Missing data — mechanisms and possible solutions / Datos ausentes: mecanismos y posibles soluciones

Abstract One of the most common problems facing empirical researchers is when a portion of the data is missing. We will review three different types of ‘missingness’, namely missing completely at random, missing at random and missing not at random, and we will discuss how missing data can affect data analysis. We review methods to deal with missing data, including the simple ‘complete-case analysis’ approach, in which we only use the observations in the data set for which all the data is available, and the more sophisticated ‘multiple imputation’ approach, in which we repeat the analysis using multiple (completed) copies of the data set, and obtain the estimates of interest by averaging across all analyses. We will demonstrate how to implement solutions to missing data and review the limitations of the methods.