![]() ![]() If any variables have high percentages of missingness, you may want to exclude them from -especially- multivariate analyses. Since we've 464 cases in total, (464 - N) is the number of missing values per variable. The N column shows the number of non missing values per variable. *Note: (464 - N) = number of missing values. The easiest way for doing so is running the syntax below. Make sure the output tables show both values and value labels. User Missing Values for Categorical VariablesĪ quick way for inspecting categorical variables is running frequency distributions and corresponding bar charts. Let's now see if any values should be set as user missing and how to do so.
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