User manual SPSS DATA VALIDATION 14.0

[. . . ] SPSS Data Validation 14. 0 TM For more information about SPSS® software products, please visit our Web site at http://www. spss. com or contact SPSS Inc. 233 South Wacker Drive, 11th Floor Chicago, IL 60606-6412 Tel: (312) 651-3000 Fax: (312) 651-3668 SPSS is a registered trademark and the other product names are the trademarks of SPSS Inc. No material describing such software may be produced or distributed without the written permission of the owners of the trademark and license rights in the software and the copyrights in the published materials. The SOFTWARE and documentation are provided with RESTRICTED RIGHTS. [. . . ] For each analysis variable, this option shows all single-variable validation rules that were violated and the number of values that violated each rule. It also reports the total number of single-variable rule violations for each variable. Summarize violations by rule. For each single-variable validation rule, this option reports variables that violated the rule and the number of invalid values per variable. It also reports the total number of values that violated each rule across variables. Display descriptive statistics. This option allows you to request descriptive statistics for analysis variables. A frequency table is generated for each categorical variable. A table of summary statistics including the mean, standard deviation, minimum, and maximum is generated for the scale variables. Move cases with validation rule violations. This option moves cases with single-variable or cross-variable rule violations to the top of the active dataset for easy perusal. 19 Validate Data Validate Data Save Figure 3-6 Validate Data: Save tab The Save tab allows you to save variables that record rule violations to the active dataset. Summary Variables. These are individual variables that can be saved. Default names for the variables are provided; you can edit them. Empty case indicator. Values of the variable reflect the scope specified on the Basic Checks tab. Duplicate ID group. Cases that have the same case identifier (other than cases with incomplete identifiers) are assigned the same group number. Cases with unique or incomplete identifiers are coded 0. 20 Chapter 3 Incomplete ID indicator. Cases with empty or incomplete case identifiers are assigned the value 1. All other cases are coded 0. Validation rule violations. This is the casewise total count of single-variable and cross-variable validation rule violations. Replace existing summary variables. Variables saved to the data file must have unique names, or replace variables with the same name. Save indicator variables. This option allows you to save a complete record of validation rule violations. Each variable corresponds to an application of a validation rule and has a value of 1 if the case violates the rule and a value of 0 if it does not. Chapter Identify Unusual Cases 4 The Anomaly Detection procedure searches for unusual cases based on deviations from the norms of their cluster groups. The procedure is designed to quickly detect unusual cases for data-auditing purposes in the exploratory data analysis step, prior to any inferential data analysis. This algorithm is designed for generic anomaly detection; that is, the definition of an anomalous case is not specific to any particular application, such as detection of unusual payment patterns in the healthcare industry or detection of money laundering in the finance industry, in which the definition of an anomaly can be well-defined. Example. A data analyst hired to build predictive models for stroke treatment outcomes is concerned about data quality because such models can be sensitive to unusual observations. [. . . ] Peer group 3 has middling costs and missing values. This organization suggests that peer group 2 is composed of patients who were dead on arrival, thus incurring very little cost and causing all of the treatment and rehabilitation variables to be missing. Peer group 3 likely contains many patients who died during treatment, thus incurring the treatment costs but not the rehabilitation costs and causing the rehabilitation variables to be missing. Peer group 1 is likely composed almost entirely of patients who survived through treatment and rehabilitation, thus incurring the highest costs. 71 Identify Unusual Cases Categorical Variable Norms Figure 6-12 Categorical variable norms (first 10 variables) The categorical variable norms serve much the same purpose as the scale norms, but categorical variable norms report the modal (most popular) category and the number and percentage of cases in the peer group that fall into that category. [. . . ]