Grade Retention in Primary School has Robust and Long-term Positive Effects on Academic Performance and Psychosocial Well-being. How to Avoid Model Dependence and Selection-bias in Grade Retention Research.

In this study, the effects of grade retention on school performance (mathematics) and psychosocial well-being are analyzed based on a large existing dataset that has already given rise to several publications. What is new in our study is that the data are analyzed for all years of grade retention and that different methods are used for which we thoroughly check the assumptions and if possible, test them. To improve analytical robustness, the effects of grade retention were investigated using different analysis techniques: latent growth models (LGM), cross-lagged panel models (CLPM), marginal structural models (MSM) and Sequential Conditional Mean Models (SCMM). Across the different methods, it appears that grade retention has a substantial and long-term positive effect on mathematics performance and well-being. The more the methods keep selection-bias and other forms of bias under control, the larger the estimates of the effects. Methods that make full use of longitudinal data are the least bias-sensitive. Finally, the size of the individual effects appears to depend exclusively on the intelligence of the pupil and on the ratio of grade repeaters in the school.