Abstract:Missing data occurs in every field and most researchers choose simple approach to deal with. But this approach may introduce bias and result in inaccurate results. In this study, we will explore the method suitable for large sample and multivariate missing data patterns. In this paper, we utilized a cross-sectional survey data, providing information about youth health risk behavior in Beijing. Using R to simulate random missing data sets with different proportion of missing data based on the survey data set. F… Show more
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