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Weighting in Multilevel Models
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Weighting in Multilevel Models. |
| 개인저자 | Tong, Bing. |
| 단체저자명 | Michigan State University. Measurement and Quantitative Methods - Doctor of Philosophy. |
| 발행사항 | [S.l.]: Michigan State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 100 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781088388877 |
| 학위논문주기 | Thesis (Ph.D.)--Michigan State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Kelly, Kimberly S. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Large-scale survey programs usually use complex sampling designs such as unequal probabilities of selection, stratifications, and/or clustering to collect data to save time and money. This leads to the necessity to incorporate sampling weights into multilevel models in order to obtain accurate estimates and valid inferences. However, the weighted multilevel estimators have been lately developed and minimal guidance is left on how to use sampling weights in multilevel models and which estimator is most appropriate.The goal of this study is to examine the performance of multilevel pseudo maximum likelihood (MPML) estimation methods using different scaling techniques under the informative and non-informative condition in the context of a two-stage sampling design with unequal probabilities of selection. Monte Carlo simulation methods are used to evaluate the impact of three factors, including informativeness of the sampling design, intraclass correlation coefficient (ICC), and estimation methods. Simulation results indicate that including sampling weights in the model still produce biased estimates for the school-level variance. In general, the weighted methods outperform the unweighted method in estimating intercept and student-level variance while the unweighted method outperforms the weighted methods for school-level variance estimation in the informative condition. In general, the cluster scaling estimation method is recommended in the informative sampling design. Under the non-informative condition, the unweighted method can be considered a better choice than the weighted methods for all the parameter estimates. Besides, the ICC has obvious effects on school-level variance estimates in the informative condition, but in the noninformative condition, it also affects intercept estimates. An empirical study is included to illustrate the model. |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
