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Personality-Based Profile Matching in Personnel Selection: Estimates of Method Prevalence and Criterion-Related Validity


  • John T. Kulas

    Corresponding author
    1. Saint Cloud State University, USA
      John T. Kulas, Department of Psychology, Saint Cloud State University, St Cloud, MN, USA. Email:
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  • The author would like to thank Briana Olson for conducting the phone survey, the phone survey respondents for their participation, and CPP, Inc. and the Center for Creative Leadership for providing access to the CPI and 360° data used in this research.

John T. Kulas, Department of Psychology, Saint Cloud State University, St Cloud, MN, USA. Email:


Profile matching refers to selection based on applicant similarity to a pre-specified pattern of standing across several mutually considered personality dimensions. Although many investigations support the use of personality data through univariate, linear-based selection methodologies, there is no evidence within the literature that supports (or refutes) the use of profile matching. Regardless, a phone survey revealed that 62 per cent of consultative vendor organisations implement some form of profile matching. The current study addresses this scientist–practitioner void by investigating the broad, cross-organisational viability of three different profile matching strategies (profile band specification, profile similarity estimation, and configural scoring). Although some specifications of profile matching came close (empirically) to challenging linear regression cross-validation estimates, the profile matching strategy is considered to be burdened with additional conceptual concerns (primarily resulting from a lack of formal model specification) as well as practical limitations (for example, the likely creation of an artificial predictor ceiling). Linear regression is presented here as the more effective use of multi-trait information; however, if practitioners continue to utilise profile matching, it is suggested that they consider either adopting a configural scoring approach or referencing an index of profile similarity rather than retaining and applying desired profile bands.

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