“…Simulation studies that compare FIML to numerous other methods of handling missing data in the context of longitudinal and multilevel modelingincluding listwise deletion, pairwise deletion, similar response pattern imputation, stochastic regression imputation, multiple imputation, and expectation-maximization imputation algorithmsuggest that FIML is superior to these analytic approaches to handling missing data (Enders & Bandalos, 2001;Larsen, 2011;Lee, Harring, & Stapleton, 2019;Newman, 2003). Furthermore, FIML is a common strategy to account for missing data in FFWCS (e.g., Carlson, McLanahan, & Brooks-Gunn, 2008;Gard, McLoyd, Mitchell, & Hyde, 2020, In Press;McLeod, Johnson, Cryer-Coupet, & Mincy, 2019;Meadows, McLanahan, & Knab, 2009;Waller et al, 2019).…”