scholarly journals Regression Discontinuity Designs with an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English Language Learners

2021 ◽  
Author(s):  
Youmi Suk ◽  
Peter Steiner ◽  
Jee-Seon Kim ◽  
Hyunseung Kang
Keyword(s):  
English Language Learners ◽  
Language Learners ◽  
Treatment Effect ◽  
English Language ◽  
Regression Discontinuity ◽  
Average Treatment Effect ◽  
Extended Time ◽  
Treatment Assignment ◽  
Average Treatment ◽  
Regression Discontinuity Designs

Regression discontinuity designs are commonly used for program evaluation with continuous treatment assignment variables. But in practice, treatment assignment is frequently based on discrete or ordinal variables. In this study, we propose a regression discontinuity design with an ordinal running variable to assess the effects of extended time accommodations (ETA) for English language learners (ELL). ETA eligibility is determined by ordinal ELL English proficiency categories of National Assessment of Educational Progress data. We discuss the identification and estimation of the average treatment effect, intent-to-treat effect, and the local average treatment effect at the cutoff. We also propose a series of sensitivity analyses to probe the effect estimates' robustness to the choices of scaling functions and cutoff scores, and unmeasured confounding.

A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

2013 ◽  
Vol 1 (1) ◽  
pp. 135-154 ◽  
Author(s):  
Peter M. Aronow ◽  
Joel A. Middleton
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Covariate Adjustment ◽  
Randomized Experiments ◽  
Treatment Assignment ◽  
Unbiased Estimators ◽  
Average Treatment

AbstractWe derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

The Effects of the Initial English Language Learner Classification on Students’ Later Academic Outcomes

2017 ◽  
Vol 40 (2) ◽  
pp. 175-195 ◽  
Author(s):  
Nami Shin
Keyword(s):  
Academic Achievement ◽  
English Language Learners ◽  
Language Learners ◽  
English Language ◽  
Regression Discontinuity ◽  
Language Learner ◽  
Elementary Grades ◽  
Regression Discontinuity Design ◽  
State Laws ◽  
Positive Effects

Despite federal and state laws and regulations that require states and local districts to provide English language learners (ELLs) with support services, prior research has indicated that ELLs are, in general, lagging behind non-ELLs in academic achievement. An unanswered question is whether the initial designation of students as ELL influences their later academic achievement and how. Using a regression discontinuity design, this study compares the outcomes for students near the cutoff for being initially classified as ELL and initially fluent English proficient (IFEP) students. Among students near the cutoff, the classification had significant positive effects on ELLs’ academic achievement in elementary grades and, to some extent, in the later grades.

scholarly journals ON NONPARAMETRIC INFERENCE IN THE REGRESSION DISCONTINUITY DESIGN

2017 ◽  
Vol 34 (3) ◽  
pp. 694-703 ◽  
Author(s):  
Vishal Kamat
Keyword(s):  
Treatment Effect ◽  
Null Hypothesis ◽  
Regression Discontinuity ◽  
Nonparametric Tests ◽  
Average Treatment Effect ◽  
Nonparametric Inference ◽  
Regression Discontinuity Design ◽  
Average Treatment ◽  
Size Distortions

This paper studies the validity of nonparametric tests used in the regression discontinuity design. The null hypothesis of interest is that the average treatment effect at the threshold in the so-called sharp design equals a pre-specified value. We first show that, under assumptions used in the majority of the literature, for any test the power against any alternative is bounded above by its size. This result implies that, under these assumptions, any test with nontrivial power will exhibit size distortions. We next provide a sufficient strengthening of the standard assumptions under which we show that a version of a test suggested in Calonico, Cattaneo, and Titiunik (2014) can control limiting size.

scholarly journals Supporting English Language Learners' Experiences in the Classroom

2020 ◽  
Vol 5 ◽  
Author(s):  
Bailey Flynn ◽  
Class of 2020
Keyword(s):  
English Language Learners ◽  
Language Learners ◽  
English Language ◽  
Reading Aloud ◽  
After School ◽  
Fort Worth ◽  
Extended Time ◽  
Before School

I was interested in studying English Language Learners (ELL) and how they are supported.Thus, I looked into how each level (US, Texas, Fort Worth, and McLean 6th Grade Center) supports these students. The support given to English Language Learners at McLean 6th Grade Center is extended time, dictionaries, audio options, differentiated texts, reading aloud, before school help, during lunch help, and after school help. The teachers track English Language Learners’ progress separately and adjust accommodations each six weeks to make sure each student's needs are being met. Based on these supports, I developed a survey that was sent to my students at McLean 6th grade, asking for their opinions on the support they were receiving. This survey would have originally been given in class, but due to the COVID-19 outbreak, the survey took place online. This limited my number of responses. I sent the survey to all 170 students and 51 responded. I then drew conclusions from my research and the survey on the effectiveness of supports offered.  

scholarly journals A Generalized Regression-adjustment Estimator for Average Treatment Effects from Panel Data

2016 ◽  
Vol 16 (4) ◽  
pp. 826-836 ◽  
Author(s):  
David M. Drukker
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Potential Outcomes ◽  
Treatment Assignment ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Regression Adjustment ◽  
Simple Regression

I illustrate that the simple regression-adjustment estimator is inconsistent for the average treatment effect when the random effects affecting treatment assignment are correlated with the random effects that affect the potential outcomes. I present a simple parametric estimator that is consistent in this case.

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