Monitoring the Growth of Youth Protective Factors: Implications for Prevention Service Delivery in Routine Practice

• In Event: Building Infrastructure for Social Emotional Learning in Routine Practice: Assessing Student Competence and Instruction Fidelity

Abstract

Objective: Social Emotional Learning (SEL) programs have demonstrated the capacity to enhance protective factors to prevent early behavioral problems (Kam, Greenberg, & Kusche, 2004).Through repeated assessment of protective factors, program administrators could make real-time implementation decisions regarding which children may benefit from an accelerated intervention, how the intervention should be administered, and whether a child is responding to an intervention. This paper aims to produce practice-relevant knowledge to inform these decisions by examining: 1) To what extent protective factors change when no SEL programs are explicitly implemented; 2) how much growth in protective factors can be expected in response to high quality SEL instruction; and 3) to what extent growth of student protective factors can be predicted by student demographics, baseline levels of protective factors, and classroom-level SEL implementation quality.

Methods: Social emotional competence was assessed among students in grades K-5 (n=8,446) at three time points (October, January, & June). Grades K-2 students received SEL programming while grades 3-5 students served as the quasi-comparison group. Technical assistance providers conducted monthly observations reporting on the implementation quality. Using multilevel modeling to account for variation across time, individual, and teachers, we first examined the growth in protective factors over time by intervention condition. We then assessed how student demographics, baseline protective factors, and implementation quality (intervention group only) affect the rate of growth in youth protective factors.

Results: Youth protective factors significantly increased across time, averaging an increase of 0.76 T-Score points per time interval (p<0.001) in the comparison group and 1.83 T-Score points per time interval (p<0.001) in the intervention group. For each time interval, and in both conditions, females’ protective factors grew faster than males (p<0.001). Students identified as having “strengths” at baseline grew faster within each time interval, across conditions, relative to those identified as having “typical” levels of protective factors or a “need for instruction” at baseline. However, in the absence of an intervention, children in the “strength” range grew more disproportionately relative to their peers. In the comparison group, African American students grew at a slower rate per time interval (p<0.01) compared to European American students. This racial difference was not observed in the intervention group. Finally, ratings of implementation quality (b=1.7) significantly (p<0.001) predicted youth protective factor growth over time.

Conclusion: While all youth experienced significant growth in protective factors, those exposed to SEL experienced higher rates of growth. Growth rate differences by gender and baseline levels of protective factors were observed across intervention and comparison groups, with comparison groups indicating greater differences. This is especially notable in the difference in growth rates by race where only the comparison group shows significant difference. These results suggest that the SEL program may have reduced the potential disparities in the rate of growth in protective factors based on individual characteristics. Finally, implementation quality was associated with growth in youth protective factors. Limitations will be discussed.

Authors

• B. K. Elizabeth Kim, University of California - Berkeley

• Valerie Shapiro, UC Berkeley

• Jennifer Fleming Robitaille, Devereux Center For Resilient Children

• Paul Augustine LeBuffe, Devereux Center for Resilient Children