missingdata

Missing data refers to the absence of observed values for variables in a dataset where a value would normally be recorded. Missingness can occur in any field and for any data type, and it may result from nonresponse, data entry errors, sensor failure, or participant attrition. It is important to distinguish missing values from zero, empty strings, or deliberately excluded categories.

Missing data are commonly described by their mechanism: missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR). MCAR means the likelihood of a value being missing is independent of any data, observed or unobserved. MAR means the probability of missingness depends on observed data but not on the missing values themselves. NMAR means missingness depends on the unobserved value that is missing, or on unobserved factors. Understanding the mechanism guides the choice of handling method, as MCAR and MAR can sometimes be addressed with appropriate techniques, while NMAR requires modeling of the missingness process.

The presence of missing data can reduce statistical power and bias estimates if not handled properly. Simple deletion methods, such as complete-case (listwise) analysis or pairwise deletion, can lead to biased results when data are not MCAR or to substantial loss of information. Imputation methods replace missing values with plausible substitutes. Single imputation methods include mean, median, regression, or hot-deck approaches, but they often overlook uncertainty. Multiple imputation creates several complete datasets by drawing from distributions for the missing values, analyzes each dataset, and pools results; this approach reflects uncertainty and is widely recommended, especially for MAR data. Techniques such as multivariate imputation by chained equations (MICE) or model-based imputation are popular.

Diagnostics and reporting are essential: assess missingness patterns, compare distributions before and after imputation, conduct sensitivity analyses for NMAR assumptions, and clearly document the amount of missing data and the methods used. Software tools across R, Python, SAS, SPSS, and Stata provide dedicated capabilities for handling missing data.