Eisenbarth, Hedwig and Lilienfeld, Scott O. and Yarkoni, Tal (2015) Using a Genetic Algorithm to Abbreviate the Psychopathic Personality Inventory-Revised (PPI-R). PSYCHOLOGICAL ASSESSMENT, 27 (1). pp. 194-202. ISSN 1040-3590, 1939-134X
Full text not available from this repository. (Request a copy)Abstract
Some self-report measures of personality and personality disorders, including the widely used Psychopathic Personality Inventory-Revised (PPI-R), are lengthy and time-intensive. In recent work, we introduced an automated genetic algorithm (GA)-based method for abbreviating psychometric measures. In Study 1, we used this approach to generate a short (40-item) version of the PPI-R using 3 large-N German student samples (total N = 1,590). The abbreviated measure displayed high convergent correlations with the original PPI-R, and outperformed an alternative measure constructed using a conventional approach. Study 2 tested the convergent and discriminant validity of this short version in a fourth student sample (N = 206) using sensation-seeking and sensitivity to reward and punishment scales, again demonstrating similar convergent and discriminant validity for the PPI-R-40 compared with the full version. In a fifth community sample of North American participants acquired using Amazon Mechanical Turk, the PPI-R-40 showed similarly high convergent correlations, demonstrating stability across language, culture, and data-collection method. Taken together, these studies suggest that the GA approach is a viable method for abbreviating measures of psychopathy, and perhaps personality measures in general.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SHORT-FORM; VALIDATION; VALIDITY; psychopathy; genetic algorithm; abbreviation; personality |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Abteilung für Forensische Psychiatrie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 24 Jul 2019 08:43 |
| Last Modified: | 24 Jul 2019 08:43 |
| URI: | https://pred.uni-regensburg.de/id/eprint/5912 |
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