Using the California Psychological Inventory to assess the Big Five personality domains: A hierarchical approach

The present research developed a new, multi-step approach for hierarchically assessing the Big Five personality domains from a large and diverse pool of existing questionnaire items: those of the California Psychological Inventory (CPI). First, the Abridged Big Five Circumplex (AB5C) structural model was used to organize the CPI item pool in Big Five factor space. Using the 10 resulting CPI-AB5C circumplexes as starting points, 16 facet scales were then developed to assess specific personality characteristics within the broad Big Five domains. Finally, principal components analysis with validimax rotation was used to score the five domains from the facet scales. In three independent samples, the resulting CPI-Big Five measure demonstrated strong reliability, convergence with selfand peer-reports, and discriminant validity. Availability of the new measure brings more than a half-century’s worth of archival CPI data to bear on contemporary research questions about the Big Five. Additionally, the process of developing the CPI-Big Five measure illustrates some of the challenges that may arise when attempting to assess new psychological constructs from existing measures, as well as methods for addressing such challenges. 2008 Elsevier Inc. All rights reserved. Christopher J. Soto’sworkon thisprojectwas supported inpartby a Chancellor’s Opportunity Predoctoral Fellowship from the University of California, Berkeley and by a Graduate Research Fellowship from theNational Science Foundation; Christopher J. Soto andOliver P. John also received support from a grant from the Retirement Research Foundation, a University of California Faculty Research Grant, and National Institute of Mental Health Grant MH-43948. The authors thank Lewis R. Goldberg for sharing access to the community validation sample data. They thank Joshua Eng, Carol J. Garvan, and Laura P. Naumann for serving as judges for the correlate-matching task. Finally, they thank Lewis R. Goldberg, Ravenna Helson, Laura P. Naumann, and the Mills Lab at the University of California, Berkeley for their helpful feedback on this work.