Once you have identified unusual cases, you can further examine them and determine if they should be included in your analyses. You can flag outliers by creating a new variable. This searches for unusual cases based upon deviations from similar cases, and gives reasons for such deviations. Prevent outliers from skewing analyses when you use the IBM SPSS Data Preparation Anomaly Detection procedure. With this knowledge you can determine data validity and remove or correct suspicious cases at your discretion before analysis. You can specify validation rules for individual variables (such as range checks) and cross-variable checks (for example, "retired 30 year-olds").
You can receive reports of invalid cases as well as summaries of rule violations and the number of cases affected. This enables you to apply rules to perform data checks based on each variable's measure level (whether categorical or continuous).įor example, if you're analyzing data that has variables on a five-point Likert scale, use the Validate Data procedure to apply a rule for five-point scales and flag all cases that have values outside of the 1-5 range. To eliminate manual checks, use the IBM SPSS Data Preparation Validate Data procedure. And since every analyst in your organization could use a slightly different method, maintaining consistency from project to project may be a challenge. This approach is time consuming and prone to errors. You might run a frequency on your data, print the frequencies, circle what needs to be fixed and check for case IDs. Perform Data Checksĭata validation has typically been a manual process.
IBM SPSS Data Preparation easily plugs into IBM SPSS Statistics Base so you can seamlessly work in the IBM SPSS environment. Use the specialized data preparation techniques in IBM SPSS Data Preparation to facilitate data preparation in the analytical process. Expand your Data Preparation Techniques with IBM SPSS Data Preparation
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