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Performance of Parametric Vs. Data Mining Methods for Estimating Propensity Scores with Multilevel Data: a Monte Carlo Study
Performance of Parametric Vs. Data Mining Methods for Estimating Propensity Scores with Multilevel Data: a Monte Carlo Study
- Material Type
- 학위논문
- 0015760656
- Date and Time of Latest Transaction
- 20210215113412
- ISBN
- 9798684669354
- DDC
- 371
- Author
- Fan, Meng.
- Title/Author
- Performance of Parametric Vs. Data Mining Methods for Estimating Propensity Scores with Multilevel Data: a Monte Carlo Study
- Publish Info
- [Sl] : University of Delaware, 2020
- Publish Info
- Ann Arbor : ProQuest Dissertations & Theses, 2020
- Material Info
- 158 p
- General Note
- Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
- General Note
- Advisor: May, Henry.
- 학위논문주기
- Thesis (Ph.D.)--University of Delaware, 2020.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Subject Added Entry-Topical Term
- Educational tests & measurements
- Subject Added Entry-Topical Term
- Statistics
- Subject Added Entry-Topical Term
- Educational technology
- Subject Added Entry-Topical Term
- Information science
- Index Term-Uncontrolled
- Data mining
- Index Term-Uncontrolled
- Monte Carlo simulations
- Index Term-Uncontrolled
- Multilevel data
- Index Term-Uncontrolled
- Propensity score analysis
- Index Term-Uncontrolled
- Mathematics education
- Index Term-Uncontrolled
- Student performance
- Added Entry-Corporate Name
- University of Delaware School of Education
- Host Item Entry
- Dissertations Abstracts International. 82-05B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- 소장사항
-
202102 2021
- Control Number
- joongbu:590205
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