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Exploratory Data Analysis With Clustered Data: Simulation and Application With Oregon's Statewide Longitudinal Data System Using Generalized Linear Mixed-Effects Model Trees
Exploratory Data Analysis With Clustered Data: Simulation and Application With Oregon's Statewide Longitudinal Data System Using Generalized Linear Mixed-Effects Model Trees
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152009
- ISBN
- 9798383563168
- DDC
- 379.1
- 서명/저자
- Exploratory Data Analysis With Clustered Data: Simulation and Application With Oregons Statewide Longitudinal Data System Using Generalized Linear Mixed-Effects Model Trees
- 발행사항
- [Sl] : University of Oregon, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 218 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Zvoch, Keith.
- 학위논문주기
- Thesis (Ph.D.)--University of Oregon, 2024.
- 초록/해제
- 요약Simulations were conducted to establish best practice in hyperparameter optimization and accounting for clustering in Generalized Linear Mixed-Effects Model Trees (GLMM trees). Using data-driven best practices, the relationship between a 9th Grade On-Track to Graduate (9G-OTG) indicator and observed high school graduation within four years was explored. Data originated from two cohorts of the Oregon State Longitudinal Data System (SLDS) and were joined with external datasets. Restricted to complete cases, the data were comprised of more than 58,000 observations, each with more than 1500 variables measured at student, school, district, and zip code levels. GLMM trees explored heterogeneity in a cross-classified multilevel logistic regression which regressed observed graduation on 9G-OTG, accounting for variance in school- and zip-code-level random intercepts. Subgroups were identified for whom the probability of graduating among on- and-off track students were systematically heterogeneous, relative to the supraordinate group. Results suggest that for most students, 9G-OTG is a potent early warning indicator of graduation, but systematic variation in the indicator's effectiveness was found along all levels except district. Subgroups were defined by combinations of alternative schools, absences, transferring schools, being enrolled in more than one instructional program, neighborhood unemployment, and sex. Implications and recommendations to measurement, practice, and evaluation are discussed.
- 일반주제명
- Educational evaluation
- 일반주제명
- Statistics
- 일반주제명
- Education policy
- 일반주제명
- Computer science
- 일반주제명
- Information technology
- 키워드
- Graduation
- 기타저자
- University of Oregon Department of Education Studies
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798383563168
■035 ▼a(MiAaPQ)AAI31330961
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a379.1
■1001 ▼aLoan, Christopher M.▼0(orcid)0000-0002-1260-7868
■24510▼aExploratory Data Analysis With Clustered Data: Simulation and Application With Oregon's Statewide Longitudinal Data System Using Generalized Linear Mixed-Effects Model Trees
■260 ▼a[Sl]▼bUniversity of Oregon▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a218 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Zvoch, Keith.
■5021 ▼aThesis (Ph.D.)--University of Oregon, 2024.
■520 ▼aSimulations were conducted to establish best practice in hyperparameter optimization and accounting for clustering in Generalized Linear Mixed-Effects Model Trees (GLMM trees). Using data-driven best practices, the relationship between a 9th Grade On-Track to Graduate (9G-OTG) indicator and observed high school graduation within four years was explored. Data originated from two cohorts of the Oregon State Longitudinal Data System (SLDS) and were joined with external datasets. Restricted to complete cases, the data were comprised of more than 58,000 observations, each with more than 1500 variables measured at student, school, district, and zip code levels. GLMM trees explored heterogeneity in a cross-classified multilevel logistic regression which regressed observed graduation on 9G-OTG, accounting for variance in school- and zip-code-level random intercepts. Subgroups were identified for whom the probability of graduating among on- and-off track students were systematically heterogeneous, relative to the supraordinate group. Results suggest that for most students, 9G-OTG is a potent early warning indicator of graduation, but systematic variation in the indicator's effectiveness was found along all levels except district. Subgroups were defined by combinations of alternative schools, absences, transferring schools, being enrolled in more than one instructional program, neighborhood unemployment, and sex. Implications and recommendations to measurement, practice, and evaluation are discussed.
■590 ▼aSchool code: 0171.
■650 4▼aEducational evaluation
■650 4▼aStatistics
■650 4▼aEducation policy
■650 4▼aComputer science
■650 4▼aInformation technology
■653 ▼aGraduation
■653 ▼aHyperparameter optimization
■653 ▼aModel-based recursive partitioning
■653 ▼aMultilevel modeling
■653 ▼aState Longitudinal Data Systems
■690 ▼a0443
■690 ▼a0463
■690 ▼a0458
■690 ▼a0489
■690 ▼a0984
■71020▼aUniversity of Oregon▼bDepartment of Education Studies.
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0171
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162410▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
