SPSS Data Preparation 15.0 Manual by SPSS

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Missing values of continuous variables are substituted with their corresponding grand means, and missing categories of categorical variables are grouped and treated as a valid category. The processed variables are then used in the analysis. Optionally, you can request the creation of an additional variable that represents the proportion of missing variables in each case and use that variable in the analysis. 21 Identify Unusual Cases Identify Unusual Cases Options Figure 4-5 Identify Unusual Cases dialog box, Options tab Criteria for Identifying Unusual Cases.

In this data file, case 288 is missing the Patient ID, while cases 573 and 774 are missing the Hospital ID. Duplicate Identifiers Figure 6-5 Duplicate case identifiers (first 11 shown) A case should be uniquely identified by the combination of values of the identifier variables. The first 11 entries in the duplicate identifiers table are shown here. These duplicates are patients with multiple events who were entered as separate cases for each event. Because this information can be collected in a single row, these cases should be cleaned up.

The Visual Binning dialog boxes offer several automatic methods for creating bins without the use of a guide variable. These “unsupervised” rules are useful for producing descriptive statistics, such as frequency tables, but Optimal Binning is superior when your end goal is to produce a predictive model. Output. The procedure produces tables of cut points for the bins and descriptive statistics for each binning input variable. Additionally, you can save new variables to the active dataset containing the binned values of the binning input variables and save the binning rules as SPSS syntax for use in discretizing new data.

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