title: Using Marginal Structural Modeling for Grade Retention Effects.
abstract: 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 et al. (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 et al. (this issue) as an example, we provide our insights on four aspects of MSM: (a) covariate selection, (b) estimation of weights,
authors.affiliation: a Department of Psychology , Fordham University.
authors.forename: Evgeniya
authors.initials: E
authors.lastname: Reshetnyak
authors.affiliation: a Department of Psychology , Fordham University.
authors.forename: Heining
authors.initials: H
authors.lastname: Cham
authors.affiliation: b Department of Educational Psychology , Texas A & M University.
authors.forename: Jan N
authors.initials: JN
authors.lastname: Hughes
keywords: Causal inference; grade retention; machine learning; marginal structural model; missing data