Investigating the Relationship Between English Proficiency and Mathematical Abilities: Empirical Likelihood Inference for Nonparametric Regression with Clustered Data.
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| Title: | Investigating the Relationship Between English Proficiency and Mathematical Abilities: Empirical Likelihood Inference for Nonparametric Regression with Clustered Data. |
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| 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193123992 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Investigating the Relationship Between English Proficiency and Mathematical Abilities: Empirical Likelihood Inference for Nonparametric Regression with Clustered Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Premathilaka%2C+Methsarani%22">Premathilaka, Methsarani</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Rong%22">Liu, Rong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> rong.liu@utoledo.edu</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Fei%22">Gao, Fei</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Measurement%22">Measurement</searchLink>. Apr-Jun2026, Vol. 24 Issue 2, p85-95. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Language+ability%22">Language ability</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+ability%22">Mathematical ability</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Nonparametric+statistics%22">Nonparametric statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Inferential+statistics%22">Inferential statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Panel+analysis%22">Panel analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>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.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/15366367.2024.2417171 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 85 Subjects: – SubjectFull: Language ability Type: general – SubjectFull: Mathematical ability Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Academic achievement Type: general – SubjectFull: Nonparametric statistics Type: general – SubjectFull: Inferential statistics Type: general – SubjectFull: Panel analysis Type: general Titles: – TitleFull: Investigating the Relationship Between English Proficiency and Mathematical Abilities: Empirical Likelihood Inference for Nonparametric Regression with Clustered Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Premathilaka, Methsarani – PersonEntity: Name: NameFull: Liu, Rong – PersonEntity: Name: NameFull: Gao, Fei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr-Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15366367 Numbering: – Type: volume Value: 24 – Type: issue Value: 2 Titles: – TitleFull: Measurement Type: main |
| ResultId | 1 |
