In data analysis, “missing data” occurs when no scores are recorded for a variable in an observation. Data are often “missing” because governments or private entities choose not to report the information, or because the information is never recorded in the first place. Sometimes missing values are caused by the researcher’s inattention (e.g. mistakes in data entry) or bias.
While missing data are a common feature of every research project, they have profound (and often unacknowledged) consequences for the conclusions that can be validly drawn from an analysis. If whole cases are dropped for missing a score on a single variable or a potentially important independent variable is ignored because of real or imagined problems with data collection, we risk learning the wrong lessons from our analyses.
The Missing Data Depot is a newsletter and blog by Kevin Wallsten (Professor of Political Science at California State University, Long Beach) devoted to exploring what is left out of our quantitative and empirical discussions of American politics. The aim is to identify and discuss areas where our dominant political narratives blind us (either intentionally or unintentionally) to greater truths about our contemporary moment.
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