Validate Data

The Validate Data dialog box allows you to identify suspicious and invalid cases, variables, and data values in the active dataset.

Example. A data analyst must provide a monthly customer satisfaction report to her client. The data she receives every month needs to be quality checked for incomplete customer IDs, variable values that are out of range, and combinations of variable values that are commonly entered in error. The Validate Data dialog box allows the analyst to specify the variables that uniquely identify customers, define single-variable rules for the valid variable ranges, and define cross-variable rules to catch impossible combinations. The procedure returns a report of the problem cases and variables. Moreover, the data has the same data elements each month, so the analyst is able to apply the rules to the new data file next month.

Statistics. The procedure produces lists of variables, cases, and data values that fail various checks, counts of violations of single-variable and cross-variable rules, and simple descriptive summaries of analysis variables.

Weights. The procedure ignores the weight variable specification and instead treats it as any other analysis variable.

To Validate Data

This feature requires the Data Preparation option.

1. From the menus choose:

   Data > Validation > Validate Data...

2. Select one or more analysis variables for validation by basic variable checks or by single-variable validation rules.

   Alternatively, you can:

3. Click the Cross-Variable Rules tab and apply one or more cross-variable rules.

Optionally, you can:

• Select one or more case identification variables to check for duplicate or incomplete IDs. Case ID variables are also used to label casewise output. If two or more case ID variables are specified, the combination of their values is treated as a case identifier.

Fields with Unknown Measurement Level