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Author: Barbara L. Wolfe
Publisher:
ISBN:
Category : Missing observations (Statistics)
Languages : en
Pages : 38
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Book Description
Author: Barbara L. Wolfe
Publisher:
ISBN:
Category : Missing observations (Statistics)
Languages : en
Pages : 38
Get Book Here
Book Description
Author: Barbara L. Wolfe
Publisher:
ISBN:
Category : Missing observations (Statistics)
Languages : en
Pages : 46
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Book Description
Author: Barbara L. Wolfe
Publisher:
ISBN:
Category :
Languages : en
Pages : 38
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Book Description
Author: Meltem Alemdar
Publisher:
ISBN:
Category : Missing observations (Statistics)
Languages : en
Pages : 118
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Book Description
Unlike multilevel data with a purely nested structure, data that are cross-classified not only may be clustered into hierarchically ordered units but also may belong to more than one unit at a given level of a hierarchy. In a cross-classified design, students at a given school might be from several different neighborhoods and one neighborhood might have students who attend a number of different schools. In this type of scenario, schools and neighborhoods are considered to be cross-classified factors, and cross-classified random effects modeling (CCREM) should be used to analyze these data appropriately. A common problem in any type of multilevel analysis is the presence of missing data at any given level. There has been little research conducted in the multilevel literature about the impact of missing data, and none in the area of cross-classified models. The purpose of this study was to examine the effect of data that are missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), on CCREM estimates while exploring multiple imputation to handle the missing data. In addition, this study examined the impact of including an auxiliary variable that is correlated with the variable with missingness (the level-1 predictor) in the imputation model for multiple imputation. This study expanded on the CCREM Monte Carlo simulation work of Meyers (2004) by the inclusion of studying the effect of missing data and method for handling these missing data with CCREM. The results demonstrated that in general, multiple imputation met Hoogland and Boomsma's (1998) relative bias estimation criteria (less than 5% in magnitude) for parameter estimates under different types of missing data patterns. For the standard error estimates, substantial relative bias (defined by Hoogland and Boomsma as greater than 10%) was found in some conditions. When multiple imputation was used to handle the missing data then substantial bias was found in the standard errors in most cells where data were MNAR. This bias increased as a function of the percentage of missing data.
Author: Phyllis Lorena Garrett
Publisher:
ISBN: 9781109689013
Category : Educational evaluation
Languages : en
Pages : 137
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Book Description
The use of polytomous items in assessments has increased over the years, and as a result, the validity of these assessments has been a concern. Differential item functioning (dif) and missing data are two factors that may adversely affect assessment validity. Both factors have been studied separately, but dif and missing data are likely to occur simultaneously in real assessment situations. This study investigated the Type I error and power of several dif detection methods and methods of handling missing data for polytomous items generated under the partial credit model. The Type I error and power of the Mantel and ordinal logistic regression were compared using within-person mean substitution and multiple imputation when data were missing completely at random. In addition to assessing the Type I error and power of dif detection methods and methods of handling missing data, this study also assessed the impact of missing data on the effect size measure associated with the Mantel, the standardized mean difference effect size measure, and ordinal logistic regression, the R-squared effect size measure. Results indicated that the performance of the Mantel and ordinal logistic regression depended on the percent of missing data in the data set, the magnitude of dif, and the sample size ratio. The Type I error for both dif detection methods varied based on the missing data method used to impute the missing data. Power to detect dif increased as dif magnitude increased, but there was a relative decrease in power as the percent of missing data increased. Additional findings indicated that the percent of missing data, dif magnitude, and sample size ratio also influenced the effect size measures associated with the Mantel and ordinal logistic regression. The effect size values for both dif detection methods generally increased as dif magnitude increased, but as the percent of missing data increased, the effect size values decreased. [The dissertation citations contained here are published with the permission of ProQuest llc. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.].
Author: William Gregory Madow
Publisher:
ISBN:
Category : Sampling (Statistics).
Languages : en
Pages : 454
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Book Description
Author: Hyukje Kwon
Publisher:
ISBN:
Category :
Languages : en
Pages : 149
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Book Description
Listwise deletion tends to provide the largest RMSE on both fixed and random effects. The relative difference in the RMSE between listwise deletion and the other missing data treatments was substantially large with large proportion of missingness (PM=30%) and smaller sample sizes (N
Author: Patricia Berglund
Publisher: SAS Institute
ISBN: 162959203X
Category : Computers
Languages : en
Pages : 164
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Book Description
Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.
Author: Anne Catherine Black
Publisher:
ISBN:
Category :
Languages : en
Pages : 200
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Book Description
Author: Michael J. Daniels
Publisher: CRC Press
ISBN: 1420011189
Category : Mathematics
Languages : en
Pages : 324
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Book Description
Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ
