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The Nomological Net of Knowledge, Self-Reported Knowledge, and Overclaiming in Children.

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Title: The Nomological Net of Knowledge, Self-Reported Knowledge, and Overclaiming in Children.
Authors: Goecke, Benjamin (AUTHOR), Schroeders, Ulrich (AUTHOR), Zettler, Ingo (AUTHOR), Schipolowski, Stefan (AUTHOR), Golle, Jessika (AUTHOR), Wilhelm, Oliver (AUTHOR)
Source: Journal of Personality Assessment. Sep/Oct2023, Vol. 105 Issue 5, p702-713. 12p. 4 Diagrams, 3 Charts.
Subjects: Crystallized intelligence, Fluid intelligence, Personality, Latent variables, Adults
Abstract: Research on self-reported knowledge and overclaiming in children is sparse. With the current study, we aim to close this gap by developing an overclaiming questionnaire measuring self-reported knowledge and overclaiming that is tailored to children. Moreover, we examine the nomological net of self-reported knowledge and overclaiming in childhood discussing three perspectives: Overclaiming as (a) a result of deliberate self-enhancement tendencies, (b) a proxy for declarative knowledge, and (c) an indicator of creative engagement. We juxtaposed overclaiming, as indicated by claiming familiarity with non-existent terms, and self-reported knowledge with fluid and crystallized intelligence, creativity, and personality traits in a sample of 897 children attending third grade. The results of several latent variable analyses were similar to findings known from adult samples: We found no strong evidence for any of the competing perspectives on overclaiming. Just like in adults, individual differences in self-reported knowledge were strongly inflated by overclaiming, and only weakly related to declarative knowledge. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Personality Assessment is the property of Taylor & Francis Ltd 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: The Nomological Net of Knowledge, Self-Reported Knowledge, and Overclaiming in Children.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Personality+Assessment%22">Journal of Personality Assessment</searchLink>. Sep/Oct2023, Vol. 105 Issue 5, p702-713. 12p. 4 Diagrams, 3 Charts.
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  Data: Research on self-reported knowledge and overclaiming in children is sparse. With the current study, we aim to close this gap by developing an overclaiming questionnaire measuring self-reported knowledge and overclaiming that is tailored to children. Moreover, we examine the nomological net of self-reported knowledge and overclaiming in childhood discussing three perspectives: Overclaiming as (a) a result of deliberate self-enhancement tendencies, (b) a proxy for declarative knowledge, and (c) an indicator of creative engagement. We juxtaposed overclaiming, as indicated by claiming familiarity with non-existent terms, and self-reported knowledge with fluid and crystallized intelligence, creativity, and personality traits in a sample of 897 children attending third grade. The results of several latent variable analyses were similar to findings known from adult samples: We found no strong evidence for any of the competing perspectives on overclaiming. Just like in adults, individual differences in self-reported knowledge were strongly inflated by overclaiming, and only weakly related to declarative knowledge. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Personality Assessment is the property of Taylor & Francis Ltd 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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        Value: 10.1080/00223891.2022.2144332
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      – Code: eng
        Text: English
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              Text: Sep/Oct2023
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