Yongnam Kim
Biographic Data
| ID | 4270478 |
|---|---|
| NAME | Yongnam Kim |
| GIVEN NAMES | Yongnam |
| FAMILY NAME | Kim |
| SIGNATURE | KIM Y |
| AFFILIATIONS | University of Wisconsin–Madison |
| ORCID | 0000-0001-6731-7123 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 5 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 2 |
What does “Asia–Pacific” mean to Aper? Toward high-quality scholarship and inclusive practices in international education
Causal research designs and analysis in education
Gain Scores Revisited
For misguided reasons, social scientists have long been reluctant to use gain scores for estimating causal effects. This article develops graphical models and graph-based arguments to show that gain score methods are a viable strategy for identifying causal treatment effects in observational studies. The proposed graphical models reveal that gain score methods rely on a bias-removing mechanism that is quite different to regular matching or covari…
Graphical Models for Quasi-experimental Designs
Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand and, thus, warrant a causal interpretation of the estimated effect. In this article, we discuss and com…
Identifying Causal Estimands for Time-Varying Treatments Measured with Time-Varying (Age or Grade-Based) Instruments
This commentary discusses causal estimands of same-age and same-grade comparisons for assessing grade-retention effects on student ability and performance. Using potential outcomes notation, we show that same-age and same-grade comparisons refer to different retention-promotion contrasts and therefore assess different causal questions. We also comment on deleting versus censoring records of students who dropped out of the study or do not belong t…
Gain Scores Revisited
For misguided reasons, social scientists have long been reluctant to use gain scores for estimating causal effects. This article develops graphical models and graph-based arguments to show that gain score methods are a viable strategy for identifying causal treatment effects in observational studies. The proposed graphical models reveal that gain score methods rely on a bias-removing mechanism that is quite different to regular matching or covari…
Graphical Models for Quasi-experimental Designs
Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand and, thus, warrant a causal interpretation of the estimated effect. In this article, we discuss and com…
Identifying Causal Estimands for Time-Varying Treatments Measured with Time-Varying (Age or Grade-Based) Instruments
This commentary discusses causal estimands of same-age and same-grade comparisons for assessing grade-retention effects on student ability and performance. Using potential outcomes notation, we show that same-age and same-grade comparisons refer to different retention-promotion contrasts and therefore assess different causal questions. We also comment on deleting versus censoring records of students who dropped out of the study or do not belong t…
Graphical Models for Quasi-experimental Designs
Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand and, thus, warrant a causal interpretation of the estimated effect. In this article, we discuss and com…
Gain Scores Revisited
For misguided reasons, social scientists have long been reluctant to use gain scores for estimating causal effects. This article develops graphical models and graph-based arguments to show that gain score methods are a viable strategy for identifying causal treatment effects in observational studies. The proposed graphical models reveal that gain score methods rely on a bias-removing mechanism that is quite different to regular matching or covari…
What does “Asia–Pacific” mean to Aper? Toward high-quality scholarship and inclusive practices in international education
Causal research designs and analysis in education
Advanced Causal Inference Techniques (4 works) · Causal inference (3 works) · Computer Science (3 works) · Econometrics (3 works) · Mathematics (3 works) · School Choice and Performance (3 works) · Statistics (3 works) · Economic growth (2 works) · Economics (2 works) · Higher education (2 works)