Abstract and Keywords
The inevitability and importance of missing data ought to move researchers to prevent, treat, and report the condition. Unfortunately, despite great advances in the field, researchers tend to ignore missing data. We hypothesize that ignoring missing data stems from low interest, unavailable solutions, and higher priorities by most social scientists. Thus, we aimed to remedy those potential mechanisms by providing a clear demonstration of missing-data handling in three distinct data analysis scenarios (psychometric, longitudinal, and covariance models) using R. Each of these exemplar procedures comes with code and data allowing readers to replicate and extend our examples to their own data. By demonstrating the use of missing-data—handling techniques in a freely available statistical package (R), we hope to increase available options and reduce the researcher's burden for handling missing data in common social science data analytic scenarios.
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