Addressing Methodological Challenges in Follow-Up RCTs During the COVID-19 Pandemic: The Impact of the Good Behavior Game and MyTeachingPartner™ on Teacher Burnout and Self-Efficacy.

Retention of early career teachers is a critical issue in education, with burnout and self-efficacy serving as important precursors to teachers leaving the field. An integration of the PAX Good Behavior Game (GBG; Barrish et al., 1969) and MyTeachingPartner (MTP; Allen et al., 2015) was tested in a randomized controlled trial (RCT) to investigate whether the combined programs would improve long-term outcomes for early career teachers. However, due to the COVID-19 pandemic, there was a higher proportion of missing data and attrition in follow-up data collection than otherwise would have been expected. The current paper focused specifically on intervention impacts on teacher-reported burnout and self-efficacy through the COVID-19 pandemic and explored various approaches for addressing missing data as an illustrative example for other researchers who may similarly have faced missing data challenges due to the pandemic. Participants included in the original trial were N = 188 early career teachers (grades K-3) who were randomly assigned to either the intervention (i.e., GBG + MTP; n = 94 teachers) or control condition (n = 94) and provided baseline data. Specifically, teachers reported on their burnout and self-efficacy at pre-intervention (Fall), post-intervention (Spring), 1-year post intervention (in Fall and Spring), COVID Year 1 (Spring 2021), and COVID Year 2 (Spring 2022). We conducted a series of outcomes analyses under varying missing data assumptions (i.e., MCAR, MAR, NMAR). There were mixed findings (i.e., both null and beneficial) regarding GBG + MTP impacts on burnout, which varied across missing data assumptions; however, there were no GBG + MTP impacts on self-efficacy. This study may also provide insight for other researchers encountering similar challenges when analyzing follow-up data collected during the COVID-19 pandemic, as we highlight pros and cons of several different approaches for modeling missing data related to attrition due to the COVID-19 pandemic and related school closures.