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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 Mu...
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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  
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소장사항  
202102 2021
Control Number  
joongbu:590205
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