The paper discusses the impact of missing data on quantitative research, emphasizing the need for principled methods to handle missing values. It reviews three such methods: multiple imputation (MI), full information maximum likelihood (FIML), and the expectation-maximization (EM) algorithm. These methods are applied to a real-world dataset, and their results are compared with those from complete data and listwise deletion (LD). The paper highlights the importance of statistical assumptions and provides recommendations for researchers, including the explicit acknowledgment of missing data, the use of principled methods, and incorporating appropriate treatment into manuscript review standards. The discussion covers the terminology, mechanisms, and patterns of missing data, as well as the detailed steps and considerations for each method. The paper concludes by emphasizing the importance of statistical assumptions and providing practical guidelines for researchers.The paper discusses the impact of missing data on quantitative research, emphasizing the need for principled methods to handle missing values. It reviews three such methods: multiple imputation (MI), full information maximum likelihood (FIML), and the expectation-maximization (EM) algorithm. These methods are applied to a real-world dataset, and their results are compared with those from complete data and listwise deletion (LD). The paper highlights the importance of statistical assumptions and provides recommendations for researchers, including the explicit acknowledgment of missing data, the use of principled methods, and incorporating appropriate treatment into manuscript review standards. The discussion covers the terminology, mechanisms, and patterns of missing data, as well as the detailed steps and considerations for each method. The paper concludes by emphasizing the importance of statistical assumptions and providing practical guidelines for researchers.