Investigating the Relationship Between English Proficiency and Mathematical Abilities: Empirical Likelihood Inference for Nonparametric Regression with Clustered Data.

Saved in:
Bibliographic Details
Title: Investigating the Relationship Between English Proficiency and Mathematical Abilities: Empirical Likelihood Inference for Nonparametric Regression with Clustered Data.
Authors: Premathilaka, Methsarani1 (AUTHOR), Liu, Rong2 (AUTHOR) rong.liu@utoledo.edu, Gao, Fei3 (AUTHOR)
Source: Measurement. Apr-Jun2026, Vol. 24 Issue 2, p85-95. 11p.
Subjects: Language ability, Mathematical ability, Simulation methods & models, Academic achievement, Nonparametric statistics, Inferential statistics, Panel analysis
Abstract: The use of repeated or clustered data is gaining popularity in numerous research studies. Although Wald type SCBs were available, the estimation of standard errors poses a challenge in nonparametric models, making it difficult to estimate the standard errors. In this paper, we focus on providing SCBs using empirical likelihood (EL) when repeated measures have the same cluster size. The performance of the EL-based SCBs is evaluated by simulation studies which support the asymptotic properties. The proposed method is applied to analyze data collected over 3 years from 886 students. The findings indicate a notable association between the students' English proficiency and their mathematical abilities. [ABSTRACT FROM AUTHOR]
Copyright of Measurement is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
Description
Abstract:The use of repeated or clustered data is gaining popularity in numerous research studies. Although Wald type SCBs were available, the estimation of standard errors poses a challenge in nonparametric models, making it difficult to estimate the standard errors. In this paper, we focus on providing SCBs using empirical likelihood (EL) when repeated measures have the same cluster size. The performance of the EL-based SCBs is evaluated by simulation studies which support the asymptotic properties. The proposed method is applied to analyze data collected over 3 years from 886 students. The findings indicate a notable association between the students' English proficiency and their mathematical abilities. [ABSTRACT FROM AUTHOR]
ISSN:15366367
DOI:10.1080/15366367.2024.2417171