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The Robustness of Multilevel Multipl...

Medhanie, Amanuel Gebri.

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The Robustness of Multilevel Multiple Imputation for Handling Missing Data in Hierarchical Linear Models.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The Robustness of Multilevel Multiple Imputation for Handling Missing Data in Hierarchical Linear Models./
作者:	Medhanie, Amanuel Gebri.
面頁冊數:	264 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.
Contained By:	Dissertation Abstracts International74-11B(E).
標題:	Psychology, Psychometrics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3589097
ISBN:	9781303275722

The Robustness of Multilevel Multiple Imputation for Handling Missing Data in Hierarchical Linear Models.
Medhanie, Amanuel Gebri.

The Robustness of Multilevel Multiple Imputation for Handling Missing Data in Hierarchical Linear Models. - 264 p.

Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.

Thesis (Ph.D.)--University of Minnesota, 2013.

Missing data often present problems for credible statistical analyses. Luckily there are valid methods for dealing with missing data but the context in which the data are missing can impact the performance of these methods. Relatively little is known about the proper way to handle missing data in multilevel data structures. This study used a Monte Carlo simulation to compare the performance of three missing data methods on multilevel data (multilevel multiple imputation, multiple imputation ignoring the multilevel structure, and listwise deletion). The comparison of these methods was made under conditions known or believed to influence both the performance of missing data methods and multilevel modeling. The results suggest that listwise deletion performs well compared to multilevel multiple imputation but multiple imputation ignoring the multilevel structure performed poorly. The implications of these results for educational research are discussed.

ISBN: 9781303275722Subjects--Topical Terms:

1017742
Psychology, Psychometrics.

The Robustness of Multilevel Multiple Imputation for Handling Missing Data in Hierarchical Linear Models.
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500 $a Adviser: Michael Harwell.
502 $a Thesis (Ph.D.)--University of Minnesota, 2013.
520 $a Missing data often present problems for credible statistical analyses. Luckily there are valid methods for dealing with missing data but the context in which the data are missing can impact the performance of these methods. Relatively little is known about the proper way to handle missing data in multilevel data structures. This study used a Monte Carlo simulation to compare the performance of three missing data methods on multilevel data (multilevel multiple imputation, multiple imputation ignoring the multilevel structure, and listwise deletion). The comparison of these methods was made under conditions known or believed to influence both the performance of missing data methods and multilevel modeling. The results suggest that listwise deletion performs well compared to multilevel multiple imputation but multiple imputation ignoring the multilevel structure performed poorly. The implications of these results for educational research are discussed.
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