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2010 37: 1086Criminal Justice and BehaviorLisa M. Jewell and J. Stephen Wormith

Targeting Male Batterers: A Meta-AnalysisVariables Associated With Attrition From Domestic Violence Treatment Programs

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1086

CRIMINAL JUSTICE AND BEHAVIOR, Vol. XX, No. X, Month 2007 1086-XXXDOI: © 2007 American Association for Correctional and Forensic Psychology

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CRIMINAL JUSTICE AND BEHAVIOR, Vol. 37 No. 10, October 2010 1086-1113DOI: 10.1177/0093854810376815© 2010 International Association for Correctional and Forensic Psychology

AUTHORS’ NOTE: This research was supported by a Canada Graduate Scholarship awarded to the first author from the Social Sciences and Humanities Research Council of Canada. We would like to thank Shannon Durand for her assistance with this study. Correspondence should be addressed to Lisa M. Jewell, Department of Psychology, University of Saskatchewan, 9 Campus Drive, Arts Building, Saskatoon, Saskatchewan, Canada, S7N 5A5; phone: 306-966-1773; e-mail: [email protected].

VARIABLES ASSOCIATED WITH ATTRITION FROM DOMESTIC VIOLENCE TREATMENT PROGRAMS TARGETING MALE BATTERERS

A Meta-Analysis

LISA M. JEWELLJ. STEPHEN WORMITHUniversity of Saskatchewan

Attrition from domestic violence treatment programs is of concern to correctional treatment providers because batterers who do not complete treatment are at higher risk for recidivism. This meta-analysis was conducted to determine the extent to which various demographic, violence-related, and intrapersonal variables predict attrition from domestic violence treatment programs for male batterers. A total of 30 studies that focused on in-program attrition and were published in English between 1985 and 2010 were included in the meta-analysis. Several variables distinguished treatment completers from dropouts, including employment, age, income, education, marital status, race, referral source, previous domestic violence offenses, criminal history, and alcohol and drug use. Furthermore, the theoretical orientation of the treatment program (i.e., feminist psychoeducational vs. cognitive-behavioral therapy) was found to be an important moderating variable. Findings suggest that the variables that predict attrition tend to be the same variables that predict recidivism and are discussed in relation to the responsivity principle.

Keywords: domestic violence; male batterers; treatment; attrition; meta-analysis; responsivity

Domestic violence (defined as physical, sexual, and psychological abuse directed toward one partner in a romantic relationship by the other) affects thousands of

Canadians and Americans each year (Davidson et al., 2001; Plichta, 2004). In 2004, approximately 7% of Canadian women and 6% of Canadian men living in common-law or marital relationships had been the victims of domestic violence at some point in the previ-ous 5 years (Statistics Canada, 2006). Comparably, the Home Office reported that 6% of women and 4% of men living in England and Wales had been the victims of domestic vio-lence in 2008-2009, with 38% of these individuals experiencing victimization more than once during the 1-year period (Walker, Flatley, Kershaw, & Moon, 2009). In the United States, the National Violence Against Women Survey (Tjaden & Thoennes, 2000) investi-gated the lifetime prevalence of domestic violence and found that 22.1% and 7.4% of American women and men, respectively, had been abused by an intimate partner at some

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Jewell, Wormith / DOMESTIC VIOLENCE TREATMENT ATTRITION 1087

point in their lives. In fact, domestic violence is a recognized worldwide problem; however, cultural norms surrounding abuse, discipline, and chastisement make it difficult to identify the prevalence and nature of abusive relationships in many cultures (Garcia-Moreno, Jansen, Ellsberg, Heise, & Watts, 2006).

Not surprisingly, numerous personal and societal costs are associated with domestic violence, including physical injuries, psychological distress, death, and the provision of health care, counseling, shelter, and criminal justice services (Greaves, Hankivsky, & Kingston-Riechers, 1995; Plichta, 2004). Offender treatment programs constitute one of the services offered by the criminal justice sector to address domestic violence and are designed to prevent individuals who have engaged in this form of violence from doing so in the future. The majority of treatment programs target male offenders because, even though intimate partner violence may be directed toward either female or male victims, women tend to suffer more severe injuries than men (Babcock, Green, & Robie, 2004; Tjaden & Thoennes, 2000). For instance, women are more likely to be the victims of mul-tiple assaults, suffer from the most severe forms of partner abuse, and experience negative emotional consequences (Statistics Canada, 2006). Consequently, many researchers have characterized domestic violence as a gendered form of violence (Anderson, 2005; Gill, 2006). In light of this perspective, the remainder of this article focuses explicitly on male batterer treatment programs.

Numerous studies have been carried out to explore the relationship between domestic violence treatment completion and recidivism. A recent meta-analysis conducted by Babcock et al. (2004) suggested that male batterer treatment programs were associated with a “small” reduction in domestic violence recidivism (d = .18). However, a limitation noted by Babcock et al. (2004) in interpreting their results is that many of the studies included in their meta-analysis (k = 22) were affected by program attrition or dropout, making it dif-ficult to estimate the effectiveness of treatment accurately. In general, domestic violence treatment programs are plagued by high attrition rates, with anywhere from 15% to 58% of individuals failing to complete treatment (Bennett, Stoops, Call, & Flett, 2007; Rondeau, Brodeur, Brochu, & Lemire, 2001). Wormith and Olver (2002) suggested that the client characteristics that cause offenders to be more at risk for failing to complete treatment may be the same characteristics that put offenders at risk for recidivism. In line with Wormith and Olver’s conjecture, numerous studies have reported that individuals who fail to com-plete domestic violence treatment are more likely than individuals who completed treat-ment to continue to abuse their partners (Babcock & Steiner, 1999; Bennett et al., 2007; Gordon & Moriarty, 2003). Furthermore, women are more likely to remain with men who are enrolled in domestic violence treatment programs (Gondolf, 1988). Thus, it is important to understand the offender characteristics associated with attrition to identify individuals who may be likely to drop out of treatment and to develop strategies to reduce treatment dropout.

Considerable research has been conducted to identify factors that predict domestic vio-lence treatment attrition. Typically, studies have focused on three broad categories of vari-ables: demographic variables, violence-related factors, and intrapersonal characteristics (Daly & Pelowski, 2000). The findings have been mixed with respect to all three of these categories. Thus, we conducted a meta-analysis to determine the extent to which various variables are associated with treatment completion and dropout.



1088 CRIMINAL JUSTICE AND BEHAVIOR

DEMOGRAPHIC VARIABLES

Demographic variables typically reflect “fixed” characteristics of an individual and are the most common variables that have been examined in relation to domestic violence treat-ment attrition (Daly & Pelowski, 2000). In fact, researchers exploring attrition are more likely to compare treatment completers and treatment dropouts on demographic variables than either violence-related or intrapersonal variables. Age, education, employment status, income, ethnicity, and marital status are among the variables most frequently investigated. Research has consistently shown that batterers who are older, employed, married, or Caucasian; who earn higher incomes; or who have more education tend to be more likely to complete treatment than batterers who are younger, unemployed, single, or a minority-group member; who earn lower incomes; or who have less education (Daly & Pelowski, 2000). However, the relative strength of each variable in differentiating between treatment completers and dropouts remains unknown. A meta-analysis will be helpful in determining which variables are most strongly related to attrition.

VIOLENCE-RELATED VARIABLES

The second category of variables that is associated with domestic violence treatment attrition relates to batterers’ experiences with violence, and here the evidence has been inconsistent. Frequently, researchers have assessed batterers’ criminal history in terms of any prior arrests or convictions or any domestic-violence-specific arrests or convictions (Cadsky, Hanson, Crawford, & Lalonde, 1996; Dalton, 2001). Treatment dropouts tend to have more prior arrests and/or convictions than treatment completers (Bowen & Gilchrist, 2006; Rondeau et al., 2001). However, some studies have found treatment completers to have more prior arrests and/or convictions (Dalton, 2001; Daly, Power, & Gondolf, 2001). Furthermore, some studies exploring batterers’ prior domestic violence arrests have found first-time offenders to be most likely to complete treatment (Babcock & Steiner, 1999; Bennett et al., 2007), whereas others have found offenders with greater histories of domes-tic violence to be more likely to complete treatment (Dalton, 2001). Research also has explored whether offenders who are court mandated to treatment are more likely to com-plete treatment than men who are not mandated, but findings are mixed with respect to whether treatment referral source is related to attrition (Dalton, 2001; Faulkner, Cogan, Nolder, & Shooter, 1991).

In addition, violence-related factors include batterers’ personal experiences with being victims of abuse and witnessing violence in their families of origin. Although batterers’ personal experiences of abuse have been examined infrequently, the evidence suggests that experiencing abuse is related to treatment dropout (Cadsky et al., 1996). The witnessing of violence between one’s parents has been investigated more often, and some research indi-cates that batterers who have witnessed violence as a child or adolescent also tend to be less likely to complete treatment (Cadsky et al., 1996; Chang & Saunders, 2002). However, other studies do not support this conclusion (Bowen & Gilchrist, 2006; Daly et al., 2001). In addition, the severity of abuse that men direct toward their partners has been associated with treatment attrition. The few studies exploring the severity of violence have generally found that men who inflict more severe forms of physical or psychological abuse are more likely to drop out of treatment than men who use less severe forms of violence (Carney,




Buttell, & Muldoon, 2006; Rooney & Hanson, 2001). Given the inconsistent results associ-ated with many of the violence-related variables, a meta-analysis will provide a more com-prehensive perspective on their ability to predict attrition by statistically establishing the overall direction and magnitude of these effects (Durlak & Lipsey, 1991).

INTRAPERSONAL CHARACTERISTICS

The last category of variables related to domestic violence treatment attrition consists of intrapersonal characteristics. The most common characteristics are alcohol and drug use, both of which have consistently been correlated with dropping out of treatment (Bowen & Gilchrist, 2006; Dalton, 2001; Faulkner et al., 1991; Hamberger, Lohr, & Gottlieb, 2000). However, as with the demographic variables, the relative strength of these variables for predicting treatment attrition is unknown.

A few studies have examined anger as a correlate of treatment completion. These studies tend to find that higher levels of anger are associated with treatment completion (Bowen & Gilchrist, 2006; Cadsky et al., 1996; Chang & Saunders, 2002). The relationship between depression and domestic violence treatment attrition also has been assessed, but these studies have not generally demonstrated a significant relationship (Faulkner et al., 1991; Saunders & Parker, 1989). Other intrapersonal variables known to be associated with treat-ment attrition in general, such as risk level, motivation, psychopathology, and therapeutic alliance, have not been studied frequently enough with male batterers to allow for com-mentary about their impact on treatment dropout (Daly & Pelowski, 2000; Wormith & Olver, 2002).

RELATIONSHIP BETWEEN PREDICTORS OF ATTRITION AND RECIDIVISM

It appears that many of the variables thought to be predictive of attrition from domestic violence treatment programs also are known predictors of domestic violence recidivism. For instance, factors such as younger age, lower socioeconomic status, unemployment, educa-tion, being unmarried, substance abuse, antisocial personality and conduct, violence in one’s family of origin, history of relationship conflict or abuse, prior severity of assaults, and previous arrests for partner abuse have all been identified as predictors of partner abuse recidivism (Babcock & Steiner, 1999; Cattaneo & Goodman, 2003; Hilton & Harris, 2005; Hilton, Harris, & Rice, 2001). Many of these variables are not unique to the prediction of domestic violence recidivism as they also predict general recidivism. For instance, a meta-analysis conducted by Gendreau, Little, and Goggin (1996) found that some of the most reliable predictors of male adult offender recidivism included age, race, criminal history, family factors, criminogenic need factors (i.e., antisocial personality, companions, interper-sonal conflict, substance abuse), and social achievement. Furthermore, it has been found that general offender risk assessments can be used to sufficiently predict partner abuse recidi-vism (Hanson & Wallace-Capretta, 2004; Hilton et al., 2001). Thus, it may be possible to predict domestic violence treatment attrition and recidivism using the same variables.

ADDITIONAL ISSUES RELATED TO ATTRITION TO BE CONSIDERED

A number of general issues must be considered when thinking about domestic violence treatment dropout beyond the types of variables that have been associated with attrition.




First, it is necessary to consider the type of attrition that is reported in individual studies. Specifically, attrition may occur at three different points in time: (a) after referral and before assessment (i.e., postreferral), (b) after assessment but before beginning treatment (i.e., postassessment), and (c) during treatment (i.e., in program; Gondolf & Foster, 1991).

Researchers studying domestic violence treatment programs have only rarely looked at attrition that occurs between referral and assessment (see Gondolf & Foster, 1991, as a notable exception). The majority of research focuses exclusively on in-program attrition (Carney et al., 2006; DeMaris, 1989; Faulkner et al., 1991; Rondeau et al., 2001), whereas a handful of studies examine the differences between treatment completers and batterers who drop out between assessment and treatment (Cadsky et al., 1996; Chang & Saunders, 2002; Hamberger et al., 2000; Rooney & Hanson, 2001; Saunders & Parker, 1989). Unfortunately, some researchers have grouped individuals who have dropped out of treat-ment after assessment with those who dropped out during treatment, which obscures any unique differences between these two types of dropouts (Babcock & Steiner, 1999; Bowen & Gilchrist, 2007; Daly et al., 2001). Others have failed to identify the type of attrition with which they were concerned (Beldin, 2008; Bennett et al., 2007; Taft, Murphy, Elliott, & Keaser, 2001). Therefore, the current study focused specifically on in-program attrition because this type of attrition is most often reported and allowed for a sufficient number of studies to be included in the meta-analysis. Studies that collapsed postassessment and in-program attrition and those that did not specify the nature of attrition were included in the meta-analysis, as their samples likely reflect the more common in-program attrition. However, the type of attrition assessed in each study was coded as a moderator variable, and additional analyses were conducted to determine whether unclear definitions of attri-tion affected the results of the meta-analysis. It should be noted that treatment attrition also may be imposed on participants who are asked to terminate a program by staff, returned to jail, or transferred to another facility. However, this kind of attrition has rarely been discussed in the domestic violence treatment literature and is not addressed in this article.

A second issue that must be considered with respect to attrition is the definition of treat-ment completion employed across studies. Currently, there is no agreed-on definition of attrition. Some studies used definitions that were very demanding (e.g., batterers who missed one or more treatment sessions were considered dropouts; Beldin, 2008), whereas others were much more forgiving (e.g., batterers who attended fewer than 67% of treatment ses-sions were considered treatment dropouts; DeHart, Kennerly, Burke, & Follingstad, 1999). In some cases, attrition also has been recorded as a continuous variable (e.g., Rooney & Hanson, 2001). The lack of standardized definitions across studies likely contributes to the wide range of attrition rates observed in the literature. In addition, inconsistent conceptu-alizations of attrition may limit the extent to which it is possible to draw conclusions about the variables associated with treatment dropout and may result in lower effect sizes in the meta-analysis.

Finally, in considering the results of the meta-analysis, it is necessary to keep in mind the characteristics of the treatment programs under investigation. In addition to employing different definitions of treatment attrition, there also are differences across interventions in terms of program length and treatment modality. Batterer intervention programs may range from 8 to 36 weeks of group treatment (Faulkner et al., 1991; Gondolf & Foster, 1991). Consequently, attrition may be related to program length because there is less opportunity for offenders attending 8-week programs to drop out than there is for offenders who attend




36-week programs. Therefore, program length was an important variable to consider in the meta-analysis.

With respect to treatment modality, there are two commonly accepted treatment modali-ties employed in batterer intervention programs: feminist psychoeducational treatments and cognitive-behavioral therapy. Feminist psychoeducational treatments tend to focus on the belief that domestic violence is a direct consequence of living in a patriarchal society that sanctions men’s use of power and control over women (Babcock et al., 2004). Consequently, men are challenged to change their attitudes and behaviors toward women that stem from their assumed positions of male privilege. In contrast, cognitive-behavior therapy is based on the notion that violence is a learned response that allows the batterer to accomplish a specific goal (Babcock et al., 2004). Thus, to change the repertoire of behaviors that batter-ers can draw on to lead nonviolent lives, cognitive-behavior therapy often involves teaching them communication, assertiveness, and social skills, as well as anger management tech-niques. In reality, most treatment programs integrate elements of the feminist psychoeduca-tional model with the principles of cognitive-behavioral therapy, which can make it difficult to distinguish these treatment modalities from each other. A recent meta-analysis conducted by Babcock et al. (2004) suggested there was no difference in terms of either modality’s effectiveness in reducing domestic violence recidivism. However, it is possible that treatment attrition may be related to the type of programming offered to the batterers.

On the basis of the literature reviewed, it seems that several variables may potentially be related to domestic violence treatment attrition. However, the many nonsignificant and inconsistent findings make it difficult to establish which variables are the best predictors of treatment attrition (Cadsky et al., 1996). Thus, this meta-analysis explored the extent to which various demographic, violence-related, and intrapersonal variables were able to dis-tinguish between treatment completers and dropouts. It was expected that demographic variables (i.e., being older, employed, married, or White) would be positively associated with treatment completion, whereas violence-related variables (i.e., having a history of prior domestic violence offenses or other crimes, being the victim of abuse, witnessing family violence, or being court-referred) and intrapersonal variables (i.e., using alcohol and drugs or being depressed) would be negatively related to treatment completion.

METHOD

STRATEGY FOR SEARCHING THE LITERATURE

A literature search was conducted using PsycINFO, Sociological Abstracts, and Criminal Justice Abstracts to identify studies examining correlates of domestic violence treatment attrition. The keywords domestic violence, intimate partner violence, partner abuse, and batterer were used and cross-referenced with the words treatment, intervention, attrition, and dropout. In addition, the reference sections from two review articles (Babcock et al., 2004; Daly & Pelowski, 2000) were examined for potential articles, as was the reference section of each article included in the meta-analysis. Furthermore, Social Science Citations was used to identify recent studies that cite the articles included in the meta-analysis. Finally, to avoid issues associated with publication bias, several researchers who have conducted research on domestic violence treatment attrition were contacted as a means of




identifying technical reports, conference presentations, and other studies published in the gray literature. On the basis of this search strategy, 39 published studies from 36 published journal articles and 2 unpublished dissertations were found (1 journal article contained 2 separate studies).

INCLUSION CRITERIA

Studies were required to meet numerous criteria to be included in the meta-analysis. First, the treatment program had to be directed primarily toward male batterers, and men had to compose the great majority (at least 90%) of the study’s participants. This resulted in one study being excluded (Tollefson, Gross, & Lundahl, 2008). In addition, three studies that treated male and female couples as the unit of analysis were also excluded (Brown & O’Leary, 2000; Brown, O’Leary, & Feldbau, 1997; Heyman, Brown, Feldbau-Kohn, & O’Leary, 1999). Second, in-program attrition had to serve as the study’s primary focus. Studies that collapsed postassessment and in-program attrition were included in the meta-analysis because individuals who dropped out postassessment generally composed a minority of these samples. Similarly, studies that did not explicitly specify the nature of attrition assessed tended to imply they were concerned with in-program attrition and also were included. However, studies investigating only postreferral or postassessment attrition were excluded (i.e., Gondolf & Forster, 1991). Third, studies had to employ a quasiexperi-mental or experimental design comparing treatment dropouts to treatment completers. However, correlational studies were included if they specifically related variables to treat-ment attrition and attrition was defined as a continuous variable. Fourth, studies had to be published between 1985 and April 2010 and reported in English. Fifth, studies had to assess treatment programs targeted toward men who had directed violence toward a female inti-mate partner. Sixth, the sample had to comprise adult participants. Finally, it had to be possible to calculate an effect size statistic (r) for relevant variables from data either included in the article or provided directly from authors. Four studies (DeHart et al., 1999; Gondolf, 2008; Huss & Ralston, 2008; Stalans & Seng, 2007) were excluded on the basis of this criterion. Ultimately, 30 separate studies (from 27 published articles and 2 unpub-lished dissertations) were included in the meta-analysis.

CODING PROCEDURES

Each of the remaining 30 studies was coded for variables related to study context, treat-ment and methodological characteristics, attrition, and sample characteristics. With respect to study context, the year of publication, country, type of publication (i.e., peer-reviewed journal article, unpublished dissertation, etc.), and first author’s professional affiliation (i.e., social work, psychology, etc.) were recorded. The methodological quality of the study was rated using the scientific method score developed by Sherman et al. (1997), where studies employing correlational designs are considered to be weak, quasi-experiments are considered to be moderate designs, and randomized experiments are considered to be strong designs. Various treatment characteristics were also coded, such as the theoretical orienta-tion of the treatment program (i.e., feminist psychoeducational or cognitive-behavioral), loca-tion of the treatment (i.e., institutional or community based), program format (i.e., group or individual therapy), referral source (i.e., court mandated or self-referral), and program




length (defined as the number of weeks in group therapy). In addition, each study’s defini-tion of treatment attrition was recorded. Finally, overall sample characteristics were coded for each study, including the overall sample size compared; the proportion of the sample that was male, employed, married, Caucasian, and a treatment completer or dropout; and the mean age and income of the sample.

To ensure the reliability of the coding, both the first author and a trained undergraduate research assistant coded the studies independently. To assess the reliability of the coding process, the percentage agreement was determined for each of the variables coded, as was the intraclass correlation coefficient (ICC). With the exception of three variables (i.e., over-all treatment completion rate, overall percentage employed, and overall percentage married), there was at least 80% agreement between raters on all variables coded. Other researchers in the field (Dowden, Antonowicz, & Andrews, 2003; Parhar, Wormith, Derkzen, & Beauregard, 2008) have used 80% agreement among raters as an indicator of satisfactory interrater reliability. Furthermore, all variables (with the exception of theoretical orienta-tion and program length) obtained absolute ICCs ranging from .90 to 1.0. The absolute ICCs for theoretical orientation and program length were .83 and .79, respectively. In gen-eral, the raters consistently rated each variable. Slight discrepancies were observed in the calculation of overall treatment completion rates, percentage employed, and percentage married because of the misleading way in which these statistics were presented in the original articles. For example, some studies used a larger sample to calculate the overall demographic characteristics than they did to carry out analyses related to treatment attri-tion, but this was not always clearly stated. For the few instances in which inconsistent ratings occurred, each discrepancy was reviewed jointly by the coders, and consensus about the correct rating was determined on the basis of the operational definition employed.

EFFECT SIZE CODING

In addition to coding various study procedures, effect sizes were coded for comparisons between treatment completers and dropouts for each of the following variables (if present in a given study): age, education, employment, income, marital status, race, referral source, criminal history, first-time domestic violence offender, personal experience of abuse, pres-ence of violence in the family of origin, severity of psychological abuse inflicted, severity of physical abuse inflicted, anger, alcohol use, drug use, and depression. Where possible, the effect size was calculated from raw data provided in the article (e.g., proportions, means and standard deviations, correlations) to allow for more accurate effect sizes (Durlak & Lipsey, 1991). When these statistics were not available, the effect size was estimated from the test statistics that were reported (e.g., c2, t test). In addition, the direction of the results (i.e., whether the data favored treatment completers or treatment dropouts) was coded. All raw data and test statistics used to estimate effect sizes were entered in Comprehensive Meta-Analysis Version 2.0 (Borenstein, Hedges, Higgins, & Rothstein, 2005), and this program was then used to calculate the effect sizes for each study as well as the summary effect sizes.

EFFECT SIZE INDICATOR AND META-ANALYTIC STRATEGY

A correlation coefficient (r) was used as the effect size indicator to allow for a common effect size to be calculated across variables because some variables were treated dichotomously by some researchers and continuously by others (Durlak & Lipsey, 1991). The correlation




coefficient also has the advantage of being a widely used and understood statistic that is relatively easy to interpret (Gendreau & Smith, 2007; Wilson, 2001). Effect sizes were cal-culated using the formulas provided by Borenstein, Hedges, Higgins, and Rothstein (2009).

A random (rather than a fixed) effects model was employed to analyze the data, as this model provides a more conservative estimate of effect size and is thought to be appropriate for most criminal justice meta-analyses (Wilson, 2001). Because of presumed differences in effect size from one setting or jurisdiction to another, random effects models attempt to esti-mate a true population effect size and assume that the true effect size varies from study to study (Borenstein et al., 2009). In other words, random effects models assume that some por-tion of the study-level variability is unexplained by the study features included in the effect size statistic, and the goal of this model is to estimate the mean distribution of these effects (Borenstein et al., 2009; Wilson, 2001). In contrast, fixed effects models are based on the assumption that one true effect underlies all of the studies included in a meta analysis and estimates derived using this model are applicable only to the population of studies included in the meta-analysis (Borenstein et al., 2009; Hanson, Bourgon, Helmus, & Hodgson, 2009).

A number of additional steps were taken to ensure the meta-analytic results obtained would be accurate and useful. To establish the boundaries of an effect size, 95% confidence intervals (CIs) are reported for the grand mean effect size. An effect size can be considered to be statistically significant at the p < .05 level if the CI does not contain zero (Babcock et al., 2004). Furthermore, given that effect sizes based on small samples tend to be less precise than those based on large samples, all analyses were conducted using weighted effect sizes (Wilson, 2001). In addition, the Q statistic was examined for each analysis to determine whether the mean effect size reflects effects that are consistent across studies (i.e., are statistically homogenous; Borenstein et al., 2009; Wilson, 2001). Significant Q statistics indicate that there is more variability in the effect sizes than can be accounted for by sampling error and that they may not reflect a common population (Durlak & Lipsey, 1991; Wilson, 2001). As a result, further explorations were conducted when significant Q statis-tics were obtained to determine if any moderators could account for the observed hetero-geneity. Furthermore, outliers (defined as an effect size greater than three standard deviations from the mean) were removed from the meta-analytic calculations because the presence of outliers distorts the grand mean effect size. Finally, the effect sizes that are reported in this meta-analysis reflect independent comparisons. That is, several meta-analytic calculations were computed in the present study, and each calculation employs only one effect size from each study (Borenstein et al., 2009).

RESULTS

SUMMARY INFORMATION ABOUT THE STUDIES

Table 1 describes the overall characteristics of the 30 studies that were included in the meta-analysis. Most studies were published between 2000 and 2010 and were conducted in the United States. Treatment programs were equally as likely to employ feminist psych-oeducational models and cognitive-behavioral therapy, and nearly all programs provided group treatment in a community setting. Most studies focused solely on in-program attri-tion (70%), and 57% used samples consisting of a combination of participants who were court referred or self-referred to the program. Furthermore, 90% of the studies employed a




TABLE 1: Overall Characteristics of the Studies Included in the Meta-Analysis

Characteristic Number of Studies Proportion of Studies

Type of publicationJournal article 28 .93Unpublished dissertation 2 .07

Date of publication1989–1999 7 .232000–2010 17 .57

Country of originUnited States 25 .83Canada 4 .13United Kingdom 1 .03

Professional affiliation of first authorPsychology 12 .40Social work 10 .33Medicine 2 .07Sociology 1 .03Criminal justice 1 .03Nursing 1 .03Public health 1 .03Women’s and gender studies 1 .03Unknown 1 .03

Theoretical orientation of treatmentFeminist psychoeducational 12 .40Cognitive behavioral 12 .40Other or unknown 6 .20

Treatment formatGroup treatment 29 .97Unknown 1 .03

Location of treatmentCommunity setting 28 .93Unknown 2 .07

Type of attrition focused onIn-program attrition only 21 .70In-program and postassessment attrition collapsed 6 .20Unspecified 3 .10

Study designCorrelational 3 .10Nonequivalent comparison group 27 .90

Referral sourceCourt mandated 12 .40Combination of court mandated and self-referral 17 .57Unknown 1 .03

Program length (in weeks of group treatment)16 or fewer weeks 14 .4717 or more weeks 14 .47Unspecified 2 .07

Note. k = 27.

nonequivalent comparison group quasiexperimental design; however, three studies (Catlett, Toews, & Walilko, 2010; Daly et al., 2001; Taft, Murphy, Elliott, & Keaser, 2001) employed correlational designs. Programs ranged in length from 8 to 36 group sessions, and program completion rates ranged from 22% to 78%. Sample sizes for the studies varied from N = 34 to N = 3,460, and a total of 11,665 participants are reflected in the meta-analytic results. Table 2 provides more specific sample and program characteristics of each coded study.



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asse

ssm

ent

and

in-p

rogr

am a

ttriti

on

Com

plet

ed 2

4 or

mor

e se

ssio

ns29

.926

wee

kly

sess

ions

; 6 m

onth

ly

sess

ions

; FP

E

Bel

din,

200

8N

= 1

84C

ompl

eted

all

trea

tmen

t se

ssio

ns55

.424

or

36 w

eekl

y se

ssio

ns; u

nkno

wn

100%

mal

eM

= 3

1.7

age

81%

em

ploy

ed67

.9%

Cau

casi

an15

.2%

mar

ried

Cou

rt m

anda

ted

Uns

peci

fied

type

of

attr

ition

Ben

nett

et a

l., 2

007

N =

549

Uns

peci

fied

75.2

Unk

now

n; u

nkno

wn

100%

mal

e74

.9%

em

ploy

ed34

.8%

Cau

casi

an41

.9%

mar

ried

Cou

rt m

anda

ted

Uns

peci

fied

type

of

attr

ition

Bow

en &

Gilc

hris

t, 20

06N

= 1

2010

0% m

ale

M =

34.

95 a

ge84

.8%

Cau

casi

an24

% m

arrie

dC

ourt

man

date

dB

oth

post

asse

ssm

ent

and

in-p

rogr

am a

ttriti

on

Com

plet

ed a

t le

ast

16 o

f 24

gro

up

sess

ions

67.5

24 g

roup

ses

sion

s, 1

indu

ctio

n se

ssio

n, 5

mon

thly

follo

w-u

p; F

PE

(con

tiune

d)



1097

TAB

LE

2:

(co

nti

nu

ed)

Stu

dy

Sam

ple

D

efin

ition

of

Pro

gram

Com

plet

ion

Pro

gram

Com

plet

ion

Rat

e (%

)P

rogr

am L

engt

h an

d T

heor

etic

al

Orie

ntat

ion

But

tell

& C

arne

y, 2

002

N =

137

100%

mal

eM

= 3

4.0

age

89%

em

ploy

ed49

.6%

Cau

casi

an39

.4%

mar

ried

Cou

rt m

anda

ted

In-p

rogr

am a

ttriti

on

Com

plet

ed a

ll se

ssio

ns56

.216

wee

kly

sess

ions

, in

clud

ing

2 or

ient

atio

n, 1

2 ps

ycho

educ

atio

nal,

and

2 te

rmin

atio

n cl

asse

s; C

BT

But

tell

& C

arne

y, 2

008

N =

1,7

0210

0% m

ale

M =

34.

8 ag

e91

.8%

em

ploy

ed49

.1%

Cau

casi

an59

.6%

mar

ried

Cou

rt m

anda

ted

In-p

rogr

am a

ttriti

on

Com

plet

ed a

ll se

ssio

ns49

.926

wee

kly

sess

ions

, in

clud

ing

2 or

ient

atio

n, 2

0 ps

ycho

educ

atio

nal,

and

4 te

rmin

atio

n cl

asse

s; C

BT

But

tell

& P

ike,

200

2N

= 8

3C

ompl

eted

all

sess

ions

67.0

12 w

eekl

y se

ssio

ns; C

BT

100%

mal

eM

= 3

1.0

age

42.2

% C

auca

sian

40.9

% m

arrie

dC

ourt

man

date

dIn

-pro

gram

attr

ition

Cad

sky

et a

l., 1

996

N =

218

100%

mal

eC

ourt

man

date

d an

d se

lf-re

ferr

alIn

-pro

gram

attr

ition

Com

plet

e at

leas

t 8

of 1

0 se

ssio

ns61

.010

wee

kly

sess

ions

; CB

T



1098

Car

ney

et a

l., 2

006

N =

114

Com

plet

ed a

ll 16

ses

sion

s49

.116

wee

kly

sess

ions

; CB

T10

0% m

ale

M =

32.

0 ag

e46

.0%

Cau

casi

an43

.2%

mar

ried

Cou

rt m

anda

ted

In-p

rogr

am a

ttriti

onC

atle

tt et

al.,

201

0N

= 1

5410

0% m

ale

M =

34.

4 ag

e38

.3%

Cau

casi

anC

ourt

man

date

dIn

-pro

gram

attr

ition

Com

plet

ed a

t le

ast

16 o

f 20

se

ssio

ns54

.5In

take

inte

rvie

w; b

imon

thly

as

sess

men

t se

ssio

ns u

ntil

asse

ssm

ent

com

plet

e; 1

6 gr

oup

sess

ions

; FP

E

Cha

ng &

Sau

nder

s, 2

002

N =

178

100%

mal

eM

= 3

2.0

age

77.0

% C

auca

sian

8.0%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

Com

plet

ed a

t le

ast

16 o

f 20

se

ssio

ns62

.020

wee

kly

sess

ions

; FP

E a

nd C

BT

In-p

rogr

am a

ttriti

onD

alto

n, 2

001

N =

85

Com

plet

ed 1

4 or

mor

e se

ssio

ns70

.616

wee

kly

sess

ions

; FP

E92

% m

ale

M =

32.

0 ag

e84

% e

mpl

oyed

54%

Cau

casi

an44

% m

arrie

dC

ourt

man

date

d an

d se

lf-re

ferr

alIn

-pro

gram

attr

ition

(con

tiune

d)



Car

ney

et a

l., 2

006

N =

114

Com

plet

ed a

ll 16

ses

sion

s49

.116

wee

kly

sess

ions

; CB

T10

0% m

ale

M =

32.

0 ag

e46

.0%

Cau

casi

an43

.2%

mar

ried

Cou

rt m

anda

ted

In-p

rogr

am a

ttriti

onC

atle

tt et

al.,

201

0N

= 1

5410

0% m

ale

M =

34.

4 ag

e38

.3%

Cau

casi

anC

ourt

man

date

dIn

-pro

gram

attr

ition

Com

plet

ed a

t le

ast

16 o

f 20

se

ssio

ns54

.5In

take

inte

rvie

w; b

imon

thly

as

sess

men

t se

ssio

ns u

ntil

asse

ssm

ent

com

plet

e; 1

6 gr

oup

sess

ions

; FP

E

Cha

ng &

Sau

nder

s, 2

002

N =

178

100%

mal

eM

= 3

2.0

age

77.0

% C

auca

sian

8.0%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

Com

plet

ed a

t le

ast

16 o

f 20

se

ssio

ns62

.020

wee

kly

sess

ions

; FP

E a

nd C

BT

In-p

rogr

am a

ttriti

onD

alto

n, 2

001

N =

85

Com

plet

ed 1

4 or

mor

e se

ssio

ns70

.616

wee

kly

sess

ions

; FP

E92

% m

ale

M =

32.

0 ag

e84

% e

mpl

oyed

54%

Cau

casi

an44

% m

arrie

dC

ourt

man

date

d an

d se

lf-re

ferr

alIn

-pro

gram

attr

ition

(con

tiune

d)

1099

TAB

LE

2:

(co

nti

nu

ed)

Stu

dy

Sam

ple

D

efin

ition

of

Pro

gram

Com

plet

ion

Pro

gram

Com

plet

ion

Rat

e (%

)P

rogr

am L

engt

h an

d T

heor

etic

al

Orie

ntat

ion

Dal

y et

al.,

200

1N

= 2

2010

0% m

ale

M =

31.

8 ag

e82

.2%

em

ploy

ed32

.1%

Cau

casi

anC

ourt

man

date

d an

d se

lf-re

ferr

alB

oth

post

asse

ssm

ent

and

in-p

rogr

am a

ttriti

on

Com

plet

ed 1

8 or

mor

e se

ssio

ns

and

two

eval

uatio

n se

ssio

ns48

.018

wee

kly

sess

ions

; 1 in

divi

dual

ev

alua

tion,

1 g

roup

orie

ntat

ion;

F

PE

DeM

aris

, 19

89N

= 2

9510

0% m

ale

M =

32.

0 ag

e74

% e

mpl

oyed

24%

Cau

casi

an71

% m

arrie

dC

ourt

man

date

dIn

-pro

gram

attr

ition

Com

plet

ed a

t le

ast

11 o

f 12

se

ssio

ns72

.012

gro

up s

essi

ons;

FP

E

Dup

lant

is,

Rom

ans,

& B

ear,

2006

N =

313

100%

mal

eM

= 3

3.0

age

66.1

% C

auca

sian

Cou

rt m

anda

ted

and

self-

refe

rral

Bot

h po

stas

sess

men

t an

d in

-pro

gram

attr

ition

N =

34

Uns

peci

fied

Unk

now

n24

gro

up,

2 dr

ug a

nd a

lcoh

ol

educ

atio

n, 1

con

sulta

tion,

and

2

inta

ke s

essi

ons;

FP

E a

nd C

BT

Faul

kner

et

al.,

1991

Uns

peci

fied

52.9

8 se

ssio

ns; C

BT

100%

mal

eM

= 3

1.5

age

52.9

% e

mpl

oyed



1100

55.9

% C

auca

sian

35.3

% m

arrie

dC

ourt

man

date

d an

d se

lf-re

ferr

alIn

-pro

gram

attr

ition

Ger

lock

, 20

01N

= 6

110

0% m

ale

M =

38.

8 ag

e54

.8%

Cau

casi

anC

ourt

man

date

dIn

-pro

gram

attr

ition

Bat

tere

rs a

re s

atis

fact

orily

re

habi

litat

ed37

.726

wee

kly

sess

ions

; 4 in

trod

uctio

n m

eetin

gs; F

PE

Gor

don

& M

oria

rty,

200

3N

= 1

32C

ompl

eted

all

sess

ions

61.4

20 o

r 24

wee

kly

sess

ions

; FP

E10

0% m

ale

M =

34.

8 ag

e80

% e

mpl

oyed

58%

Cau

casi

an56

% m

arrie

dC

ourt

man

date

dIn

-pro

gram

attr

ition

Ham

berg

er &

Has

tings

, 19

89N

= 1

5610

0% m

ale

M =

30.

9 ag

e68

.6%

em

ploy

ed81

.4%

Cau

casi

an41

.7%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

Bot

h po

stas

sess

men

t an

d in

-pro

gram

attr

ition

Com

plet

ed a

ll ev

alua

tion

and

inte

rven

tion

sess

ions

56.4

12 g

roup

, 3

indi

vidu

al a

sses

smen

t, an

d 1

post

inte

rven

tion

eval

uatio

n se

ssio

n; C

BT

Ham

berg

er e

t al

., 20

00N

= 4

8210

0% m

ale

Com

plet

ed a

ll se

ssio

ns57

.112

wee

kly

and

3 in

divi

dual

se

ssio

ns; C

BT

(con

tiune

d)



TAB

LE

2:

(co

nti

nu

ed)

Stu

dy

Sam

ple

D

efin

ition

of

Pro

gram

Com

plet

ion

Pro

gram

Com

plet

ion

Rat

e (%

)P

rogr

am L

engt

h an

d T

heor

etic

al

Orie

ntat

ion

M =

31.

5 ag

e77

.6%

em

ploy

ed69

.1%

Cau

casi

an32

.6%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

In-p

rogr

am a

ttriti

onM

cClo

skey

, S

itake

r, G

rigsb

y, &

M

allo

y, 2

003

N =

216

100%

mal

eM

= 3

5.0

age

76%

em

ploy

ed69

% C

auca

sian

35%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

Bot

h po

stas

sess

men

t an

d in

-pro

gram

attr

ition

Com

plet

ed a

ll se

ssio

ns22

.216

or

26 w

eekl

y se

ssio

ns; 3

ps

ycho

educ

atio

nal c

lass

es; 1

in

take

inte

rvie

w; F

PE

Ron

deau

et

al.,

2001

N =

286

Com

plet

ed a

ll se

ssio

ns42

.314

to

25 g

roup

ses

sion

s; u

nkno

wn

100%

mal

e62

.6%

em

ploy

ed34

.3%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

In-p

rogr

am a

ttriti

onR

oone

y &

Han

son,

200

1N

= 3

0610

0% m

ale

M =

35.

5 ag

e77

% e

mpl

oyed

Cou

rt m

anda

ted

and

self-

refe

rral

In-p

rogr

am a

ttriti

on

Uns

peci

fied

50.7

12 t

o 25

wee

kly

sess

ions

; mul

tiple

pr

ogra

ms

1101 at University of Bucharest on August 5, 2014cjb.sagepub.comDownloaded from


1102

Rot

hman

, G

upta

, P

avlo

s, D

ang,

&

Cou

tinho

, 20

07N

= 3

,460

100%

mal

eM

= 3

3.3

age

55.4

% C

auca

sian

Uns

peci

fied

refe

rral

sou

rce

Uns

peci

fied

type

of

attr

ition

N =

196

Com

plet

ed a

ll re

quire

d se

ssio

ns,

paid

all

fees

, an

d fu

lfille

d pr

ogra

m r

equi

rem

ents

51.8

Unk

now

n; u

nkno

wn

Sar

tin,

2004

Com

plet

ed a

ll se

ssio

ns59

.224

wee

kly

sess

ions

; FP

E10

0% m

ale

M =

35.

0 ag

e89

.2%

em

ploy

ed80

.1%

Cau

casi

an34

.5%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

In-p

rogr

am a

ttriti

onS

aund

ers

& P

arke

r, 19

89—

Stu

dy 1

N =

138

Uns

peci

fied

Unk

now

n12

gro

up s

essi

ons;

CB

T10

0% m

ale

M =

29.

9 ag

e70

% e

mpl

oyed

74.8

% C

auca

sian

27.9

% m

arrie

dC

ourt

man

date

d an

d se

lf-re

ferr

alIn

-pro

gram

attr

ition

Sau

nder

s &

Par

ker,

1989

—S

tudy

2N

= 1

33U

nspe

cifie

d78

.212

gro

up s

essi

ons;

CB

T10

0% m

ale

M =

30.

8 ag

e78

% e

mpl

oyed

88.2

% C

auca

sian

33.1

% m

arrie

dC

ourt

man

date

d an

d se

lf-re

ferr

alIn

-pro

gram

attr

ition

(con

tiune

d)



1103

TAB

LE

2:

(co

nti

nu

ed)

Stu

dy

Sam

ple

D

efin

ition

of

Pro

gram

Com

plet

ion

Pro

gram

Com

plet

ion

Rat

e (%

)P

rogr

am L

engt

h an

d T

heor

etic

al

Orie

ntat

ion

Sco

tt, 2

004

N =

308

100%

mal

eM

= 3

5.4

age

65%

em

ploy

ed83

% C

auca

sian

Cou

rt m

anda

ted

and

self-

refe

rral

In-p

rogr

am a

ttriti

on

Com

plet

ed a

ll se

ssio

ns38

.617

wee

kly

sess

ions

; 1 in

take

se

ssio

n; F

PE

Taft,

Mur

phy,

Elli

ott,

& K

ease

r, 20

01N

= 1

0110

0% m

ale

M =

33.

6 ag

e80

% e

mpl

oyed

60.4

% C

auca

sian

33%

mar

ried

Cou

rt m

anda

ted

and

self-

refe

rral

In-p

rogr

am a

ttriti

on

Com

plet

ed a

t le

ast

75%

of

sess

ions

69.3

16 w

eekl

y se

ssio

ns; C

BT

Taft,

Mur

phy,

Elli

ott,

& M

orre

l, 20

01N

= 1

8910

0% m

ale

M =

34.

2 ag

e60

.0%

Cau

casi

an30

.0%

mar

ried

Cou

rt m

anda

ted

and

se

lf-re

ferr

alIn

-pro

gram

attr

ition

Com

plet

ed a

t le

ast

75%

of

sess

ions

(12

of

16 s

essi

ons)

76.7

16 w

eekl

y se

ssio

ns; C

BT

Not

e. k

= 3

0. F

PE

= fe

min

ist

psyc

hoed

ucat

iona

l tre

atm

ent;

CB

T =

cog

nitiv

e-be

havi

oral

the

rapy

.




DEMOGRAPHIC VARIABLES

Table 3 summarizes the grand mean effect size obtained for each variable assessed using meta-analytic techniques. Overall, the demographic variables were better able to distin-guish between treatment completers and treatment dropouts than the violence-related and intrapersonal variables. In general, individuals who were employed were 20% more likely to complete treatment than individuals who were unemployed, batterers who were older were 16% more likely to complete treatment than those who were younger, and men who had higher incomes were 13% more likely to complete treatment than those with lower incomes. Modest effect sizes also were observed for the relationship between attrition and education (r = .08), marital status (r = .08), and race (r = .09). Examinations of the Q sta-tistic for each of the demographic variables revealed that several of the variables (i.e., employment, age, and education) demonstrated significant heterogeneity. As such, further analyses were conducted to determine whether any of the moderator variables could account for the observed variability.

Theoretical orientation of the treatment program moderated the relationship observed between age and treatment completion (after one outlier was removed). Specifically, com-pared to individuals who were younger, older individuals were significantly more likely to complete cognitive-behavioral programs (r = .18, 95% CI = .12 to .23, k = 10) or unspeci-fied programs (r = .13, 95% CI = .01 to .24, k = 4) than feminist psychoeducational pro-grams (r = .02, 95% CI = –.10 to .13, k = 7). In addition, there was a significant difference with respect to program length wherein men who were older were more likely to complete programs that were of a shorter duration (i.e., 16 or fewer weeks; r = .18, 95% CI = .13 to .24, k = 11) than men attending longer programs (r = .08, 95% CI = .02 to .16, k = 9). Theoretical orientation also moderated the relationship between education and attrition. Batterers who had less education were more likely to drop out of feminist psychoeduca-tional treatment programs (r = .14, 95% CI = .08 to .20, k = 6) than cognitive behavioral programs (r = .10, 95% CI = .05 to .15, k = 11) or unspecified programs (r = .01, 95% CI = –.05 to .06, k = 6) compared to men who had more education. In addition, significantly higher rates of treatment completion among men who had more education were observed within studies that employed samples consisting of both postassessment and in-program dropouts (r = .17, 95% CI = .09 to .24, k = 3) than studies that examined only in-program attrition (r = .08, 95% CI = .03 to .12, k = 17) or did not specify the nature of attrition (r = .03, 95% CI = –.03 to .11, k = 3). None of the potential moderator variables (i.e., type of attrition, referral source, theoretical orientation, program length, sample size, country, pro-fessional affiliation, and publication type) explained the heterogeneity observed with respect to employment.

VIOLENCE-RELATED VARIABLES

Of all the violence-related variables, referral source was most strongly associated with attrition. Batterers who were court mandated were 16% more likely than batterers who were not court mandated to complete treatment. Furthermore, being a first-time domestic violence offender and having a previous criminal history were both modestly associated with completion. Specifically, men attending treatment after their first domestic violence offense were approximately 14% more likely to complete treatment than batterers who had



1105

TAB

LE

3:

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ect

Siz

es o

f Var

iab

les

Rel

ated

to

Att

riti

on

Ave

rage

Cor

rela

tions

and

95%

Con

fiden

ce I

nter

vals

(C

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Ran

dom

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cts

Mod

el

Var

iabl

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orre

latio

n (r

)95

% C

IQ

Sta

tistic

kR

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of

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(r)

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tion

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ogra

phic

var

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mpl

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ent

.20

.15

to .

2441

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02 t

o .5

14,

419

TC

mor

e lik

ely

to b

e em

ploy

edA

ge .

16.0

8 to

.24

370.

70*

22–.

42 t

o .8

38,

621

TC

mor

e lik

ely

to b

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der

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me

.13

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169.

7212

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3,64

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15–.

14 t

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697

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ely

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1327

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94,

879

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of

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16 t

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0537

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9–.

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62,

370

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61,

530

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of

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2,58

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t

Sev

erity

of

phys

ical

abu

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.31

3,17

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03–.

18 t

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7–.

24 t

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784

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igni

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= n

umbe

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eter

; TD

= t

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men

t dr

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dom

estic

vio

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previously been arrested or convicted for domestic violence. Conversely, batterers who had a criminal history (i.e., they had been arrested or convicted for crimes separate from the domestic violence incident that placed them in treatment) were 10% more likely to drop out of treatment.

The effect sizes associated with referral source and having a criminal history were both significantly heterogeneous. Again, the theoretical orientation of the treatment program moderated the relationship between referral source and attrition, with men who were court mandated to treatment being more likely to complete feminist psychoeducational treatment programming (r = .31, 95% CI = .23 to .39, k = 3) than cognitive-behavioral programs (r = .10, 95% CI = .01 to .19, k = 6) or unspecified programs (r = .03, 95% CI = –.12 to .18, k = 2) compared to men who were not court referred. None of the potential moderator variables (i.e., type of attrition, referral source, theoretical orientation, program length, sample size, country, professional affiliation, and publication type) explained the heteroge-neity observed with respect to criminal history.

The effect sizes observed for all other violence-related variables (i.e., personal experi-ence of abuse, witnessing violence in one’s family of origin, and the severity of the physi-cal and psychological abuse inflicted) were nonsignificant, indicating that these variables are not significant predictors of attrition. The nonsignificant findings may be because of the relatively few studies included in the analyses (k = 10 for severity of physical abuse, k = 9 for family of origin violence, and k = 7 for personal experience of abuse and severity of psychological abuse).

INTRAPERSONAL VARIABLES

Both alcohol use and drug use were associated with treatment attrition, with treatment dropouts suffering from alcohol and drug problems being 12% and 10%, respectively, less likely to complete treatment than batterers not contending with these issues. A Q test indi-cated that the effect size calculated for alcohol use reflects a homogenous population of studies but that drug use does not. Additional analyses were conducted for drug use, and sample size was found to moderate its relationship with attrition. Specifically, studies that had samples of 200 or fewer participants reported greater differences between the treatment completion rates of drug users versus nondrug users (r = –.18, 95% CI = –.26 to –.11, k = 8) than samples consisting of more than 200 participants (r = –.06, 95% CI = –.10 to –.02, k = 4). The other two intrapersonal variables (i.e., anger and depression) did not signifi-cantly differentiate between treatment completers and dropouts.

DISCUSSION

The results from this meta-analysis suggest that numerous variables are related to treat-ment completion. The strongest predictors associated with treatment completion were employ-ment, age, and referral source (rs ≥ .16), wherein individuals who were employed, older, and court mandated were more likely to remain in treatment than were persons who were unem-ployed, younger, or not court mandated. Other correlates of attrition associated with modest differences between treatment completers and dropouts were previous domestic violence offenses, income, drug use, and criminal history (rs ≥ .10). Specifically, individuals who




were attending domestic violence treatment after their first partner assault offense or who had higher incomes were significantly more likely to complete treatment. In contrast, indi-viduals who had a criminal history prior to attending domestic violence treatment or had drug problems were more likely to drop out of treatment. Men who had more education, were married, were Caucasian, or did not have problems with alcohol also completed treat-ment at a somewhat higher rate than persons who were less educated, were unmarried, were members of a minority group, or had problems with alcohol (rs ≥ .08). Variables such as being a victim of abuse, witnessing violence within one’s family of origin, severity of physi-cal and psychological abuse, depression, and anger did not distinguish between treatment completers and dropouts.

The results obtained from the meta-analysis suggest the same variables that have been found to predict domestic violence recidivism also predict attrition from domestic violence treatment programs. These findings support Wormith and Olver’s (2002) suggestion that attrition and recidivism are predicted by the same variables. However, some variables are clearly better predictors than others, indicating that certain variables may be uniquely suited to predicting domestic violence attrition. A particularly interesting finding arising from the meta-analysis is that the theoretical orientation of the treatment program served as an important moderator variable that explained some of the heterogeneity in effect sizes for age, education, and referral source. Specifically, men who were more educated and court mandated were more likely to complete feminist psychoeducational programs than were men who were not as educated or court mandated, whereas batterers who were older completed cognitive-behavioral programs more readily than younger men. Although the theoretical orientation of a program may not have a significant impact on domestic violence recidivism (Babcock et al., 2004), our results suggest it may affect attrition.

The pattern of results obtained from the meta-analysis also fits nicely into the risk, need, and responsivity framework developed by Andrews, Bonta, and Hoge (1990). Briefly, this theory is based on the notions that treatment intensity should be matched to offenders’ risk level, treatment should focus primarily on criminogenic needs, and treatment should be deliv-ered in a way that best matches an offender’s ability and learning style (Andrews et al., 1990). In fact, many of the variables found to be related to domestic violence treatment attrition reflect the criminogenic needs (i.e., employment, drug and alcohol use, lack of familial ties) that put offenders at risk for recidivism. Of particular relevance to domestic violence treatment dropout, however, is the responsivity principle, which assumes that treatment strategies most likely to produce change should be selected and tailored to match each offender’s learning styles, motivation, aptitude, and ability (Andrews, 2000).

Indeed, many of the variables associated with treatment completion and attrition reflect the individual responsivity characteristics that should be taken into consideration when matching offenders to treatment, such as age, race, education, employment, income, and various personality characteristics (including depression and mental illness; Kennedy, 2000). The findings that individuals who are older, are employed, are married, earn higher incomes, and are first-time domestic violence offenders are more likely to remain in treat-ment may reflect that these individuals have more stable lifestyles, are more socially inte-grated, and consequently have more to lose (both economically and socially) by not completing treatment compared to those who have more unstable lifestyles (Babcock & Steiner, 1999). Thus, lifestyle instability may be an important responsivity issue to consider when treating male batterers, and it has implications for how treatment providers can tailor interventions




to increase the likelihood that offenders who are unemployed, are unmarried, and have greater criminal histories remain in treatment (Cadsky et al., 1996). For instance, program providers may make treatment more flexible for clients at risk for dropping out because of their unstable lifestyles, provide extra services to these individuals, or make more of an effort to contact clients who fail to attend a group session to encourage them to remain with the program (Cadsky et al., 1996; Chang & Saunders, 2002). In light of the evidence sug-gesting that theoretical orientation is a moderator variable that can differentiate treatment completers from dropouts, we also suggest that, when case managers have the luxury of choosing between programs, they should refer offenders according to the match between personal characteristics (i.e., age, education, and referral source) and the theoretical orien-tation of the treatment programs.

The relationship observed between education and program completion also may be reflective of another central responsivity issue: cognitive functioning (Kennedy, 2000). Although little research has explicitly studied the relationship between male batterers’ intellectual and cognitive functioning and domestic violence program completion, the level of education obtained may be a proxy for lower levels of cognitive functioning. That is, individuals with less education may find it more difficult to “keep up” with domestic vio-lence treatment programs and, as a result, decide to drop out (Chang & Saunders, 2002). More research is needed to explore this potential implication. However, if less education is found to be a proxy for lower cognitive abilities, it may be possible to decrease attrition by ensuring that treatment is delivered to individuals with less education in a way that they are able to follow and understand or by providing additional assistance to these persons outside of the group meetings (Chang & Saunders, 2002; Rooney & Hanson, 2001).

Given that others (Wormith & Olver, 2002) have found that factors related to recidivism also predict attrition and that substance abuse is a known predictor of domestic violence recidivism (Hilton & Harris, 2005), it was not surprising to find that offenders who suffer from alcohol and drug abuse were less likely to persist in treatment. Therefore, it may be possible to increase batterers’ likelihood of attending and remaining in treatment by address-ing substance abuse (Daly & Pelowski, 2000). Similarly, the finding that individuals belonging to a minority group have a greater tendency to drop out of treatment suggests that the provision of culturally sensitive programs may be a means of increasing treatment completion among racial minority offenders (Chang & Saunders, 2002).

The responsivity principle also brings to our attention additional individual characteris-tics that may be related to attrition and should be considered in future research. Specifically, motivation is thought to be an important factor for predicting whether participants will remain in treatment (Daly & Pelowski, 2000; Kennedy, 2000). Some researchers (e.g., Cadsky et al., 1996) have used court referral as a proxy for measuring motivation and have argued that individuals who volunteer for treatment will be more intrinsically motivated to attend and complete treatment. However, others have suggested that individuals who are extrinsically motivated by the courts will be more likely to complete treatment (Faulkner et al., 1991; Hamberger & Hastings, 1989). A meta-analysis by Parhar et al. (2008) exam-ined the relative impact of being court mandated and self-referred on treatment success and found that court-mandated treatment tends to be ineffective, whereas voluntary treatment tends to bring about more successful results. The results from our meta-analysis suggest that individuals who are court mandated to treatment are more likely to complete domestic violence treatment, suggesting that extrinsic motivation may be sufficient for this particular




group of offenders. However, given that referral source is not an ideal measure of motivation and that these results are in contrast to the meta-analysis conducted by Parhar et al., the cur-rent finding should be considered carefully. In the future, researchers should explicitly meas-ure motivation as a means of determining the extent to which it is a predictor of attrition.

Other factors related to responsivity that have not been studied in relation to domestic violence treatment attrition are therapist characteristics (Kennedy, 2000). Specifically, a therapist’s ability to establish a therapeutic alliance and a therapist’s ability to enhance offenders’ motivation have been identified as important factors in preventing program attri-tion (Dalton, 2001; Kennedy, 2000; Wormith & Olver, 2002). However, little research has focused on the relationship between therapist characteristics and domestic violence treat-ment attrition. This question should be addressed in future studies (Chang & Saunders, 2002). Aside from the current meta-analytic findings, the congruence between the client and treatment also has not been widely explored in relation to domestic violence treatment attrition (Cadsky et al., 1996; Rooney & Hanson, 2001). For instance, minimal research has investigated whether congruence between batterers’ self-identified problems and the treat-ment that is provided leads to higher rates of program completion (Cadsky et al., 1996). In addition, the relationship between batterers’ learning style and likelihood of completing treatment is largely unknown (Rooney & Hanson, 2001). Given our finding that the theo-retical orientation of a program may influence attrition, it seems that program characteris-tics are important issues to consider when assigning batterers to treatment programs. Clinicians and researchers are encouraged to explore the congruence between client and treatment characteristics with attrition as we have begun to show that responsivity issues may be used to minimize treatment attrition.

CLINICAL AND POLICY IMPLICATIONS

Several implications pertaining to policy can be derived from these meta-analytic findings. With respect to clinical policy, the results underscore the importance of using demographic, violence-related, and intrapersonal client characteristics to determine which individuals may be at risk for attrition from domestic violence treatment programs. On identifying individuals who are at risk for dropping out, it may be possible to minimize their attrition by providing them with additional supports to enhance their ability to attend group sessions and to under-stand the content of the programming. Therapists might also consider “pretreatment sessions” with identified offenders using motivational interviewing techniques to prepare the clients for the formal programming to follow (Miller & Rollnick, 2002).

The meta-analysis also has implications for sentencing. When developing conditions for sentencing, it may be valuable to take particular client characteristics (e.g., age and educa-tion) into account to ensure that clients are directed to the programs from which they are most likely to benefit. Although there is considerable debate about both the ethics and effec-tiveness of mandated versus voluntary treatment of offenders (Parhar et al., 2008), using court sentences to mandate batterers to treatment programs may be one way to increase program completion. Overall, the meta-analysis indicates that one type of batterer treat-ment programming does not fit all offenders. Batterer intervention agencies are encouraged to improve successful completion of programming by offering treatment in different for-mats rather than insisting that, for instance, all batterers must attend feminist psychoeducational treatment. Moreover, it should not be concluded from this analysis that offenders who are prone to attrition should be restricted from programming because they are less likely to




complete treatment. In fact, it is these same individuals who are the most likely to reoffend, and therefore, it is important to direct additional resources toward them to increase the likelihood they will complete treatment as per the “risk” principles (Andrews et al., 1990; Babcock & Steiner, 1999; Bennett et al., 2007; Gordon & Moriarty, 2003).

LIMITATIONS

The present meta-analysis has resulted in the identification of numerous variables that can be used to distinguish domestic violence program completers and dropouts. However, the findings should be interpreted with the following caveats in mind. First, variations in definitions of attrition, program content (particularly with respect to theoretical orienta-tion), and program length compromise our ability to compare results from different studies and introduce increased variance into the meta-analytic findings. Future researchers may consider attrition as a continuous variable (i.e., number of sessions attended) to avoid issues associated with defining attrition. Second, the current study focused only on in-program attrition; postreferral and postassessment attrition may each be characterized by a different constellation of factors. Furthermore, only studies that were offered in community- based settings using a group format were included in this meta-analysis. Different variables may predict attrition from programs offered in institutional settings or that employ other formats. Third, the fact that it was not possible to account for the variation associated with employment and criminal history suggests there may be additional variables that have not yet been studied in relation to domestic violence treatment attrition (e.g., motivation, therapeutic alliance) that may account for treatment dropout. Additional research is needed to uncover these variables. Similarly, more research is needed on variables such as the severity of abuse inflicted, personal experience of abuse, witnessing of family violence, anger, and depression, as the small number of studies exploring these variables limited our ability to detect effects in this meta-analysis. Finally, the meta-analysis was limited to English-language documents and could be enhanced by including non-English research.

A considerable body of research has focused on the relationship between domestic vio-lence treatment attrition and particular demographic, violence-related, and intrapersonal variables. At this point, it would be valuable for researchers to turn to lesser studied vari-ables (e.g., therapist characteristics, batterers’ learning styles and cognitive abilities) asso-ciated with treatment responsivity to establish whether any of these currently unstudied factors also are related to treatment attrition. Many of the limitations associated with this meta-analysis will be resolved as more evidence is gathered. Until that time, however, this study reflects an important first look at the existing evidence to establish what is currently known about the client and program factors associated with domestic violence treatment dropout. It also provides direction to providers of domestic violence treatment programs that target male batterers about how to minimize dropout by matching client characteristics with the mode of service delivery.

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Lisa M. Jewell is a PhD candidate in applied social psychology at the University of Saskatchewan. She is currently carrying out research in the areas of intimate partner violence, homelessness, and prejudice and discrimination directed toward sexual minorities.

J. Stephen Wormith is a professor in the Department of Psychology at the University of Saskatchewan. His research inter-ests include risk assessment, offender treatment, sexual offenders, gangs, and crime prevention.



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