Using Marginal Structural Modeling for Grade Retention Effects
Bibliographic Data
| ID | 19290973 |
|---|---|
| Authors | Evgeniya Reshetnyak (0000-0001-9478-4405, Fordham University), Heining Cham (0000-0002-2933-056X, Fordham University, corresponding author), Jan N Hughes (0000-0002-4433-1034, Texas A&M University) |
| Year | 2016 |
| Volume | 51 |
| Issue | 6 |
| Pages | 1-6 |
| Publication date | 2016-08-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2016.1200454 |
| PMID | 27485663 |
| OpenAlex | W2501992360 |
| Language | EN |
| Citations received | 2 |
| References cited | 25 |
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
Analysis of covariance · Confounding · Covariate · Econometrics · Equating · Marginal structural model · Missing data · Propensity score matching · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Psychology · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
Counterfactuals and Causal Inference
Causality
Marginal Structural Models and Causal Inference in Epidemiology
Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.
Covariate Balancing Propensity Score
Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity‐score matched samples
Constructing Inverse Probability Weights for Marginal Structural Models
The Bioecological Model of Human Development
Remedial Education and Student Achievement
Time-Varying Treatments in Observational Studies
Covariate Selection in Propensity Scores Using Outcome Proxies
A Systematic Review of Propensity Score Methods in the Social Sciences
On the Success of Failure
Endogenous Selection Bias
| Unique citing works | 2 |
|---|---|
| Citations per year | 0.2 |
| Citation span | 2016 - 2020 (5) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |