東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

The performance of missing data trea...

Adachi, Eishi.

FindBook

Google Book

Amazon

博客來

The performance of missing data treatments for longitudinal data with a time-varying covariate.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The performance of missing data treatments for longitudinal data with a time-varying covariate./
作者:	Adachi, Eishi.
面頁冊數:	191 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0342.
Contained By:	Dissertation Abstracts International67-01B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3205968
ISBN:	9780542519758

The performance of missing data treatments for longitudinal data with a time-varying covariate.
Adachi, Eishi.

The performance of missing data treatments for longitudinal data with a time-varying covariate. - 191 p.

Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0342.

Thesis (Ph.D.)--The University of Texas at Austin, 2006.

The purpose of this study was to investigate the performance of missing data treatments for longitudinal data with a time-varying covariate. For a longitudinal study, data are collected repeatedly from the same individual, and the occurrence of missing observations due to attrition is common. When longitudinal data are nested within individuals, missing data treatments may need to take the nested data structures into account.

ISBN: 9780542519758Subjects--Topical Terms:

517247
Statistics.

The performance of missing data treatments for longitudinal data with a time-varying covariate.
LDR:03385nmm 2200337 4500 001 1821983
005 20061115130116.5
008 130610s2006 eng d
020 $a 9780542519758
035 $a (UnM)AAI3205968
035 $a AAI3205968
040 $a UnM $c UnM
100 1 $a Adachi, Eishi. $3 1911144
245 1 4 $a The performance of missing data treatments for longitudinal data with a time-varying covariate.
300 $a 191 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0342.
500 $a Adviser: Keenan A. Pituch.
502 $a Thesis (Ph.D.)--The University of Texas at Austin, 2006.
520 $a The purpose of this study was to investigate the performance of missing data treatments for longitudinal data with a time-varying covariate. For a longitudinal study, data are collected repeatedly from the same individual, and the occurrence of missing observations due to attrition is common. When longitudinal data are nested within individuals, missing data treatments may need to take the nested data structures into account.
520 $a This study compared the performance of four missing data treatments: listwise deletion and three multiple imputation methods. Nested data structures were ignored in single-level multiple imputation. On the other hand, multivariate multiple imputation addressed nested data structures by data manipulation and multilevel multiple imputation addressed them in the imputation model. The performance of listwise deletion was investigated to compare the performance of multiple imputation methods with a traditional method.
520 $a In Study 1, longitudinal data with missing observations were simulated. The experimental conditions were sample size, missing rate, missing patterns, and degree of systematic nonresponse. After missing data were treated by four missing data treatments, a two-level mixed-effects model was applied. Bias in estimation of the regression coefficients, standard errors, and variances were investigated. In Study 2, the four missing data treatments were applied to empirical data from a longitudinal study on persons with multiple sclerosis, to demonstrate the applicability of the four missing data treatments.
520 $a Study 1 showed that multivariate multiple imputation and multilevel multiple imputation resulted in less biased estimates than the other two methods for most study conditions. Single-level multiple imputation resulted in biased variance estimates under all experimental conditions. Listwise deletion also produced biased estimates, especially for the standard error for the fixed effects of time invariant variables.
520 $a With the application of empirical data, inferences of cross-level interaction fixed-effects were in disagreement among the four methods. The use of multivariate multiple imputation and multilevel multiple imputation found a significant cross-level interaction whereas the other methods did not. The results showed that nested structures should not be ignored at the imputation stage of multiple imputation.
590 $a School code: 0227.
650 4 $a Statistics. $3 517247
650 4 $a Health Sciences, Nursing. $3 1017798
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0463
690 $a 0569
690 $a 0308
710 2 0 $a The University of Texas at Austin. $3 718984
773 0 $t Dissertation Abstracts International $g 67-01B.
790 1 0 $a Pituch, Keenan A., $e advisor
790 $a 0227
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3205968