Using Marginal Structural Modeling for Grade Retention Effects

作者：Reshetnyak Evgeniya; Cham Heining; Hughes Jan N

来源：Multivariate Behavioral Research, 2016, 51(6): 871-876.

DOI：10.1080/00273171.2016.1200454

• Causal inference
• grade retention
• machine learning
• marginal structural model
• missing data

Vandecandelaere, Vansteelandt, De Fraine, and Van Damme (this issue) described marginal structural modeling (MSM) and used it to estimate the effects of a time-varying intervention, retention (holding back) in school grades, on students' math achievement. This commentary supplements Vandecandelaere etal. (this issue) and discusses several topics in retention studies and MSM. First, we discuss the importance of equating time-varying confounders in retention studies. Second, we discuss same-grade and same-age comparisons in retention studies. Third, we discuss one important section in the authors' overview of MSM: why standard methods (e.g., ANCOVA, propensity score analysis) cannot properly adjust for time-varying confounders. Finally, using the grade retention analyses in Vandecandelaere etal.

• 出版日期2016-12