Childhood exposure to domestic violence predicts relationship violence: A meta-analysis

1. Childhood exposure to domestic violence predicts relationship violence: A meta-analysis Markus Kemmelmeier and Kerry Kleyman

2. Introduction • Problem of violence and abuse in families • Immediate victims • Witnesses (not immediately victimized) • Short-term vs. long-term effects • Short-term effects victims • Long-term effects victims • Short-term/concurrent effects witnesses (e.g., Kitzmann et al., 2003) • What about long-term effects in witnesses?

3. The Cycle of Violence Hypothesis • Transmission of violence across generations • Parents/caregivers are an important influence • Child Witness  Adult Perpetrator • Child Witness  Adult Victim

4. Causal processes • Social Learning Theory • Relationship norms • Gender norms • Norms for violent behavior • Relationship models • Psychodynamic approaches

5. The “comorbidity” problem • Child is not only witness, but also • Victim • in dysfunctional family with • sexual abuse • alcohol abuse • drug abuse

6. Research field • Scattered • Psychology • Social work • Sociology • Medicine • Criminal Justice • Etc. • Difference in focus, methods, findings • Needed: An integration & review

7. Meta-analysis • Statistical synthesis of available research findings • Establishes cumulative science, • Can findings be generalized? • Moderators? • Hypothesis testing in the aggregate • But: “Garbage in, garbage out” • A meta-analysis is only as good as the data on which it is based

8. The “How to” of a meta-analysis • Define problem, concepts based on literature • Decide on inclusion (exclusion) criteria • Compare apples with apples • Find studies • Databases • Asking colleagues, listservs • Ancestry approach • Code statistics • Effect sizes • Study characteristics File drawer problem

9. The “How to” of a meta-analysis • Statistical size of the effect that is independent of sample size and specific measure used • Types r family (Pearson correlation) d family (Pearson correlation): Mean difference divided by standard deviation • Extract effect sizes • “read off” • compute • Transform • Infer Research reports might be --incomplete --wrong --unusable

10. r -- Binomial Effect Size Display (BESD) • Transform variables into dichotomous categories • Correlation between being a witness and engaging in relationship violence r = .20 • Only 4% of the variance (r2) • Risk witnesses 60% • Risk non-witnesses 40% Witnesses are 50% more likely to become violent than nonwitnesses.

11. Method: Literature Search • PsychINFO Database • Keywords: [(dating or courtship) AND (violence or abuse or aggression)] • 1008 identified, 283 included family-of-origin violence • Additional studies obtained from references and manual inspection of violence journals

12. Method: Inclusion/Exclusion Criteria • Contained witnessing or experiencing parental violence and perpetrated or experienced violence in adult relationships • Included data on physical violence in family of origin or in current dating experience • Had to report the quantitative data necessary to calculate at least one effect size • Studies had to be reported in English between 1975 and 2006 • 53 research reports were retained for coding

13. Method: Coding • Article-level coding, including: • Author gender, department affiliation, study design, location, sampling, year of publication • Construct-level coding, including: • Sample characteristics, theoretical constructs, methodological variations, and effect size • Each article was coded with on article-level, and most had multiple construct-level coding. • Two independent coders were used

14. Method: Data Analysis • 402 effect-size estimates from 53 studies • Effect-size estimates (r coefficients) were calculated from a variety of reported statistics, including means and standard deviations, chi-square values, p-values, and frequencies or proportions. (Used program Dstat) • Correlational studies, point-biserial correlations or Pearson’s r were recorded as individual effect sizes. • Because normal distributions of coefficients could often not be assumed, we used a randomization/resampling (bootstrapping) approach to estimate statistical parameters. (Using program MetaWin)

15. Check for File drawer problem • Across all studies coded, is the distribution of the effect sizes • Symmetrical • Funnel shaped?

16. Witnessing only vs. Experiencing only • Witnessing violence has as negative effects as experiencing physical violence. • Additive effect? • Type of Violence • Qb(3) = 23.17 ***/+ Types of Childhood Violence: Witnessing vs. Experiencing Effect Sizes + Note: +p<.10 *p<.05 **p<.01 ***p<.001

17. Results: Overall Witnessing Overall Effects (weighted means) • Victimization r =.107 (CI .079/.137) [74 data points] • Perpetration r =.138(CI .113/.162) [116 data points] • Unspecified r =.112 (CI .030/.212) [7 data points] • Qb(2) = 8.02, p < .28. • Witnessing family violence is as strongly linked to becoming a victim of relationship violence as it is to inflicting violence onto others.

18. Witnessing by Gender • Witnessing had stronger effects on men becoming perpetrators • No gender difference for victimization. • Victimization Qb(1) = 0.59 ns/ns • Perpetration Qb(1) = 23.85 ***/** Victimization vs. Perpetration by Gender Effect Sizes ** ns Note: +p<.10 *p<.05 **p<.01 ***p<.001

19. Perpetrator in Family of Origin by Gender, VICTIM Victimization: Perpetrator in Family of Origin by Gender Effect Sizes • Witnessing the mother perpetrate violence had the strongest effect on becoming a victim of later relationship violence. Witnessing father violence had a weaker effect on men becoming victims. • Male/Father • Qb(1) = 2.39 ns/ns • Female/Mother • Qb(1) = 0.65 ns/ns • Unspecified • Qb(1) = 0.19 ns/ns ns ns ns Note: +p<.10 *p<.05 **p<.01 ***p<.001

20. Perpetrator in Family of Origin by Gender, PERP Perpetration: Perpetrator in Family of Origin by Gender Effect Sizes • Whether male or female witnesses become perpetrators does not depend on whom they witnessed in their family of origin • Male/Father • Qb(1) = 3.30 +/ns • Female/Mother • Qb(1) = 0.49 ns/ns • Unspecified • Qb(1) = 21.32 ***/** ns ns ns Note: +p<.10 *p<.05 **p<.01 ***p<.001

21. Decade of Publication and Effect Sizes Victimization Qb(2) = 4.04 ns/ns Perpetration Qb(2) = 23.40 ***/** Note: +p<.10 *p<.05 **p<.01 ***p<.001

22. Witnessing by SES • Witnessing had the strongest effect on becoming a perpetrator in high SES samples • SES had no influence on whether witnesses became themselves victims of relationship violence • Victimization Qb(3) = 1.19 ns/ns • Perpetration Qb(4) = 29.81 ***/+ Victimization vs. Perpetration by SES Effect Sizes + ns Not enough “high” cases for analysis Note: +p<.10 *p<.05 **p<.01 ***p<.001

23. Departmental Affiliation Effect Sizes Departmental Affiliation of 1st Author • The background and affiliation of a study’s first author produce great differences in the effect sizes obtained. This suggests that research training and goals have a substantial influence on outcomes. • Departmental Affiliation • Qb(4) = 48.30 ***/** ** Note: +p<.10 *p<.05 **p<.01 ***p<.001

24. Physical vs. Psychological Violence • Witnessing violence in one’s family of origin make witnesses more likely to perpetrate psychological violence in their own relationships. • Victimization • Qb(1) = 0.01 ns/ns • Perpetration • Qb(1) = 16.42 ***/* Type of Violence: Physical vs. Psychological Violence Effect Sizes * ns Note: +p<.10 *p<.05 **p<.01 ***p<.001

25. Meta-analysis of coefficients comparing region and exposure to family violence to non-exposure Region Qb(5) = 91.27 ***/*** Note: +p<.10 *p<.05 **p<.01 ***p<.001

26. Discussion • Witnessing has pervasive effects on likelihood of becoming • a perpetrator • a victim Of relationship violence • Long-term effects! • Gender effects limited • Mainly males becoming perpetrators

27. Discussion • Other moderators • Class • Discipline • Social Learning Theory can help explain • BUT very limited support for a gender specificity hypothesis • Limitations