Exploring the Benefits of a Parent Partner Mentoring Program in Child Welfare

Parent partner mentoring programs are an innovative strategy for child welfare agencies to engage families in case planning and service delivery. These programs recruit and train parents who have been involved in the system and have successfully resolved identified child abuse or neglect issues to work with families with current open cases in the child welfare system. Parent partner mentors can provide social and emotional support, advocacy, and practical advice for navigating this challenging system. Insofar as parent partners share similar experiences, and cultural and socioeconomic characteristics of families, they may be more successful in engaging families and building trusting supportive relationships. The current study presents qualitative data from interviews and case studies of families who were matched with a parent partner in a large county in a Midwestern state. Interviews with families, parent partner mentors, child welfare agency staff, and community partners and providers suggest that parent partner programs may be just as beneficial for parent partner mentors as they are for families being mentored. These programs can build professional skills, help improve self-esteem, provide an avenue for social support, and may potentially prevent recidivism. Parent Partner programs also provide a mechanism for amplifying family voice at all levels of the agency.

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^