Factor de reasignación y factor de reasignación máximo

3.5.8.1 Program engagement and program attendance. Fourteen of the 24

participants who did not successfully complete the program left prior to being rated on program engagement, which resulted in reduced power when analyzing the relationship between program engagement and attrition (n = 10). This lack of power may in part be related to the finding that program engagement was not significantly correlated with attrition (rpb = -.13, p = .29) or the number of sessions attended (r = .13, p = .27).

3.5.8.2 Program engagement and short-term treatment targets. A series of analyses tested the hypothesis that program engagement would relate to reductions in risk/treatment targets (i.e., distorted attitudes about IPV and relationship problems).

3.5.8.2.1 Distorted attitudes about IPV. Hierarchical multiple regression analyses were used to examine whether program engagement at early- and late-treatment predicted changes in distorted attitudes about IPV while controlling for pre-treatment distorted attitudes about IPV.

Early-treatment program engagement significantly predicted reductions in distorted attitudes about IPV, ΔF (1,60) = 10.6, ΔR2 _{= .064, β = -.25, p = .002. A follow-up hierarchical}

multiple regression was conducted with the GEM subscales as predictor variables. Overall, the six subscales contributed significantly to the regression equation, ΔF (6,55) = 5.79, ΔR2 _{= 0.16, p}

< .001. Together, the six GEM subscales accounted for 16% of variance in reductions in

distorted thinking about IPV. The only subscale with a significant regression coefficient was the Working on Own Problems subscale, β = -.38, p = .001, sr2 = .057, which accounted for 6% of unique variance in reductions in distorted thinking about IPV.

Late-treatment program engagement was significantly related to reductions in distorted attitudes about IPV, ΔF(1,59) = 20.9, ΔR2 _{= 0.11, β = -.34, p < .001. A follow-up hierarchical}

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multiple regression was conducted with the GEM subscales as predictor variables. Overall, the six subscales contributed significantly to the regression equation, ΔF (6,54) = 6.03, ΔR2 _{= 0.17, p}

< .001. Together, the six GEM subscales accounted for 17% of variance in reductions distorted thinking about IPV. As with the early-treatment program engagement, the only subscale with a significant regression coefficient was the Working on Own Problems subscale, β = -.36, p = .002, sr2 = .047, which accounted for 5% of unique variance in reductions in distorted thinking about IPV.

When controlling pre-treatment distorted attitudes about IPV and early-treatment

program engagement, increases in program engagement were significantly related to reductions in distorted attitudes about IPV, ΔF(1,58) = 8.83, ΔR2 _{= 0.047, β = -.24, p = .004. A follow-up}

hierarchical multiple regression with the GEM subscales as predictors was not conducted due to a lack of power.

3.5.8.2.2 Relationship problems. Since relationship problems was coded dichotomously, change on this variable was examined using hierarchical logistic regression with pre-treatment relationship problems (yes/no) entered in the first block, the predictor variable(s) (i.e., the measure of program engagement) into the second block, and post-treatment relationship problems (yes/no) as the dependent variable.

Early program engagement significantly predicted a reduction in relationship problems from pre- to post-treatment, Wald 2 (1, n = 49) = 3.92, B = -1.27, SE = 0.64, eB= 0.28, 95% CI [0.079, 0.99], p = .048. There was insufficient power to conduct a follow-up regression analysis using all six GEM subscales as predictors. Therefore, semipartial point-biserial correlations were conducted, controlling for pre-treatment relationship problems. The Contributing (rpb = - .30, p = .020), Relating with Members (rpb = -.29, p = .023), and Working on Others’ Problems (rpb = -.26, p = .043) subscales at pre-treatment were significantly related to reductions in relationship problems. The Attending, (rpb =.040, p =.76), Relating to Worker (rpb = -.15, p = .27), and Working on Own Problems (rpb = -.18, p = .17) subscales were not.

Another hierarchical logistic regression demonstrated that late program engagement did not significantly relate to reductions in relationship problems, Wald 2 _{(1, n = 48) = 0.99, B = -}

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correlations were conducted with each subscale, controlling for pre-treatment relationship problems. Consistent with the results of the logistic regression, no GEM subscales were significantly related to reduced relationship problems, including the Attending, (rpb = -.066, p =.63), Contributing (rpb = -.005, p = .97), Relating To Worker (rpb = -.061, p = .65), Relating with Members (rpb = -.20, p = .14), Working On Own Problems (rpb = -.11, p = .42), and Working On Others’ Problems (rpb = -.19, p = .15) subscales.

When controlling pre-treatment relationship problems and early-treatment program engagement, increases in program engagement did not significantly predict reductions in relationship problems, Wald 2 _{(1, n = 48) = 0.35, B = 0.47, SE = 0.80, e}B_{= 1.60, 95% CI [0.33,} 7.66], p = .56. The large standard error and wide confidence intervals around the hazard ratio suggest that the nonsignificant finding may be related to the low base rate of endorsed

relationship problems at post-treatment (n = 12) and thus a lack of power.

3.5.8.3 Program engagement and risk. As shown in Tables 3.47 and 3.48, semipartial correlations, controlling for pre-treatment risk, were conducted to examine whether different components of early- and late-treatment program engagement were related to changes in risk on the SARA-V3. There was a tendency for higher early-treatment scores on the Working on Own Problems subscale to correlate with decreases in risk (p = .057) with small effects. There was also a tendency for decreases in risk to be positively correlated with higher late-treatment scores on the Relating with Members (p = .058), Working on Own Problems (p = .095), and Working with Others’ Problems (p = .067) subscales.

Table 3.47

Semipartial Correlations Demonstrating Relationships between Early Program Engagement and Changes in Risk on the SARA-V3

Total Attend. Contrib.

Relate Work. Relate Mem. Own Prob. Others Prob. SARA-V3 Δ -.13 .071 -.065 -.13 -.022 -.19† .13 Note. Controlling for pre-treatment risk on the SARA-V3. Attend. = Attending. Contrib. = Contributing. Relate Work. = Relating to Worker. Relate Mem. = Relating with Members. Own Prob. = Working on Own Problems. Others Prob. = Working with Others’ Problems.

114 Table 3.48

Semipartial Correlations Demonstrating Relationships between Late Program Engagement and Changes in Risk on the SARA-V3

Total Attend. Contrib.

Relate Work. Relate Mem. Own Prob. Others Prob. SARA-V3 Δ -.18 -.013 -.11 -.079 -.23† -.20† -.22† Note. Controlling for pre-treatment risk on the SARA-V3. Attend. = Attending. Contrib. = Contributing. Relate Work. = Relating to Worker. Relate Mem. = Relating with Members. Own Prob. = Working on Own Problems. Others Prob. = Working with Others’ Problems.

†_{p < .10}

Semipartial correlations also examined whether program engagement change scores related to changes in risk on the SARA-V3, while controlling for pre-treatment risk and the respective early-treatment GEM subscale. As shown in Table 3.49, increases on the Relating with Members subscale was significantly associated with decreases in risk (p = .023).

Table 3.49

Semipartial Correlations Demonstrating Relationships between Changes Program Engagement and Changes in Risk on the SARA-V3

Total Attend. Contrib.

Relate Work. Relate Mem. Own Prob. Others Prob. SARA-V3 Δ -.14 -.014 -.079 -.008 -.27* -.13 -.18 Note. Controlling for pre-treatment risk on the SARA-V3 and each respective early-treatment subscale. Attend. = Attending. Contrib. = Contributing. Relate Work. = Relating to Worker. Relate Mem. = Relating with Members. Own Prob. = Working on Own Problems. Others Prob. = Working with Others’ Problems.

*p < .05

Hierarchical multiple regressions tested the hypothesis that program engagement would predict reductions in risk, while controlling for pre-treatment risk (on the SARA-V3). Early- treatment program engagement was also controlled when early- to late-treatment change in program engagement was the predictor. Contrary to hypotheses, early program engagement ΔF (1,59) = 1.09, ΔR2 _{= .016, β = -0.13, p = .30, late program engagement ΔF (1,57) = 2.31, ΔR}2 ₌

.033, β = -0.18, p = .13, and changes in program engagement ΔF (1,56) = 1.30, ΔR2 = .019, β = - 0.15, p = .26, did not significantly predict changes in risk.

115 3.5.8.4 Program engagement and recidivism.

3.5.8.4.1 Correlations. Exploratory correlations and point-biserial correlations examined the relationships between different components of program engagement (i.e., the GEM

subscales) and recidivism, as shown in Tables 3.50 and 3.51. Table 3.50

Correlations between Early Program Engagement and Recidivism

Measure Total Attend. Contrib.

Relate Work. Relate Mem. Own Prob. Others Prob. Any General a _{-.014 -.13 .047 .079 .026 -.10 -.002} Any Violent a -.11 .080 -.15 -.11 -.097 -.056 -.15 No. of Charges -.087 -.098 -.12 .010 -.024 -.058 -.11 No. of Violent -.12 .079 -.16 -.10 -.14 -.053 -.16 Note. Any General = Any new charges. Any Violent = Any violent charges. No. of Charges = Number of new charges. No. of Violent = Number of new violent charges. Attend. = Attending. Contrib = Contributing. Relate Work. = Relating to Worker. Relate Mem. = Relating with Members. Own Prob. = Working on Own Problems. Others Prob. = Working with Others’ Problems.

a _{Point-biserial correlations.}

Table 3.51

Correlations between Late Program Engagement and Recidivism

Measure Total Attend. Contrib.

Relate Work. Relate Mem. Own Prob. Others Prob. Any General a _{-.028 -.23}† _{.058 .056 .066 -.13 -.034} Any Violent a -.18 -.16 -.067 -.064 -.12 -.28* -.16 No. of Charges -.035 -.24† .12 .10 -.014 -.14 -.068 No. of Violent -.13 -.20 .018 -.046 -.095 -.22† -.13 Note. Any General = Any new charges. Any Violent = Any violent charges. No. of Charges = Number of new charges. No. of Violent = Number of new violent charges. Attend. = Attending. Contrib = Contributing. Relate Work. = Relating to Worker. Relate Mem. = Relating with Members. Own Prob. = Working on Own Problems. Others Prob. = Working with Others’ Problems.

a _{Point-biserial correlations.} a _{Point-biserial correlations.}

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Early-treatment program engagement was not significantly associated with recidivism. For late-treatment program engagement, the Working on Own Problems subscale was related to violent recidivism (p =.027) and the number of new violent charges (p = .082), although this latter relationship only approached significance. The Attending subscale was related to general recidivism (p = .074) and the number of new charges (p = .055), although these correlations only approached significance.

3.5.8.4.2 Regressions. It was hypothesized that lower program engagement would predict recidivism. A series of Cox regression survival analyses were conducted to measure the extent to which early-treatment, late-treatment, and changes in program engagement predicted general and violent recidivism, while controlling for the effects of pre-treatment risk on the SARA-V3 and the ODARA. For analyses that examined changes in program engagement, the effects of early-treatment program engagement were also controlled. Assumptions were met regarding the adequacy of the sample size, survival experiences over time, multicollinearity, and proportionality of hazards. No issues with normality were detected for program engagement (i.e., GEM total scores) or for time to general recidivism. Time to violent recidivism had a bimodal distribution, although the assumption of normality is not required for Cox regression analyses even though normality may enhance the power of the analysis (Tabachnick & Fidell, 2013). As such, no transformations were conducted for the variables used in the Cox regression analyses.

A number of hierarchical multiple regressions, controlling pre-treatment risk and follow- up time, were also conducted to examine the extent to which early-treatment, late-treatment, and changes in program engagement predicted the number of new charges and new violent charges incurred in the follow-up period. For analyses that examined changes in program engagement, the effects of early-treatment program engagement were also controlled. These outcome variables (i.e., number of new charges and new violent charges) were not normally distributed and scatterplots demonstrated issues with homoscedasticity. Transformed scores were not used as they can negatively impact the interpretation of findings and transformed scores only

minimally improved issues with normality and homoscedasticity. The results were thus interpreted with caution.

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When controlling for pre-treatment risk, early program engagement did not predict general recidivism Wald 2 (1, n = 71) = 0.68, B = -0.34, SE = 0.41, eB = 0.71, 95% CI [0.32, 1.59], p = .41, or the number of new charges ΔF (1,66) = 0.002, ΔR2 < .001, β = 0.005, p = .97. Similarly, when controlling for pre-treatment risk, late-treatment program engagement did not predict general recidivism Wald 2 (1, n = 62) = 0.70, B = -0.38, SE = 0.45, eB = 0.69, 95% CI [0.28, 1.66], p = .40, or the number of new charges ΔF (1,57) = 0.066, ΔR2 = .001, β = -0.033, p = .80. When controlling for pre-treatment risk and early program engagement, changes in

program engagement did not predict general recidivism Wald 2 _{(1, n = 62) = 2.35, B = -1.09, SE}

= 0.71, eB = 0.34, 95% CI [0.083, 1.36], p = .13, or the number of new charges ΔF (1,56) = 0.90, ΔR2 _{= .001, β = -0.04, p = .77.}

When controlling for pre-treatment risk, early program engagement did not predict violent recidivism Wald 2 (1, n = 71) = 1.82, B = -0.86, SE = 0.64, eB = 0.42, 95% CI [0.12, 1.48], p = .18, or the number of new violent charges ΔF (1,66) = 0.45, ΔR2 = .006, β = -0.081, p = .50. However, late-treatment program engagement approached significance in predicting violent recidivism while controlling for pre-treatment risk, Wald 2 (1, n = 62) = 3.35, B = -1.39, SE = 0.76, eB _{= 0.25, 95% CI [0.056, 1.10], p = .067. But, late program engagement did not} significantly predict the number of new violent charges when controlling for pre-treatment risk, ΔF (1,57) = 0.63, ΔR2 _{= .009, β = -0.10, p = .43. When controlling for pre-treatment risk and}

early program engagement, changes in program engagement did not predict violent recidivism Wald 2 (1, n = 62) = 2.49, B = -1.07, SE = 0.68, eB = 0.34, 95% CI [0.090, 1.30], p = .12, or the number of new violent charges ΔF (1,56) = 0.16, ΔR2 = .002, β = -0.054, p = .69.

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CHAPTER 4: DISCUSSION