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dc.creatorAsiri, Zahra
dc.date.accessioned2021-01-15T17:43:10Z
dc.date.available2021-01-15T17:43:10Z
dc.date.created2020-12
dc.date.issued2020-09-08
dc.date.submittedDecember 2020
dc.identifier.urihttps://hdl.handle.net/11274/12582
dc.description.abstractThis study is about missing data mechanisms developed by Rubin (1976), including missing data completely at random, missing data at random, and missing data not at random. This study utilizes a scenario at King Khaled University where potential employees complete a Post-Graduate General Aptitude Test (PGGA) to represent techniques for handling missing data. There are both traditional methods of handling missing data and modern methods that are more sophisticated for subsequent analyses and offer specific advantages. This study will go through the process of imputing data to understand how to deal with missing data depending on the missing data mechanism. This study concludes by providing recommendations for handling missing data primarily through regression imputation and multiple imputation, which are exemplified through the researcher’s simulated data related to the PGGA and job performance. Strengths and limitations of different techniques are discussed.
dc.format.mimetypeapplication/pdf
dc.subjectMissing data
dc.subjectMissing data mechanism
dc.subjectMissing data completely at random
dc.subjectMissing data at random
dc.subjectMissing data not at random
dc.subjectRegression imputation
dc.subjectMultiple imputation.
dc.titleDealing with missing data in the work environment at King Khalid University
dc.typeThesis
dc.date.updated2021-01-15T17:43:12Z
thesis.degree.departmentMathematics and Computer Sciences
thesis.degree.disciplineMathematics
thesis.degree.grantorTexas Woman's University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
dc.contributor.committeeMemberFalley, Brandi
dc.contributor.committeeMemberHamner , Mark
dc.contributor.committeeMemberE. Edwards, Don
dc.contributor.committeeMemberDemuynck, Marie
dc.type.materialtext


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