Perpetration in Adolescents: Potential Cognitive and Emotional Mechanisms.” Psychology of Violence 8:67–75.

19.

Cava

María-Jesús

Martínez-Ferrer

Belén

Buelga

Sofía

Carrascosa

Laura

. 2020. “Sexist Attitudes, Romantic Myths, and Offline Dating Violence as Predictors of Cyber Dating Violence Perpetration in Adolescents.” Computers in Human Behavior 111:1–11.

20.

Centers for Disease Control and Prevention. 2019. Youth Risk Behavior Survey Data”. Retrieved September 14, 2021 (www.cdc.gov/yrbs).

21.

Chapple

Constance L

. 2003. “Examining Intergenerational Violence: Violent Role Modeling or Weak Parental Controls?” Violence and Victims 18:143–62.

22.

Chapple

Constance

Vaske

Jamie

Worthen

Meredith GF

. 2014. “Gender Differences in Associations with Deviant Peer Groups: Examining Individual, Interactional, and Compositional Factors.” Deviant Behavior 35:394–411.

23.

Clarey

Amy

Hokoda

Audrey

Ulloa

Emilio C.

. 2010. “Anger Control and Acceptance of Violence as Mediators in the Relationship Between Exposure to Interparental Conflict and Dating Violence Perpetration in Mexican Adolescents.” Journal of Family Violence 25:619–25.

24.

Clarke

Diaon

. 2018. “Exploring Childhood Trauma and Teen Dating Violence Experiences among a Clinical Sample of Youth Throughout NYC.” PhD dissertation, School of Social Service, Fordham University.

25.

Cochran

John K.

Jones

Shayne

Jones

Angela M.

Sellers

Christine S.

. 2016. “Does Criminal Propensity Moderate the Effects of Social Learning Theory Variables on Intimate Partner Violence?” Deviant Behavior 37:965–76.

26.

Cochran

John K.

Maskaly

Jon

Jones

Shayne

Sellers

Christine S.

. 2017. “Using Structural Equations to Model Akers’ Social Learning Theory with Data on Intimate Partner Violence.” Crime & Delinquency 63:39–60.

27.

Collins

W. Andrew

. 2003. “More Than Myth: The Developmental Significance of Romantic Relationships During Adolescence.” Journal of Research on Adolescence 13:1–24.

28.

Courtain

Audrey

Glowacz

Fabienne

. 2021. “Exploration of Dating Violence and Related Attitudes Among Adolescents and Emerging Adults.” Journal of Interpersonal Violence 36:2975–98.

29.

Daff

Elizabeth S.

McEwan

Troy E.

Luebbers

Stefan

. 2020. “The Role of Cognition in Youth Intimate Partner Abuse.” Journal of Interpersonal Violence 37:11:7–8.

30.

Daly

Kathleen

. 1992. “Women's Pathways to Felony Court: Feminist Theories of Lawbreaking and Problems of Representation.” Southern California Review of Law & Women's Studies 2:11–40.

31.

Debnam

Katrina J.

Temple

Jeff R.

. 2021. “Dating Matters and the Future of Teen Dating Violence Prevention.” Prevention Science 22:187–92.

32.

De La Rue

Lisa

Polanin

Joshua R.

Espelage

Dorothy L.

Pigott

Terri D.

. 2017. “A Meta-Analysis of School-Based Interventions Aimed to Prevent or Reduce Violence in Teen Dating Relationships.” Review of Educational Research 87:7–34.

33.

De Puy

Jacqueline

Hamby

Sherry L.

Lindemuth

Caroline

. 2014. “Teen Dating Violence in French-Speaking Switzerland: Attitudes and Experiences.” International Journal of Conflict and Violence 8:305–15.

34.

Doherty

Nicole A.

Feeney

Judith A.

. 2004. “The Composition of Attachment Networks Throughout the Adult Years.” Personal Relationships 11:469–88.

35.

Duval

Sue

Tweedie

Richard

. 2000. “Trim and Fill: A Simple Funnel-Plot–Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis.” Biometrics 56:455–63.

36.

Ellis

Wendy E.

Chung-Hall

Janet

Dumas

Tara M.

. 2013. “The Role of Peer Group Aggression in Predicting Adolescent Dating Violence and Relationship Quality.” Journal of Youth and Adolescence 42:487–99.

37.

Enosh

Guy

. 2007. “Cognition or Involvement? Explaining Sexual-Coercion in High-School Dating.” Sexual Abuse: A Journal of Research and Treatment 19:311–29.

38.

Espinoza

Guadalupe

Hokoda

Audrey

Ulloa

Emilio C.

Ulibarri

Monica D.

Castaneda

Donna

. 2012. “Gender Differences in the Relations Among Patriarchal Beliefs, Parenting, and Teen Relationship Violence in Mexican Adolescents.” Journal of Aggression, Maltreatment & Trauma 21:721–38.

39.

Exner-Cortens

Deinera

Eckenrode

John

Rothman

Emily

. 2013. “Longitudinal Associations Between Teen Dating Violence Victimization and Adverse Health Outcomes.” Pediatrics 131:71–8.

40.

Fawson

Peter R

. 2012. “Fostering Healthy Teen Intimate Relationships Through an In-school Violence Prevention Program.” PhD dissertation, College of Social Work, University of Utah.

41.

Fawson

Peter R

. 2015. “Controlling Behaviors as a Predictor of Partner Violence Among Heterosexual Female and Male Adolescents.” Partner Abuse 6:217–29.

42.

Fawson

Peter R.

Broce

Robert

MacNamara

Maureen

Gedney

Chris

. 2018. “Victim to Aggressor: The Relationship Between Intimate Partner Violence Victimization, Perpetration, and Mental Health Symptoms Among Teenage Girls.” Partner Abuse 9:3–17.

43.

Fernández-Fuertes

Andrés A.

Fuertes

Antonio

Fernández-Rouco

Noelia

Orgaz

Begoña

. 2019. “Past Aggressive Behavior, Costs and Benefits of Aggression, Romantic Attachment, and Teen Dating Violence Perpetration in Spain.” Children and Youth Services Review 100:376–83.

44.

Fernández-González

Liria

Calvete

Esther

Orue

Izaskun

. 2019. “The Role of Acceptance of Violence Beliefs and Social Information Processing on Dating Violence Perpetration.” Journal of Research on Adolescence 29:763–76.

45.

Flisher

Alan J.

Myer

Landon

Mèrais

Adele

Lombard

Carl

Reddy

Priscilla

. 2007. “Prevalence and Correlates of Partner Violence among South African Adolescents.” Journal of Child Psychology and Psychiatry 48:619–27.

46.

Fosco

Gregory M.

Xia

Mengya

Lynn

Mark G.

Grych

John H.

. 2016. “Triangulation and Parent–Adolescent Relationships: Implications for Adolescent Dating Competence and Abuse.” Journal of Research on Adolescence 26:524–37.

47.

Foshee

Vangie A.

Bauman

Karl E.

Arriaga

Ximena B.

Helms

Russell W.

Koch

Gary G.

Linder

George Fletcher

. 1998. “An Evaluation of Safe Dates, an Adolescent Dating Violence Prevention Program.” American Journal of Public Health 88:45–50.

48.

Foshee

Vangie A.

Bauman

Karl E.

Fletcher Linder

. 1999. “Family Violence and the Perpetration of Adolescent Dating Violence: Examining Social Learning and Social Control Processes.” Journal of Marriage and the Family 61:331–42.

49.

Foshee

Vangie A.

Benefield

Thad S.

McNaughton Reyes

Heath Luz

Ennett

Susan T.

Faris

Robert

Chang

Ling-Yin

Hussong

Andrea

Suchindran

Chirayath M.

. 2013. “The Peer Context and the Development of the Perpetration of Adolescent Dating Violence.” Journal of Youth and Adolescence 42:471–86.

50.

Foshee

Vangie A.

Ennett

Susan T.

Bauman

Karl E.

Benefield

Thad

Suchindran

Chirayath

. 2005. “The Association Between Family Violence and Adolescent Dating Violence Onset: Does it Vary by Race, Socioeconomic Status, and Family Structure?” The Journal of Early Adolescence 25:317–44.

51.

Foshee

Vangie A.

Linder

Fletcher

MacDougall

James E.

Bangdiwala

Shrikant

. 2001. “Gender Differences in the Longitudinal Predictors of Adolescent Dating Violence.” Preventive Medicine 32:128–41.

52.

Foshee

Vangie A.

Luz McNaughton Reyes

Chen

May S.

Ennett

Susan T.

Basile

Kathleen C.

DeGue

Sarah

Vivolo-Kantor

Alana M.

Moracco

Kathryn E.

Michael Bowling

. 2016. “Shared Risk Factors for the Perpetration of Physical Dating Violence, Bullying, and Sexual Harassment Among Adolescents Exposed to Domestic Violence.” Journal of Youth and Adolescence 45:672–86.

53.

Foshee

Vangie A

McNaughton Reyes

Heather Luz

Ennett

Susan T.

. 2010. “Examination of Sex and Race Differences in Longitudinal Predictors of the Initiation of Adolescent Dating Violence Perpetration.” Journal of Aggression, Maltreatment & Trauma 19:492–516.

54.

Foshee

Vangie A

McNaughton Reyes

Heath Luz

Ennett

Susan T.

Suchindran

Chirayath

Mathias

Jasmine P.

Karriker-Jaffe

Katherine J.

Bauman

Karl E.

Benefield

Thad S.

. 2011. “Risk and Protective Factors Distinguishing Profiles of Adolescent Peer and Dating Violence Perpetration.” Journal of Adolescent Health 48:344–50.

55.

Foshee

Vangie A

McNaughton Reyes

Heath Luz

Vivolo-Kantor

Alana M.

Basile

Kathleen C.

Chang

Ling-Yin

Faris

Robert

Ennett

Susan T.

. 2014. “Bullying as a Longitudinal Predictor of Adolescent Dating Violence.” Journal of Adolescent Health 55:439–44.

56.

Foshee

Vangie A.

Reyes

Luz McNaughton

Tharp

Andra T.

Chang

Ling-Yin

Ennett

Susan T.

Simon

Thomas R.

Latzman

Natasha E.

Suchindran

Chiravath

. 2015. “Shared Longitudinal Predictors of Physical Peer and Dating Violence.” Journal of Adolescent Health 56:106–12.

57.

Friedlander

Laura J.

Connolly

Jennifer A.

Pepler

Debra J.

Craig

Wendy M.

. 2013. “Extensiveness and Persistence of Aggressive Media Exposure as Longitudinal Risk Factors for Teen Dating Violence.” Psychology of Violence 3:310–22.

58.

Furukawa

Toshi A.

Barbui

Corrado

Cipriani

Andrea

Brambilla

Paolo

Watanabe

Norio

. 2006. “Imputing Missing Standard Deviations in Meta-Analyses can Provide Accurate Results.” Journal of Clinical Epidemiology 59:7–10.

59.

Garrido

Edward F.

Taussig

Heather N.

. 2013. “Do Parenting Practices and Prosocial Peers Moderate the Association Between Intimate Partner Violence Exposure and Teen Dating Violence?” Psychology of Violence 3:354–66.

60.

Garthe

Rachel

. 2014. “Longitudinal Relations between Parental Monitoring, Parental Acceptance, and Externalizing Behaviors among Urban African American Adolescents.” PhD dissertation, Department of Psychology, Virginia Commonwealth University.

61.

Garthe

Rachel C.

Sullivan

Terri N.

Farrell

Albert

. 2018. “Dating Violence Perpetration and Perceived Parental Support for Fighting and Nonviolent Responses to Conflict: An Autoregressive Cross-Lagged Model.” Journal of Adolescence 68:221–31.

62.

Garthe

Rachel C.

Sullivan

Terri N.

McDaniel

Michael A.

. 2017. “A Meta-Analytic Review of Peer Risk Factors and Adolescent Dating Violence.” Psychology of Violence 7:45–57.

63.

Gilbert

Leah K.

Annor

Francis B.

Kress

Howard

. 2020. “Associations Between Endorsement of Inequitable Gender Norms and Intimate Partner Violence and Sexual Risk Behaviors Among Youth in Nigeria: Violence Against Children Survey, 2014.” Journal of Interpersonal Violence 37:11–12.

64.

Giordano

Peggy C.

Kaufman

Angela M.

Manning

Wendy D.

Longmore

Monica A.

. 2015. “Teen Dating Violence: The Influence of Friendships and School Context.” Sociological Focus 48:150–71.

65.

Giordano

Peggy C.

Soto

Danielle A.

Manning

Wendy D.

Longmore

Monica A.

. 2010. “The Characteristics of Romantic Relationships Associated with Teen Dating Violence.” Social Science Research 39:863–74.

66.

Glass

Gene V

. 2015. “Meta-Analysis at Middle Age: A Personal History.” Research Synthesis Methods 6:221–31.

67.

Gomez

Anupama Manchikanti

. 2010. “Intersections of Gender Equity, Intimate Partner Violence and Relationship Dynamics Among Brazilian Youth.” PhD dissertation, Department of Maternal and Child Health, The University of North Carolina at Chapel Hill.

68.

Grasley

Carolyn

. 2002. “Abuse in Teen Dating: A Multi-Factorial Analysis of the Association Between Child Maltreatment and Abuse in Subsequent Intimate Relationships.” PhD dissertation, University of Western Ontario.

69.

Grych

John H.

Kinsfogel

Kristen M.

2010. “Exploring the Role of Attachment Style in the Relation Between Family Aggression and Abuse in Adolescent Dating Relationships.” Journal of Aggression, Maltreatment & Trauma 19:624–40.

70.

Hall

Diane M

. 2001. “Adolescent Dating Violence: An Exploratory investigation of context and correlates.” PhD dissertation, Department of Education, University of Pennsylvania.

71.

Hamby

Sherry

Jackson

Amy

. 2010. “Size Does Matter: The Effects of Gender on Perceptions of Dating Violence.” Sex Roles 63:324–31.

72.

Hare

Amanda L.

Miga

Erin M.

Allen

Joseph P.

. 2009. “Intergenerational Transmission of Aggression in Romantic Relationships: The Moderating Role of Attachment Security.” Journal of Family Psychology 23:808–18.

73.

Hawkins

Deborah Lynn

. 2001. “The Contribution of Child Maltreatment and Gender-role Attitudes to Aggressive Teen Relationships.” Master’s thesis, Department of Psychology, York University.

74.

Hébert

Martine

Daspe

Marie-Ève

Lapierre

Andréanne

Godbout

Natacha

Blais

Martin

Fernet

Mylène

Lavoie

Francine

. 2019. “A Meta-Analysis of Risk and Protective Factors for Dating Violence Victimization: The Role of Family and Peer Interpersonal Context.” Trauma, Violence, & Abuse 20:574–90.

75.

Heimer

Karen

De Coster

Stacy

. 1999. “The Gendering of Violent Delinquency.” Criminology; An Interdisciplinary Journal 37:277–318.

76.

Hollins

Leslie Dickman

. 2008. “Influence of Exposure to Child Maltreatment, Domestic Violence and Community Violence on Youth Aggression”. PhD dissertation, Department of Psychology, University of Illinois at Chicago.

77.

Hopper

Laura

. 2011. “Contributions of Individual and Friend Attitudes to Dating violence Experience in Adolescents.” PhD dissertation, Department of Psychology, Alliant International University.

78.

Huizinga

David

Elliott

Delbert S.

. 1986. “Reassessing the Reliability and Validity of Self-Report Delinquency Measures.” Journal of Quantitative Criminology 2:293–327.

79.

Hunter

Heather L

“Family Factors and Risk Behavior in Adolescent Dating Relationships: Heterosocial Competence as a Mediator Between Interparental Conflict and Dating Violence.” PhD dissertation, Department of Psychology, University of Kansas.

80.

Johnson

Renee M.

LaValley

Michael

Schneider

Kristin E.

Musci

Rashelle J.

Pettoruto

Kayley

Rothman

Emily F.

. 2017. “Marijuana Use and Physical Dating Violence Among Adolescents and Emerging Adults: A Systematic Review and Meta-Analysis.” Drug and Alcohol Dependence 174:47–57.

81.

Josephson

Wendy L.

Proulx

Jocelyn B.

. 2008. “Violence in Young Adolescents’ Relationships: A Path Model.” Journal of Interpersonal Violence 23:189–208.

82.

Jouriles

Ernest N.

Grych

John H.

Rosenfield

David

McDonald

Renee

Catherine Dodson

. 2011. “Automatic Cognitions and Teen Dating Violence.” Psychology of Violence 1:302–14.

83.

Jouriles

Ernest N.

Mueller

Victoria

Rosenfield

David

McDonald

Renee

Catherine Dodson

. 2012. “Teens’ Experiences of Harsh Parenting and Exposure to Severe Intimate Partner Violence: Adding Insult to Injury in Predicting Teen Dating Violence.” Psychology of Violence 2:125–38.

84.

Jung

Hyunzee

Herrenkohl

Todd I.

Lee

Jungeun Olivia

Hemphill

Sheryl A.

Heerde

Jessica A.

Skinner

Martie L.

. 2017. “Gendered Pathways from Child Abuse to Adult Crime Through Internalizing and Externalizing Behaviors in Childhood and Adolescence.” Journal of Interpersonal Violence 32:2724–50.

85.

Kaufman

Angela M

2014. “Familial Background and Relationship-Specific Correlates of Intimate Partner Violence across the Life-course”. PhD dissertation, Graduate College, Bowling Green State University.

86.

Kaukinen

Catherine

. 2014. “Dating Violence Among College Students: The Risk and Protective Factors.” Trauma, Violence, & Abuse 15:283–96.

87.

Kidman

Rachel

Kohler

Hans-Peter

. 2020. “Emerging Partner Violence Among Young Adolescents in a Low-Income Country: Perpetration, Victimization and Adversity.” PLOS One 15, Online First.

88.

Kiekens

W. J.

Baams

Laura

Fish

J. N.

Watson

R. J.

. 2021. “Associations of Relationship Experiences, Dating Violence, Sexual Harassment, and Assault with Alcohol use Among Sexual and Gender Minority Adolescents.” Journal of Interpersonal Violence. 37:17–18.

89.

Kim

Kerri L.

Jackson

Hunter

Heather L.

Conrad

Selby M.

. 2009. “Interparental Conflict and Adolescent Dating Relationships: The Role of Perceived Threat and Self-Blame Appraisals.” Journal of Interpersonal Violence 24:844–65.

90.

Kim

Jeongsuk

Macy

Rebecca J.

Wretman

Christopher J.

. 2021. “The Effect of Early Exposure to Violence on Acceptance of Violence and Dating Violence Perpetration in University Students: The Moderating Role of Pro-Violence Messages.” Journal of Aggression, Maltreatment & Trauma: 31:609–27.

91.

Kinsfogel

Kristen M

. 2001. “A Comprehensive Model of Dating Violence: Testing the Integration of Social Learning and Attachment Theories. PhD Dissertation, Department of Psychology, Marquette University.

92.

Kinsfogel

Kristen M.

Grych

John H.

. 2004. “Interparental Conflict and Adolescent Dating Relationships: Integrating Cognitive, Emotional, and Peer Influences.” Journal of Family Psychology 18:505–15.

93.

Krauss

Alison

Jouriles

Ernest N.

McDonald

Renee

Rosenfield

David

. 2020. “Measuring Teen Dating Violence Perpetration: A Comparison of Cumulative and Single Assessment Procedures.” Psychology of Violence.

94.

Kruis

Nathan E.

Seo

Chunghyeon

Kim

Bitna

. 2020. “Revisiting the Empirical Status of Social Learning Theory on Substance Use: A Systematic Review and Meta-Analysis.” Substance Use & Misuse 55:666–83.

95.

Kwong

Marilyn J.

Bartholomew

Kim

Henderson

Antonia JZ

Trinke

Shanna J.

. 2003. “The Intergenerational Transmission of Relationship Violence.” Journal of Family Psychology 17:288–301.

96.

Laskey

Philippa

Bates

Elizabeth A.

Taylor

Julie C.

. 2019. “A Systematic Literature Review of Intimate Partner Violence Victimisation: An Inclusive Review Across Gender and Sexuality.” Aggression and Violent Behavior 47:1–11.

97.

Latzman

Natasha E.

Vivolo-Kantor

Alana M.

Niolon

Phyllis Holditch

Ghazarian

Sharon R.

. 2015. “Predicting Adolescent Dating Violence Perpetration: Role of Exposure to Intimate Partner Violence and Parenting Practices.” American Journal of Preventive Medicine 49:476–82.

98.

Lavoie

Francine

Hébert

Martine

Tremblay

Richard

Vitaro

Frank

Vézina

Lucie

McDuff

Pierre

. 2002. “History of Family Dysfunction and Perpetration of Dating Violence by Adolescent Boys: A Longitudinal Study.” Journal of Adolescent Health 30:375–83.

99.

Leadbeater

Bonnie J.

Thompson

Kara

Sukhawathanakul

Paweena

. 2014. “It Gets Better, or Does it? Peer Victimization and Internalizing Problems in the Transition to Young Adulthood.” Development and Psychopathology 26:675–88.

100.

Lee

Chelsey

Wong

Jennifer S.

. 2020. “Examining the Effects of Teen Dating Violence Prevention Programs: A Systematic Review and Meta-Analysis.” Journal of Experimental Criminology, 18:1–40.

101.

Lichter

Erika L.

McCloskey

Laura A.

. 2004. “The Effects of Childhood Exposure to Marital Violence on Adolescent Gender-Role Beliefs and Dating Violence.” Psychology of Women Quarterly 28:344–57.

102.

Linder

Jennifer R

. 2002. “Parent and Peer Predictors of Physical Aggression, Verbal Aggression, and Conflict Management in Romantic Relationships in Late Adolescence and Young Adulthood: A Prospective Developmental Study. Master’s thesis, Graduate School, University of Minnesota.

103.

Lipsey

Mark W.

Wilson

David B.

. 2001. Practical Meta-Analysis. Thousand Oaks, CA: Sage Publications, Inc.

104.

Malik

Shaista

Sorenson

Susan B.

Aneshensel

Carol S.

. 1997. “Community and Dating Violence Among Adolescents: Perpetration and Victimization.” Journal of Adolescent Health 21:291–302.

105.

Martin-Storey

Alexa

Pollitt

Amanda M.

Baams

Laura

. 2021. “Profiles and Predictors of Dating Violence Among Sexual and Gender Minority Adolescents.” Journal of Adolescent Health 68:1155–61.

106.

McCauley

Heather L.

Tancredi

Daniel J.

Silverman

Jay G.

Decker

Michele R.

Bryn Austin

McCormick

Marie C.

Virata

Maria Catrina

Miller

Elizabeth

. 2013. “Gender-Equitable Attitudes, Bystander Behavior, and Recent Abuse Perpetration Against Heterosexual Dating Partners of Male High School Athletes.” American Journal of Public Health 103:1882–7.

107.

McDonell

Jim

Ott

Joyce

Mitchell

Margaret

. 2010. “Predicting Dating Violence Victimization and Perpetration Among Middle and High School Students in a Rural Southern Community.” Children and Youth Services Review 32:1458–63.

108.

Miller

Elizabeth

Culyba

Alison J.

Paglisotti

Taylor

Massof

Michael

Gao

Ports

Katie A.

Kato-Wallace

Jane

, et al. 2020. “Male Adolescents’ Gender Attitudes and Violence: Implications for Youth Violence Prevention.” American Journal of Preventive Medicine 58:396–406.

109.

Miller

Shari

Gorman-Smith

Deborah

Sullivan

Terri

Orpinas

Pamela

Simon

Thomas R.

. 2009. “Parent and Peer Predictors of Physical Dating Violence Perpetration in Early Adolescence: Tests of Moderation and Gender Differences.” Journal of Clinical Child & Adolescent Psychology 38:538–50.

110.

Mitchell

Mary G

. 1994. “Jealousy and Violence in High School Dating Relationships.” PhD dissertation, Department of Education, The University of Iowa.

111.

Molidor

Christian

Tolman

Richard M.

. 1998. “Gender and Contextual Factors in Adolescent Dating Violence.” Violence Against Women 4:180–94.

112.

Morash

Merry

. 1999. “A Consideration of Gender in Relation to Social Learning and Social Structure: A General Theory of Crime and Deviance.” Theoretical Criminology 3:451–62.

113.

Moretti

Marlene M.

Obsuth

Ingrid

Odgers

Candice L.

Reebye

Pratibha

. 2006. “Exposure to Maternal vs. Paternal Partner Violence, PTSD, and Aggression in Adolescent Girls and Boys.” Aggressive Behavior: Official Journal of the International Society for Research on Aggression 32:385–95.

114.

Morris

Anjana Madan

Mrug

Sylvie

Windle

Michael

. 2015. “From Family Violence to Dating Violence: Testing a Dual Pathway Model.” Journal of Youth and Adolescence 44:1819–35.

115.

Mueller

Victoria

Jouriles

Ernest N.

McDonald

Renee

Rosenfield

David

. 2013. “Adolescent Beliefs About the Acceptability of Dating Violence: Does Violent Behavior Change Them?” Journal of Interpersonal Violence 28:436–50.

116.

Nardi-Rodríguez

Ainara

Pastor-Mira

María de los Ángeles

López-Roig

Sofía

Ferrer-Pérez

Victoria A.

. 2019. “What do Adolescents Believe About Performing and Accepting Intimate Partner Violence Behaviors? A Reasoned Action Approach.” Journal of Family Violence 34:461–77.

117.

Neff

Joan L.

Waite

Dennis E.

. 2007. “Male Versus Female Substance Abuse Patterns Among Incarcerated Juvenile Offenders: Comparing Strain and Social Learning Variables.” Justice Quarterly 24:106–32.

118.

Niolon

Phyllis Holditch

Vivolo-Kantor

Alana M.

Latzman

Natasha E.

Valle

Linda Anne

Kuoh

Henrietta

Burton

Tessa

Taylor

Bruce G.

Tharp

Andra T.

. 2015. “Prevalence of Teen Dating Violence and Co-Occurring Risk Factors Among Middle School Youth in High-Risk Urban Communities.” Journal of Adolescent Health 56:S5–S13.

119.

O'Keefe

Maura

. 1997. “Predictors of Dating Violence Among High School Students.” Journal of Interpersonal Violence 12:546–68.

120.

Park

Sihyun

Kim

Sin-Hyang

. 2018. “The Power of Family and Community Factors in Predicting Dating Violence: A Meta-Analysis.” Aggression and Violent Behavior 40:19–28.

121.

Peitzmeier

Sarah M.

Malik

Mannat

Kattari

Shanna K.

Marrow

Elliot

Stephenson

Rob

Agénor

Madina

Reisner

Sari L.

. 2020. “Intimate Partner Violence in Transgender Populations: Systematic Review and Meta-Analysis of Prevalence and Correlates.” American Journal of Public Health 110:1–14.

122.

Peskin

Melissa F.

Markham

Christine M.

Shegog

Ross

Temple

Jeff R.

Baumler

Elizabeth R.

Addy

Robert C.

Hernandez

Belinda

, et al. 2017. “Prevalence and Correlates of the Perpetration of Cyber Dating Abuse Among Early Adolescents.” Journal of Youth and Adolescence 46:358–75.

123.

Peterson

Robert A.

Brown

Steven P.

. 2005. “On the use of Beta Coefficients in Meta-Analysis.” Journal of Applied Psychology 90:175.

124.

Pflieger

Jacqueline C.

Vazsonyi

Alexander T.

. 2006. “Parenting Processes and Dating Violence: The Mediating Role of Self-Esteem in Low-and High-SES Adolescents.” Journal of Adolescence 29:495–512.

125.

Pigott

Therese D

. 2001. “Missing Predictors in Models of Effect Size.” Evaluation & the Health Professions 24:277–307.

126.

Piquero

Nicole Leeper

Gover

Angela R.

MacDonald

John M.

Piquero

Alex R.

. 2005. “The Influence of Delinquent Peers on Delinquency: Does Gender Matter?” Youth & Society 36:251–75.

127.

Pöllänen

Katri

de Vries

Hein

Mathews

Catherine

Schneider

Francine

de Vries

Petrus J.

. 2021. “Beliefs About Sexual Intimate Partner Violence Perpetration Among Adolescents in South Africa.” Journal of Interpersonal Violence 36:2056–78.

128.

Pratt

Travis C

. 2010. “Meta-Analysis in Criminal Justice and Criminology: What it is, When It's Useful, and What to Watch Out For.” Journal of Criminal Justice Education 21:152–68.

129.

Pratt

Travis C.

Cullen

Francis T.

. 2000. “The Empirical Status of Gottfredson and Hirschi's General Theory of Crime: A Meta-Analysis.” Criminology; An Interdisciplinary Journal 38:931–64.

130.

Pratt

Travis C.

Cullen

Francis T.

Blevins

Kristie R.

Daigle

Leah

Unnever

James D.

. 2002. “The Relationship of Attention Deficit Hyperactivity Disorder to Crime and Delinquency: A Meta-Analysis.” International Journal of Police Science & Management 4:344–60.

131.

Pratt

Travis C.

Cullen

Francis T.

Sellers

Christine S.

Thomas Winfree

Jr Madensen

Tamara D.

Daigle

Leah E.

Fearn

Noelle E.

Gau

Jacinta M.

. 2010. “The Empirical Status of Social Learning Theory: A Meta-Analysis.” Justice Quarterly 27:765–802.

132.

Pratt

Travis C.

McGloin

Jean Marie

Fearn

Noelle E.

. 2006. “Maternal Cigarette Smoking During Pregnancy and Criminal/Deviant Behavior: A Meta-Analysis.” International Journal of Offender Therapy and Comparative Criminology 50:672–90.

133.

Pratt

Travis C.

Turanovic

Jillian J.

Fox

Kathleen A.

Wright

Kevin A.

. 2014. “Self-Control and Victimization: A Meta-Analysis.” Criminology; An Interdisciplinary Journal 52:87–116.

134.

Price

Lisa E

. 2002. “Risk Factors for Boys’ Psychologically Abusive Behavior in Dating Relationships.” PhD dissertation, Department of Psychology, University of New Brunswick.

135.

Price

Lisa

Sandra Byers

Belliveau

Nicole

Bonner

Robert

Caron

Bruno

Doiron

Daniel

Greenough

Jan

, et al. 1999. “The Attitudes Towards Dating Violence Scales: Development and Initial Validation.” Journal of Family Violence 14:351–75.

136.

Pusch

Natasha

Holtfreter

Kristy

. 2018. “Gender and Risk Assessment in Juvenile Offenders: A Meta-Analysis.” Criminal Justice and Behavior 45:56–81.

137.

Pusch

Natasha

Holtfreter

Kristy

. 2021. “Sex-based Differences in Criminal Victimization of Adolescents: A Meta-Analysis.” Journal of Youth and Adolescence 50:4–28.

138.

Pusch

Natasha

Reisig

Michael D.

2021. “The Overlap Between Teen Dating Violent Offending and Violent Victimization.” Criminal Justice and Behavior 48:1468–86.

139.

Puzzanchera

Charles

. 2020. “Dating Violence Reported by High School Students, 2017”. Washington, DC: Office of Juvenile Justice and Delinquency Prevention.

140.

Reed

Lauren A.

Monique Ward

Tolman

Richard M.

Lippman

Julia R.

Seabrook

Rita C.

. 2021. “The Association Between Stereotypical Gender and Dating Beliefs and Digital Dating Abuse Perpetration in Adolescent Dating Relationships.” Journal of Interpersonal Violence.

141.

Reeves

Patricia M.

Orpinas

Pamela

. 2012. “Dating Norms and Dating Violence Among Ninth Graders in Northeast Georgia: Reports from Student Surveys and Focus Groups.” Journal of Interpersonal Violence 27:1677–98.

142.

Reyes

H. Luz McNaughton

Foshee

Vangie A.

. 2013. “Sexual Dating Aggression Across Grades 8 Through 12: Timing and Predictors of Onset.” Journal of Youth and Adolescence 42:581–95.

143.

Reyes

H. Luz McNaughton

Foshee

Vangie A.

Chen

May S.

Ennett

Susan T.

. 2017. “Patterns of Dating Violence Victimization and Perpetration Among Latino Youth.” Journal of Youth and Adolescence 46:1727–42.

144.

Reyes

H. Luz McNaughton

Foshee

Vangie A.

Chen

May S.

Ennett

Susan T.

. 2019. “Patterns of Adolescent Aggression and Victimization: Sex Differences and Correlates.” Journal of Aggression, Maltreatment & Trauma 28:1130–50.

145.

Reyes

H. Luz McNaughton

Foshee

Vangie A.

Fortson

Beverly L.

Valle

Linda A.

Breiding

Matthew J.

Merrick

Melissa T.

. 2015. “Longitudinal Mediators of Relations Between Family Violence and Adolescent Dating Aggression Perpetration.” Journal of Marriage and Family 77:1016–30.

146.

Reyes

H. Luz McNaughton

Foshee

Vangie A.

Niolon

Phyllis Holditch

Reidy

Dennis E.

Hall

Jeffrey E.

. 2016. “Gender Role Attitudes and Male Adolescent Dating Violence Perpetration: Normative Beliefs as Moderators.” Journal of Youth and Adolescence 45:350–60.

147.

Richards

Tara N.

Branch

Kathryn A.

. 2012. “The Relationship Between Social Support and Adolescent Dating Violence: A Comparison Across Genders.” Journal of Interpersonal Violence 27:1540–61.

148.

Richards

Tara N.

Branch

Kathryn A.

Ray

Katherine

. 2014. “The Impact of Parental and Peer Social Support on Dating Violence Perpetration and Victimization Among Female Adolescents: A Longitudinal Study.” Violence and Victims 29:317–31.

149.

Rivera-Rivera

Leonor

Allen-Leigh

Betania

Rodríguez-Ortega

Graciela

Chávez-Ayala

Rubén

Lazcano-Ponce

Eduardo

. 2007. “Prevalence and Correlates of Adolescent Dating Violence: Baseline Study of a Cohort of 7960 Male and Female Mexican Public-School Students.” Preventive Medicine 44:477–84.

150.

Rivera

Michelle S

. 2008. “Evaluating a Social Learning Theory of Dating Aggression: The Role of Relational Aggression.” PhD dissertation, Department of Psychology, The University of Maine.

151.

Rosenthal

Robert

. 1986. “Meta-Analytic Procedures for Social Science Research Sage Publications: Beverly Hills, 1984, 148 pp.” Educational Researcher 15:18–20.

152.

Rostad

Whitney L.

Gittins-Stone

Daniel

Huntington

Charlie

Rizzo

Christie J.

Pearlman

Deborah

Orchowski

Lindsay

. 2019. “The Association Between Exposure to Violent Pornography and Teen Dating Violence in Grade 10 High School Students.” Archives of Sexual Behavior 48:2137–47.

153.

Rothman

Emily F.

Stuart

Gregory L.

Greenbaum

Paul E.

Heeren

Timothy

Bowen

Deborah J.

Vinci

Robert

Baughman

Allyson L.

Bernstein

Judith

. 2011. “Drinking Style and Dating Violence in a Sample of Urban, Alcohol-Using Youth.” Journal of Studies on Alcohol and Drugs 72:555–66.

154.

Rothstein

Hannah R.

Sutton

Alexander J.

Borenstein. (Eds.)

Michael

. 2006. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments. Hoboken, NJ: John Wiley & Sons.

155.

Ruel

Catherine

Lavoie

Francine

Hébert

Martine

Blais

Martin

. 2020. “Gender’s Role in Exposure to Interparental Violence, Acceptance of Violence, Self-Efficacy, and Physical Teen Dating Violence Among Quebec Adolescents.” Journal of Interpersonal Violence 35:3079–101.

156.

Russell

Marcia

Cupp

Pamela K.

Jewkes

Rachel K.

Gevers

Anik

Mathews

Catherine

LeFleur-Bellerose

Chantel

Small

Jeon

. 2014. “Intimate Partner Violence Among Adolescents in Cape Town, South Africa.” Prevention Science, 15: 283–95.

157.

Schwartz

Christine A

. 2003. “Adolescent Dating Violence and Self-efficacy.” PhD dissertation, Department of Psychology, University of Victoria.

158.

Schwartz

Miguel

O’Leary

Susan G.

Kendziora

Kimberly T.

. 1997. “Dating Aggression Among High School Students.” Violence and Victims 12:295–305.

159.

Scott

Terri

Brown

Shelley L.

. 2018. “Risks, Strengths, Gender, and Recidivism among Justice-Involved Youth: A Meta-Analysis.” Journal of Consulting and Clinical Psychology 86:931.

160.

Sears

Heather A.

Sandra Byers

Lisa Price

. 2007. “The Co-Occurrence of Adolescent Boys’ and Girls’ Use of Psychologically, Physically, and Sexually Abusive Behaviors in Their Dating Relationships.” Journal of Adolescence 30:487–504.

161.

Sellers

Christine S.

Cochran

John K.

Branch

Kathryn A.

. 2005. “Social Learning Theory and Partner Violence: A Research Note.” Deviant Behavior 26:379–95.

162.

Shadish

William R

Thomas

Cook

Donald T

. 2002. Experimental and Quasi-experimental Designs for Generalized Causal Inference. Houghton: Mifflin and Company.

163.

Shamu

Simukai

Gevers

Anik

Pinky Mahlangu

Nwabisa Jama Shai

Chirwa

Esnat D.

Jewkes

Rachel K.

. 2016. “Prevalence and Risk Factors for Intimate Partner Violence Among Grade 8 Learners in Urban South Africa: Baseline Analysis from the Skhokho Supporting Success Cluster Randomised Controlled Trial.” International Health 8:18–26.

164.

Shen

April Chiung-Tao

Chiu

Marcus Yu-Lung

Gao

Jianxiu

. 2012. “Predictors of Dating Violence Among Chinese Adolescents: The Role of Gender-Role Beliefs and Justification of Violence.” Journal of Interpersonal Violence 27:1066–89.

165.

Sherer

Moshe

. 2009. “The Nature and Correlates of Dating Violence among Jewish and Arab Youths in Israel.” Journal of Family Violence 24:11–26.

166.

Shorey

Ryan C.

Fite

Paula J.

Torres

Elizabeth D.

Stuart

Gregory L.

Temple

Jeff R.

. 2019. “Bidirectional Associations Between Acceptability of Violence and Intimate Partner Violence from Adolescence to Young Adulthood.” Psychology of Violence 9:108–16.

167.

Shorey

Ryan C.

Wymbs

Brian

Torres

Liz

Cohen

Joseph R.

Fite

Paula J.

Temple

Jeff R.

. 2018. “Does Change in Perceptions of Peer Teen Dating Violence Predict Change in Teen Dating Violence Perpetration Over Time?” Aggressive Behavior 44:156–64.

168.

Simon

Thomas R.

Miller

Shari

Gorman-Smith

Deborah

Orpinas

Pamela

Sullivan

Terri

. 2010. “Physical Dating Violence Norms and Behavior Among Sixth-Grade Students from Four US Sites.” The Journal of Early Adolescence 30:395–409.

169.

Simons

Ronald L.

Lin

Kuei-Hsiu

Gordon

Leslie C.

. 1998. “Socialization in the Family of Origin and Male Dating Violence: A Prospective Study.” Journal of Marriage and the Family 60:467–78.

170.

Spencer

Chelsea M.

Toews

Michelle L.

Anders

Kristin M.

Emanuels

Sarah K.

. 2021. “Risk Markers for Physical Teen Dating Violence Perpetration: A Meta-Analysis.” Trauma, Violence, & Abuse 22:619–31.

171.

Stepteau-Watson

Desiree D

. 2003. “Dating Violence Among African American Adolescents: The Risk and Protective Factors.” PhD dissertation, Department of Social Work, University of Illinois at Chicago.

172.

Stults

Christopher B.

Javdani

Shabnam

Greenbaum

Chloe A.

Barton

Staci C.

Kapadia

Farzana

Halkitis

Perry N.

. 2015. “Intimate Partner Violence Perpetration and Victimization Among YMSM: The P18 Cohort Study.” Psychology of Sexual Orientation and Gender Diversity 21:152–8.

173.

Sullivan

Christopher J.

McGloin

Jean Marie

. 2014. “Looking Back to Move Forward: Some Thoughts on Measuring Crime and Delinquency Over the Past 50 Years.” Journal of Research in Crime and Delinquency 51:445–66.

174.

Sutherland

Edwin. H

. 1939. Principles of Criminology. 3rd ed. Philadelphia, PA: J.B. Lippincott.

175.

Taylor

Sarah A

. 2018. “Exploring Adolescent Dating Violence in Rural Communities: A Mixed Methods Study.” PhD dissertation, Department of Human Sciences, The University of Nebraska-Lincoln.

176.

Temple

Jeff R.

Shorey

Ryan C.

Fite

Paula

Stuart

Gregory L.

Vi Donna

. 2013. “Substance Use as a Longitudinal Predictor of the Perpetration of Teen Dating Violence.” Journal of Youth and Adolescence 42:596–606.

177.

Temple

Jeff R.

Shorey

Ryan C.

Tortolero

Susan R.

Wolfe

David A.

Stuart

Gregory L.

. 2013b. “Importance of Gender and Attitudes About Violence in the Relationship Between Exposure to Interparental Violence and the Perpetration of Teen Dating Violence.” Child Abuse & Neglect 37:343–52.

178.

Tharp

Andra T

. 2012. “Dating Matters™: The Next Generation of Teen Dating Violence Prevention.” Prevention Science 13:398–401.

179.

Trochim

William MK

. 1989. “Outcome Pattern Matching and Program Theory.” Evaluation and Program Planning 12:355–66.

180.

Turanovic

Jillian J.

Pratt

Travis C.

. 2021. “Meta-Analysis in Criminology and Criminal Justice: Challenging the Paradigm and Charting a New Path Forward.” Justice Evaluation Journal 4:21–47.

181.

Van Ouytsel

Joris

Ponnet

Koen

Walrave

Michel

. 2018. “Cyber Dating Abuse Victimization Among Secondary School Students from a Lifestyle-Routine Activities Theory Perspective.” Journal of Interpersonal Violence 33:2767–76.

182.

Viechtbauer

Wolfgang

. 2010. “Conducting Meta-Analyses in R with the Metafor Package.” Journal of Statistical Software 36:1–48.

183.

Walls

N. Eugene

Atteberry-Ash

Brittanie

Kattari

Shanna K.

Peitzmeier

Sarah

Kattari

Leo

Langenderfer-Magruder

Lisa

. 2019. “Gender Identity, Sexual Orientation, Mental Health, and Bullying as Predictors of Partner Violence in a Representative Sample of Youth.” Journal of Adolescent Health 64:86–92.

184.

Ward

Maranda C.

McMahon

Pamela M.

Ingram

Eben M.

. 2006. “Identifying Risk Factors Associated with Intimate Partner Violence Perpetration Among Youth.” Journal of Knowledge and Best Practices in Juvenile Justice and Psychology 1:5–10.

185.

Weir

Christopher J.

Butcher

Isabella

Assi

Valentina

Lewis

Stephanie C.

Murray

Gordon D.

Langhorne

Peter

Brady

Marian C.

. 2018. “Dealing with Missing Standard Deviation and Mean Values in Meta-Analysis of Continuous Outcomes: A Systematic Review.” BMC Medical Research Methodology 18:1–14.

186.

Wesche

Rose

Dickson-Gomez

Julia

. 2019. “Gender Attitudes, Sexual Risk, Intimate Partner Violence, and Coercive Sex Among Adolescent Gang Members.” Journal of Adolescent Health 64:648–56.

187.

Whitton

Sarah W.

Dyar

Christina

Mustanski

Brian

Newcomb

Michael E.

. 2019. “Intimate Partner Violence Experiences of Sexual and Gender Minority Adolescents and Young Adults Assigned Female at Birth.” Psychology of Women Quarterly 43:232–49.

188.

Williams

Tricia S.

Connolly

Jennifer

Pepler

Debra

Craig

Wendy

Laporte

Lise

. 2008. “Risk Models of Dating Aggression Across Different Adolescent Relationships: A Developmental Psychopathology Approach.” Journal of Consulting and Clinical Psychology 76:622.

189.

Wincentak

Katherine

Connolly

Jennifer

Card

Noel

. 2017. “Teen Dating Violence: A Meta-Analytic Review of Prevalence Rates.” Psychology of Violence 7:224–41.

190.

Wolfe

David A.

Crooks

Claire

Jaffe

Peter

Chiodo

Debbie

Hughes

Ray

Ellis

Wendy

Stitt

Larry

Donner

Allan

. 2009. “A School-Based Program to Prevent Adolescent Dating Violence: A Cluster Randomized Trial.” Archives of Pediatrics & Adolescent Medicine 163:692–9.

191.

Ybarra

Michele L.

Langhinrichsen-Rohling

Jennifer

. 2019. “Linkages Between Violence-Associated Attitudes and Psychological, Physical, and Sexual Dating Abuse Perpetration and Victimization Among Male and Female Adolescents.” Aggressive Behavior 45:622–34.

192.

Zych

Viejo

Vila

Farrington

D. P.

. 2021. “School Bullying and Dating Violence in Adolescents: A Systematic Review and Meta-Analysis.” Trauma, Violence, & Abuse 2:397–412.