Missing data patterns are the combinations in which the variables with missing values occur. Exploring these patterns in multivariate data can be very useful but there are few specialized tools. The current paper presents a plot that includes relevant information for visualizing these patterns. The plot is also dynamic-interactive; so, selecting elements in it permits the highlighting of those that are more relevant according to certain criteria. An example, based on college data, is used for the purposes of illustrating the capabilities of the plot. Accessed 184 times on https://pareonline.net from November 21, 2019 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
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Valero-Mora, Pedro; Rodrigo, Maria F.; Sanchez, Mar; and SanMartin, Jaime
"A Plot for the Visualization of Missing Value Patterns in Multivariate Data,"
Practical Assessment, Research, and Evaluation: Vol. 24
, Article 9.
Available at: https://scholarworks.umass.edu/pare/vol24/iss1/9