Advances in Social Behavior Research

Advances in Social Behavior Research

Vol. 7, 29 April 2024


Open Access | Article

Assessing the Causal Effect of Special Education Services on Math Achievement: A Causal Inference and Machine Learning Study

Liangbang Li * 1
1 The Chinese University of Hong Kong

* Author to whom correspondence should be addressed.

Advances in Social Behavior Research, Vol. 7, 20-27
Published 29 April 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Liangbang Li. Assessing the Causal Effect of Special Education Services on Math Achievement: A Causal Inference and Machine Learning Study. ASBR (2024) Vol. 7: 20-27. DOI: 10.54254/2753-7102/7/2024055.

Abstract

This study aims to assess the Average Treatment Effect (ATE) of receiving special education services on revised Item Response Theory (IRT) scaled math achievement test scores. By employing a methodological repertoire comprising linear regression with ordinary least squares (OLS), propensity score matching (PSM), Bayesian Additive Regression Trees (BART), and Multilayer Perceptron (MLP), we examine the impact of these interventions. Leveraging data from the Early Childhood Longitudinal Study Kindergarten 2010-11 cohort (ECLS-K:2011), we systematically analyze the ATE of special education services on students' math achievement. The results show that all models yield negative ATE results, suggesting a deleterious effect of special education services on fifth-grade math scores. Furthermore, we employ Principal Component Analysis (PCA) to corroborate these findings, aligning with outcomes obtained from causal inference and Machine Learning (ML) based methods. This research emphasizes the importance of method diversity in educational research and highlights the need for assessments of intervention effectiveness to help educational practices and policies.

Keywords

causal inference, machine learning, early childhood longitudinal study kindergarten (ECLS-K), average treatment effect (ATE)

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
ISBN (Print)
ISBN (Online)
Published Date
29 April 2024
Series
Advances in Social Behavior Research
ISSN (Print)
2753-7102
ISSN (Online)
2753-7110
DOI
10.54254/2753-7102/7/2024055
Copyright
29 April 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated