Missing Data
Missing data refers to the absence of values in a dataset. This can occur due to various reasons such as data entry errors, equipment malfunction, or survey non-response. Missing data can have a significant impact on the analysis and interpretation of data, as it can lead to biased or inaccurate results. There are several methods to handle missing data, including deletion, imputation, and multiple imputation. Deletion involves removing observations with missing values, which can lead to a loss of information and reduced statistical power. Imputation involves estimating missing values based on the available data, which can be done using various techniques such as mean imputation, regression imputation, and k-nearest neighbor imputation. Multiple imputation involves creating multiple imputed datasets and combining the results to obtain more accurate estimates and standard errors. The choice of method depends on the amount and pattern of missing data, as well as the research question and assumptions made about the missing data mechanism.
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