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Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs

This article develops design-based ratio estimators for clustered, blocked randomized controlled trials (RCTs), with an application to a federally funded, school-based RCT testing the effects of behavioral health interventions. We consider finite population weighted least-square estimators for average treatment effects (ATEs), allowing for general weighting schemes and covariates. We consider models with block-by-treatment status interactions as well as restricted models with block indicators only. We prove new finite population central limit theorems for each block specification. We also discuss simple variance estimators that share features with commonly used cluster-robust standard error estimators. Simulations show that the design-based ATE estimator yields nominal rejection rates with standard errors near true ones, even with few clusters.

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Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.540 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2135-2146

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

RANDOMIZED CONTROLLED TRIALS
DESIGN-BASED ESTIMATORS
BLOCKED DESIGNS
CLUSTERED DESIGNS
FINITE POPULATION CENTRAL LIMIT THEOREMS

Info Detail Spesifik

https://doi.org/10.1080/01621459.2021.1906685

Pernyataan Tanggungjawab

Peter Z. Schochet, Nicole E. Pashley, Luke W. Miratrix,

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