A New Method for Estimating Teacher Value-Added

Gilraine, Michael; Gu, Jiaying; McMillan, R. S.

doi:10.2139/ssrn.3592171

Cited by 2 publications

(6 citation statements)

References 51 publications

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“…Our second simulation setting is based on a discrete approximation of the data structure employed in Gilraine, Gu, and McMillan (2020) to study teacher value‐addded methods. Several longitudinal waves of student test scores from the Los Angeles Unified School District were combined in this study.…”

Section: Simulation Evidencementioning

confidence: 99%

“…Our approach is thus more closely aligned to that of Kline and Walters (2021), who studied decision rules for assessing employer discrimination from experiments involving fictitious job applications using closely related GMM methods for binomial mixture models. Gilraine, Gu, and McMillan (2020) studied teacher value-added estimation employing nonparametric maximum likelihood methods for estimating Gaussian mixture models as we advocate below. Their analysis of data from both North Carolina and Los Angeles illustrates the advantages of more flexible mixture models for latent value added.…”

Section: Introductionmentioning

confidence: 99%

“…Gilraine, Gu, and McMillan (2020) studied teacher value‐added estimation employing nonparametric maximum likelihood methods for estimating Gaussian mixture models as we advocate below. Their analysis of data from both North Carolina and Los Angeles illustrates the advantages of more flexible mixture models for latent value added.…”

Section: Introductionmentioning

confidence: 99%

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Invidious Comparisons: Ranking and Selection as Compound Decisions

Koenker

2023

ECTA

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There is an innate human tendency, one might call it the “league table mentality,” to construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are ranked even though their inherent multi‐dimensionality would suggest that—at best—only partial orderings were possible. We consider a large class of elementary ranking problems in which we observe noisy, scalar measurements of merit for n objects of potentially heterogeneous precision and are asked to select a group of the objects that are “most meritorious.” The problem is naturally formulated in the compound decision framework of Robbins's (1956) empirical Bayes theory, but it also exhibits close connections to the recent literature on multiple testing. The nonparametric maximum likelihood estimator for mixture models (Kiefer and Wolfowitz (1956)) is employed to construct optimal ranking and selection rules. Performance of the rules is evaluated in simulations and an application to ranking U.S. kidney dialysis centers.

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Section: Simulation Evidencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Invidious Comparisons: Ranking and Selection as Compound Decisions

Koenker

2023

ECTA

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“…A Teacher Value-Added Setting. Our second simulation setting is based on a discrete approximation of the data structure employed in Gilraine, Gu, and McMillan (2020) to study teacher value-addded methods. Several longitudinal waves of student test scores from the Los Angeles Unified School District were combined in this study.…”

Section: 2mentioning

confidence: 99%

“…Here we abstract from many features of the full longitudinal structure of this data, and focus instead on simulating performance of several selection methods. We maintain our standard known variance model in which we observe Y i ∼ N (θ i , σ 2 i ) with θ i 's drawn iidly from a distribution G estimated by Gilraine, Gu, and McMillan (2020). This distribution was estimated from the full longitudinal LA sample using the nonparametric maximum likelihood estimator of Kiefer and Wolfowitz and then smoothed slightly by convolution with a biweight kernel and illustrated in the left panel of Figure 7.2.…”

Section: 2mentioning

confidence: 99%

Invidious Comparisons: Ranking and Selection as Compound Decisions

Gu¹,

Koenker²

2020

Preprint

Self Cite

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There is an innate human tendency, one might call it the "league table mentality," to construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are ranked even though their inherent multi-dimensionality would suggest that -at best -only partial orderings were possible. We consider a large class of elementary ranking problems in which we observe noisy, scalar measurements of merit for n objects of potentially heterogeneous precision and are asked to select a group of the objects that are "most meritorious." The problem is naturally formulated in the compound decision framework of Robbins's (1956) empirical Bayes theory, but it also exhibits close connections to the recent literature on multiple testing. The nonparametric maximum likelihood estimator for mixture models (Kiefer and Wolfowitz (1956)) is employed to construct optimal ranking and selection rules. Performance of the rules is evaluated in simulations and an application to ranking U.S kidney dialysis centers.

show abstract

A New Method for Estimating Teacher Value-Added

Cited by 2 publications

References 51 publications

Invidious Comparisons: Ranking and Selection as Compound Decisions

Invidious Comparisons: Ranking and Selection as Compound Decisions

Invidious Comparisons: Ranking and Selection as Compound Decisions

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