This research by Goldschmidt and Wang used the National Educational Longitudinal Study (NELS) database to examine school and student factors associated with students dropping out of different grades. The authors found that early predicted risk, at both the student level and the school level, significantly affects the odds of a student dropping out late. They also found that being held back is the single strongest predictor of dropping out and that its effect is consistent for both early and late dropouts. This resource may be particularly useful to schools and districts looking to identify risk factors that can help them implement dropout prevention strategies.
When can schools affect dropout behavior? A longitudinal multilevel analysis
CCRS Center Publication
American Educational Research Journal