2021-01-152021-01-152020-129/8/202020-Dechttps://hdl.handle.net/11274/12582This 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.application/pdfMissing dataMissing data mechanismMissing data completely at randomMissing data at randomMissing data not at randomRegression imputationMultiple imputation.Thesis2021-01-15