The impact of NEO PI-R gender norms on the assessment of personality disorder profiles.

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Title: The impact of NEO PI-R gender norms on the assessment of personality disorder profiles.
Authors: Samuel DB (AUTHOR), Ansell EB (AUTHOR), Hopwood CJ (AUTHOR), Morey LC (AUTHOR), Markowitz JC (AUTHOR), Skodol AE (AUTHOR), Grilo CM (AUTHOR), Samuel, Douglas B1 (AUTHOR), Ansell, Emily B (AUTHOR), Hopwood, Christopher J (AUTHOR), Morey, Leslie C (AUTHOR), Markowitz, John C (AUTHOR), Skodol, Andrew E (AUTHOR), Grilo, Carlos M (AUTHOR)
Source: Psychological Assessment. Sep2010, Vol. 22 Issue 3, p539-545. 7p.
Abstract: Many personality assessment inventories provide gender-specific norms to allow comparison of an individual's standing relative to others of the same gender. In some cases, this means that an identical raw score produces standardized scores that differ notably depending on whether the respondent is male or female. Thus, an important question is whether unisex-normed scores or gender-normed scores more validly assess personality. Gender-normed and unisex-normed scores from the NEO Personality Inventory--Revised (P. T. Costa & R. R. McCrae, 1992) were examined in a large clinical sample, using 2 measures of personality disorder as validating criteria. Gender-normed scores did not obtain significantly higher correlations. In fact, for 2 personality disorders (antisocial and narcissistic), gender-normed scores yielded significantly lower correlations, suggesting that personality disorder pathology relates most closely to one's absolute level of a personality trait, rather than one's standing relative to others of the same gender. Ramifications of this finding for personality research and clinical assessment are discussed. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Assessment is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The impact of NEO PI-R gender norms on the assessment of personality disorder profiles.
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  Data: <searchLink fieldCode="JN" term="%22Psychological+Assessment%22">Psychological Assessment</searchLink>. Sep2010, Vol. 22 Issue 3, p539-545. 7p.
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  Data: Many personality assessment inventories provide gender-specific norms to allow comparison of an individual's standing relative to others of the same gender. In some cases, this means that an identical raw score produces standardized scores that differ notably depending on whether the respondent is male or female. Thus, an important question is whether unisex-normed scores or gender-normed scores more validly assess personality. Gender-normed and unisex-normed scores from the NEO Personality Inventory--Revised (P. T. Costa & R. R. McCrae, 1992) were examined in a large clinical sample, using 2 measures of personality disorder as validating criteria. Gender-normed scores did not obtain significantly higher correlations. In fact, for 2 personality disorders (antisocial and narcissistic), gender-normed scores yielded significantly lower correlations, suggesting that personality disorder pathology relates most closely to one's absolute level of a personality trait, rather than one's standing relative to others of the same gender. Ramifications of this finding for personality research and clinical assessment are discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychological Assessment is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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