“…Missing data pose a serious challenge to modeling growth by introducing a potential source of systematic bias that threatens model-based inferences and generalizability (Allison, 2002; McKnight, McKnight, Sidani, & Figueredo, 2007; Schafer & Graham, 2002). Missing data are pervasive in longitudinal AA-AAS data sets, with as much as 75% attrition documented across Grades 3 to 8 (Saven et al, 2016; Tindal, Nese, Farley, Saven, & Elliott, 2016). The missingness can partially be attributed to students switching between the general and alternate assessments between years (Saven et al, 2016).…”