Using Gaussian Process Regression in Two-Dimensional Regression Discontinuity Designs. EdWorkingPaper No. 24-1043
Saved in:
| Title: | Using Gaussian Process Regression in Two-Dimensional Regression Discontinuity Designs. EdWorkingPaper No. 24-1043 |
|---|---|
| Language: | English |
| Authors: | Lily An, Zach Branson, Luke Miratrix, Annenberg Institute for School Reform at Brown University |
| Source: | Annenberg Institute for School Reform at Brown University. 2024. |
| Availability: | Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: AISR_Info@brown.edu; Web site: http://www.annenberginstitute.org |
| Peer Reviewed: | N |
| Page Count: | 52 |
| Publication Date: | 2024 |
| Document Type: | Reports - Research |
| Education Level: | High Schools Secondary Education Higher Education Postsecondary Education |
| Descriptors: | Hierarchical Linear Modeling, English Language Learners, English (Second Language), Second Language Learning, Educational Policy, Public Schools, High Schools, Classification, Language Tests, Scores, College Entrance Examinations |
| Geographic Terms: | Wisconsin |
| Assessment and Survey Identifiers: | ACT Assessment |
| Abstract: | Sometimes a treatment, such as receiving a high school diploma, is assigned to students if their scores on two inputs (e.g., math and English test scores) are above established cutoffs. This forms a multidimensional regression discontinuity design (RDD) to analyze the effect of the educational treatment where there are two running variables instead of one. Present methods for estimating such designs either collapse the two running variables into a single running variable, estimate two separate one-dimensional RDDs, or jointly model the entire response surface. The first two approaches may lose valuable information, while the third approach can be very sensitive to model misspecification. We examine an alternative approach, developed in the context of geographic RDDs, which uses Gaussian processes to flexibly model the response surfaces and estimate the impact of treatment along the full range of students that were on the margin of receiving treatment. We demonstrate theoretically, in simulation, and in an applied example, that this approach has several advantages over current approaches, including over another nonparametric surface response method. In particular, using Gaussian process regression in two-dimensional RDDs shows strong coverage and standard error estimation, and allows for easy examination of treatment effect variation for students with different patterns of running variables and outcomes. As these nonparametric approaches are new in education-specific RDDs, we also provide an R package for users to estimate treatment effects using Gaussian process regression. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | ED661553 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED661553 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
|---|---|
| Header | DbId: eric DbLabel: ERIC An: ED661553 AccessLevel: 3 PubType: Report PubTypeId: report PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Using Gaussian Process Regression in Two-Dimensional Regression Discontinuity Designs. EdWorkingPaper No. 24-1043 – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lily+An%22">Lily An</searchLink><br /><searchLink fieldCode="AR" term="%22Zach+Branson%22">Zach Branson</searchLink><br /><searchLink fieldCode="AR" term="%22Luke+Miratrix%22">Luke Miratrix</searchLink><br /><searchLink fieldCode="AR" term="%22Annenberg+Institute+for+School+Reform+at+Brown+University%22">Annenberg Institute for School Reform at Brown University</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Annenberg+Institute+for+School+Reform+at+Brown+University%22"><i>Annenberg Institute for School Reform at Brown University</i></searchLink>. 2024. – Name: Avail Label: Availability Group: Avail Data: Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: AISR_Info@brown.edu; Web site: http://www.annenberginstitute.org – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: N – Name: Pages Label: Page Count Group: Src Data: 52 – Name: DatePubCY Label: Publication Date Group: Date Data: 2024 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Hierarchical+Linear+Modeling%22">Hierarchical Linear Modeling</searchLink><br /><searchLink fieldCode="DE" term="%22English+Language+Learners%22">English Language Learners</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Policy%22">Educational Policy</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Schools%22">Public Schools</searchLink><br /><searchLink fieldCode="DE" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Tests%22">Language Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22College+Entrance+Examinations%22">College Entrance Examinations</searchLink> – Name: Subject Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Wisconsin%22">Wisconsin</searchLink> – Name: SubjectThesaurus Label: Assessment and Survey Identifiers Group: Su Data: <searchLink fieldCode="SU" term="%22ACT+Assessment%22">ACT Assessment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Sometimes a treatment, such as receiving a high school diploma, is assigned to students if their scores on two inputs (e.g., math and English test scores) are above established cutoffs. This forms a multidimensional regression discontinuity design (RDD) to analyze the effect of the educational treatment where there are two running variables instead of one. Present methods for estimating such designs either collapse the two running variables into a single running variable, estimate two separate one-dimensional RDDs, or jointly model the entire response surface. The first two approaches may lose valuable information, while the third approach can be very sensitive to model misspecification. We examine an alternative approach, developed in the context of geographic RDDs, which uses Gaussian processes to flexibly model the response surfaces and estimate the impact of treatment along the full range of students that were on the margin of receiving treatment. We demonstrate theoretically, in simulation, and in an applied example, that this approach has several advantages over current approaches, including over another nonparametric surface response method. In particular, using Gaussian process regression in two-dimensional RDDs shows strong coverage and standard error estimation, and allows for easy examination of treatment effect variation for students with different patterns of running variables and outcomes. As these nonparametric approaches are new in education-specific RDDs, we also provide an R package for users to estimate treatment effects using Gaussian process regression. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2024 – Name: AN Label: Accession Number Group: ID Data: ED661553 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED661553 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 52 Subjects: – SubjectFull: Hierarchical Linear Modeling Type: general – SubjectFull: English Language Learners Type: general – SubjectFull: English (Second Language) Type: general – SubjectFull: Second Language Learning Type: general – SubjectFull: Educational Policy Type: general – SubjectFull: Public Schools Type: general – SubjectFull: High Schools Type: general – SubjectFull: Classification Type: general – SubjectFull: Language Tests Type: general – SubjectFull: Scores Type: general – SubjectFull: College Entrance Examinations Type: general – SubjectFull: Wisconsin Type: general – SubjectFull: ACT Assessment Type: general Titles: – TitleFull: Using Gaussian Process Regression in Two-Dimensional Regression Discontinuity Designs. EdWorkingPaper No. 24-1043 Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Annenberg Institute for School Reform at Brown University – PersonEntity: Name: NameFull: Lily An – PersonEntity: Name: NameFull: Zach Branson – PersonEntity: Name: NameFull: Luke Miratrix IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Type: published Y: 2024 Titles: – TitleFull: Annenberg Institute for School Reform at Brown University Type: main |
| ResultId | 1 |
