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The big two dictionaries: Capturing agency and communion in natural language.

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Title: The big two dictionaries: Capturing agency and communion in natural language.
Authors: Pietraszkiewicz, Agnieszka, Formanowicz, Magdalena, Gustafsson Sendén, Marie, Boyd, Ryan L., Sikström, Sverker, Sczesny, Sabine
Source: European Journal of Social Psychology. Aug2019, Vol. 49 Issue 5, p871-887. 17p. 8 Charts.
Subjects: Advertising, Discriminant analysis, Encyclopedias & dictionaries, Interpersonal relations, Labor market, Linguistics, Research evaluation, Gender role, Social skills, Stereotypes, Latent semantic analysis
Abstract: Four studies developed and validated two dictionaries to capture agentic and communal expressions in natural language. Their development followed the Linguistic Inquiry and Word Count (LIWC) approach (Study 1) and we tested their validity with frequency‐based analyses and semantic similarity measures. The newly developed Agency and Communion dictionaries were aligned with LIWC categories related to agency and communion (Study 2), and corresponded with subjective ratings (Study 3), confirming their convergent validity. Very low or absent correspondence between proposed dictionaries and unrelated LIWC categories demonstrated their discriminant validity (Study 2). Finally, we applied both dictionaries to language used in advertisements. In correspondence to gender stereotypes, male‐dominated jobs were advertised with more agentic than communal words, and female‐dominated jobs with more communal than agentic words (Study 4). Both dictionaries represent reliable tools for quantifying agentic and communal content in natural language, and will improve and facilitate future research on agency and communion. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Social Psychology is the property of Wiley-Blackwell 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 big two dictionaries: Capturing agency and communion in natural language.
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  Data: <searchLink fieldCode="AR" term="%22Pietraszkiewicz%2C+Agnieszka%22">Pietraszkiewicz, Agnieszka</searchLink><br /><searchLink fieldCode="AR" term="%22Formanowicz%2C+Magdalena%22">Formanowicz, Magdalena</searchLink><br /><searchLink fieldCode="AR" term="%22Gustafsson+Sendén%2C+Marie%22">Gustafsson Sendén, Marie</searchLink><br /><searchLink fieldCode="AR" term="%22Boyd%2C+Ryan+L%2E%22">Boyd, Ryan L.</searchLink><br /><searchLink fieldCode="AR" term="%22Sikström%2C+Sverker%22">Sikström, Sverker</searchLink><br /><searchLink fieldCode="AR" term="%22Sczesny%2C+Sabine%22">Sczesny, Sabine</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Social+Psychology%22">European Journal of Social Psychology</searchLink>. Aug2019, Vol. 49 Issue 5, p871-887. 17p. 8 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Advertising%22">Advertising</searchLink><br /><searchLink fieldCode="DE" term="%22Discriminant+analysis%22">Discriminant analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Encyclopedias+%26+dictionaries%22">Encyclopedias & dictionaries</searchLink><br /><searchLink fieldCode="DE" term="%22Interpersonal+relations%22">Interpersonal relations</searchLink><br /><searchLink fieldCode="DE" term="%22Labor+market%22">Labor market</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics%22">Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Research+evaluation%22">Research evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+role%22">Gender role</searchLink><br /><searchLink fieldCode="DE" term="%22Social+skills%22">Social skills</searchLink><br /><searchLink fieldCode="DE" term="%22Stereotypes%22">Stereotypes</searchLink><br /><searchLink fieldCode="DE" term="%22Latent+semantic+analysis%22">Latent semantic analysis</searchLink>
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  Data: Four studies developed and validated two dictionaries to capture agentic and communal expressions in natural language. Their development followed the Linguistic Inquiry and Word Count (LIWC) approach (Study 1) and we tested their validity with frequency‐based analyses and semantic similarity measures. The newly developed Agency and Communion dictionaries were aligned with LIWC categories related to agency and communion (Study 2), and corresponded with subjective ratings (Study 3), confirming their convergent validity. Very low or absent correspondence between proposed dictionaries and unrelated LIWC categories demonstrated their discriminant validity (Study 2). Finally, we applied both dictionaries to language used in advertisements. In correspondence to gender stereotypes, male‐dominated jobs were advertised with more agentic than communal words, and female‐dominated jobs with more communal than agentic words (Study 4). Both dictionaries represent reliable tools for quantifying agentic and communal content in natural language, and will improve and facilitate future research on agency and communion. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Journal of Social Psychology is the property of Wiley-Blackwell 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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      – SubjectFull: Latent semantic analysis
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              Text: Aug2019
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