Middle school effects of the Dating Matters (R) comprehensive teen dating violence prevention model on physical violence, bullying, and cyberbullying:A cluster-randomized controlled trial

Few comprehensive primary prevention approaches for youth have been evaluated for effects on multiple types of violence. Dating Matters®: Strategies to Promote Healthy Teen Relationships (Dating Matters) is a comprehensive teen dating violence (TDV) prevention model designed by the Centers for Disease Control and Prevention and evaluated using a longitudinal stratified cluster-randomized controlled trial to determine effectiveness for preventing TDV and promoting healthy relationship behaviors among middle school students. In this study, we examine the prevention effects on secondary outcomes, including victimization and perpetration of physical violence, bullying, and cyberbullying. This study examined the effectiveness of Dating Matters compared to a standard-of-care TDV prevention program in 46 middle schools in four high-risk urban communities across the USA. The analytic sample (N = 3301; 53% female; 50% Black, non-Hispanic; and 31% Hispanic) consisted of 6th–8th grade students who had an opportunity for exposure to Dating Matters in all three grades or the standard-of-care in 8th grade only. Results demonstrated that both male and female students attending schools implementing Dating Matters reported 11% less bullying perpetration and 11% less physical violence perpetration than students in comparison schools. Female Dating Matters students reported 9% less cyberbullying victimization and 10% less cyberbullying perpetration relative to the standard-of-care. When compared to an existing evidence-based intervention for TDV, Dating Matters demonstrated protective effects on physical violence, bullying, and cyberbullying for most groups of students. The Dating Matters comprehensive prevention model holds promise for reducing multiple forms of violence among middle school-aged youth

A comparison of methods for creating multiple imputations of nominal variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002) ranking procedure and MICE with CART performed poorly in most conditions

Principled missing data treatments

We review a number of issues regarding missing data treatments for intervention and prevention researchers. Many of the common missing data practices in prevention research are still, unfortunately, ill-advised (e.g., use of listwise and pairwise deletion, insufficient use of auxiliary variables). Our goal is to promote better practice in the handling of missing data. We review the current state of missing data methodology and recent missing data reporting in prevention research. We describe antiquated, ad hoc missing data treatments and discuss their limitations. We discuss two modern, principled missing data treatments: multiple imputation and full information maximum likelihood, and we offer practical tips on how to best employ these methods in prevention research. The principled missing data treatments that we discuss are couched in terms of how they improve causal and statistical inference in the prevention sciences. Our recommendations are firmly grounded in missing data theory and well-validated statistical principles for handling the missing data issues that are ubiquitous in biosocial and prevention research. We augment our broad survey of missing data analysis with references to more exhaustive resources

The supermatrix technique: A simple framework for hypothesis testing with missing data

We present a new paradigm that allows simplified testing of multiparameter hypotheses in the presence of incomplete data. The proposed technique is a straight-forward procedure that combines the benefits of two powerful data analytic tools: multiple imputation and nested-model χ2 difference testing. A Monte Carlo simulation study was conducted to assess the performance of the proposed technique. Full information maximum likelihood (FIML) and single regression imputation were included as comparison conditions against which the performance of the suggested technique was judged. The imputation-based conditions demonstrated much higher convergence rates than the FIML conditions. Δχ2 statistics derived from the proposed technique were more accurate than such statistics derived from both the FIML conditions and the regression imputation conditions. Limitations of the current work and suggestions for future directions are also addressed

Randomized trial of PMTO in foster care:12-month child well-being, parenting, and caregiver functioning outcomes

Evidence-supported parenting interventions (ESPIs) increasingly are used in child welfare to improve child well-being and parenting. However, little evidence exists on the effectiveness of ESPIs with biological families of children in foster care with serious behavioral health problems. To address this gap in the literature, we examined the outcomes of in-home Parent Management Training Oregon model (PMTO). PMTO was evaluated in a randomized trial in which 918 children were assigned to PMTO or services as usual with assessment at baseline, 6 months, and 12 months. Outcome domains included child social-emotional well-being, parenting, and caregiver functioning. Our results show PMTO demonstrated linear improvements in children's social-emotional functioning, problem behaviors, and social skills. Although results for parenting were inconclusive, two of four caregiver functioning outcomes (parent mental health and readiness for reunification) were significantly improved. Overall, these findings suggest PMTO and similar ESPIs may hold promise for promoting better outcomes for biological families of children in foster care with behavioral health problems

Randomized Study of PMTO in Foster Care: Six-Month Parent Outcomes

Objective: This study examined the effects of Parent Management Training, Oregon (PMTO) model on parenting effectiveness and caregiver functioning. Method: Children in foster care with emotional and behavioral problems were randomized to PMTO (n = 461) or services as usual (n = 457) in a nonblinded study design. Using an intent-to-treat approach, analysis of covariance models tested the intervention’s overall effect and time interactions for parenting and caregiver functioning. Additional analyses were conducted to identify significant predictors of outcomes. Results: PMTO did not significantly affect parenting practices; however, positive effects were observed on caregiver functioning in mental health (odds ratio [OR] = 2.01), substance use (OR = 1.67), social supports (OR = 2.37), and readiness for reunification (OR = 1.64). While no time interactions were detected, several child, parent, and case characteristics were associated with improvements in 6-month outcomes. Conclusion: This study extends evidence on PMTO to biological families of children in foster care, including those with older youth

Randomized study of PMTO in foster care:Six-month parent outcomes

Objective:  This study examined the effects of Parent Management Training, Oregon (PMTO) model on parenting effectiveness and caregiver functioning. Method:  Children in foster care with emotional and behavioral problems were randomized to PMTO (n = 461) or services as usual (n = 457) in a nonblinded study design. Using an intent-to-treat approach, analysis of covariance models tested the intervention's overall effect and time interactions for parenting and caregiver functioning. Additional analyses were conducted to identify significant predictors of outcomes. Results:  PMTO did not significantly affect parenting practices; however, positive effects were observed on caregiver functioning in mental health (odds ratio [OR] = 2.01), substance use (OR = 1.67), social supports (OR = 2.37), and readiness for reunification (OR = 1.64). While no time interactions were detected, several child, parent, and case characteristics were associated with improvements in 6-month outcomes. Conclusion:  This study extends evidence on PMTO to biological families of children in foster care, including those with older youth

Maximizing data quality and shortening survey time: Three-form planned missing data survey design

Simulation studies have shown the three-form planned missing data design efficiently collects high quality data while reducing participant burden. This methodology is rarely used in sport and exercise psychology. Therefore, we conducted a re-sampling study with existing sport and exercise psychology survey data to test how three-form planned missing data survey design implemented with different item distribution approaches effect constructs’ internal measurement structure and validity. Results supported the efficacy of the three-form planned missing data survey design for cross-sectional data collection. Sample sizes of at least 300 (i.e., 100 per form) are recommended for having unbiased parameter estimates. It is also recommended items be distributed across survey forms to have representation of each facet of a construct on every form, and that a select few of these items be included across all survey forms. Further guidelines for three-form surveys based upon the results of this resampling study are provided