Using Marginal Structural Modeling for Grade Retention Effects.

Reshetnyak, Evgeniya; Cham, Heining; Hughes, Jan N

Reshetnyak, Evgeniya; Cham, Heining; Hughes, Jan N.

Afiliación

Reshetnyak E; a Department of Psychology , Fordham University.
Cham H; a Department of Psychology , Fordham University.
Hughes JN; b Department of Educational Psychology , Texas A & M University.

Multivariate Behav Res ; 51(6): 871-876, 2016.

Article en En | MEDLINE | ID: mdl-27485663

RESUMEN

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,

Palabras clave

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

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Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Multivariate Behav Res Año: 2016 Tipo del documento: Article Pais de publicación: Estados Unidos

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