A Monte Carlo Investigation of Missing Data in Multiple Regression Analysis PDF Download
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Author: Sanford Alan Britton
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 96
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Author: Sanford Alan Britton
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 96
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Book Description
Author: Dwight H. Newsome
Publisher:
ISBN:
Category : Missing observations (Statistics)
Languages : en
Pages : 390
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Book Description
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: Ellen Storey Vasu
Publisher:
ISBN: 9780891430339
Category : Multicollinearity
Languages : en
Pages : 98
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Author: Duan Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Due to its increasing popularity, hierarchical linear modeling (HLM) has been used along with structural equation modeling (SEM) to analyze data with nested structure. In spite of the extensive research on commonly encountered problems such as violation of normality and missing data treatment within the framework of SEM, these areas have been much less explored in HLM. The present study compared HLM and multilevel SEM through a Monte Carlo study from the perspectives of the influence of nonnormality and performance of multiple imputation based on the expectation maximization (EM) algorithm under various combinations of sample sizes at two levels. The statistical power, parameter estimates, standard errors, and estimation bias for the main effects and cross-level interaction in a two- level model were compared across the four design factors: analysis method, normality condition, missing data proportion, and sample size. HLM and multilevel SEM appeared to have similar power detecting the main effect, while HLM had better power for the cross- level interaction. Neither seemed to be sensitive to violation of the normality assumption. A higher proportion of missing data resulted in larger standard errors and estimation bias. Sample sizes at both the individual and cluster levels played a role in the statistical power for parameter estimates. The two-way interactions for the four factors were generally nonzero. Overall, both HLM and multilevel SEM were quite robust to violation of normality. SEM appears more useful in more complex path models while HLM is superior in detecting main effects. Multiple imputation based on the EM algorithm performed well in producing stable parameter estimates for up to 30% missing data. Sample size design should take into account the level at which the research is most focused.
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: Jeffrey Thomas McLeod
Publisher:
ISBN:
Category :
Languages : en
Pages : 638
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Book Description
Author: Lawrence S. Meyers
Publisher: SAGE
ISBN: 9781412904124
Category : Mathematics
Languages : en
Pages : 768
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Book Description
Multivariate designs were once the province of the very few exalted researchers who understood the underlying advanced mathematics. Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioural sciences are exposed to the complex multivariate statistical techniques without having to learn the mathematical computations needed to acquire the data output. These students - in psychology, education, political science, etc. - will never be statisticians and appropriately so, their preparation and coursework reflects less of an emphasis on the mathematical complexities of multivariate statistics and more on the analysis and the interpretation of the methods themselves and the actual data output. This book provides full coverage of the wide range of multivariate topics in a conceptual, rather than mathematical, approach. The author gears toward the needs, level of sophistication, and interest in multivariate methodology of students in applied areas that need to focus on design and interpretation rather than the intricacies of specific computations. The book includes: - Coverage of the most widely used multivariate designs: multiple regression, exploratory factor analysis, MANOVA, and structural equation modeling. - Integrated SPSS examples for hands-on learning from one large study (for consistency of application throughout the text). - Examples of written results to enable students to learn how the results of these procedures are communicated. - Practical application of the techniques using contemporary studies that will resonate with students.
Author: Barbara L. Wolfe
Publisher:
ISBN:
Category : Missing observations (Statistics)
Languages : en
Pages : 46
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Author: Pao-Kuei Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 266
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Book Description
