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Predicting attitudes toward ambiguity using natural language processing on free descriptions for open-ended question measurements.
Hitsuwari, Jimpei; Okano, Hirohito; Nomura, Michio.
Afiliación
  • Hitsuwari J; Graduate School of Education, Kyoto University, Kyoto, Japan.
  • Okano H; Japan Society for the Promotion of Science, Tokyo, Japan.
  • Nomura M; Graduate School of Education, Kyoto University, Kyoto, Japan.
Sci Rep ; 14(1): 8276, 2024 04 09.
Article en En | MEDLINE | ID: mdl-38594447
ABSTRACT
Individual traits and reactions to ambiguity differ and are conceptualized in terms of an individual's attitudes toward ambiguity or ambiguity tolerance. The development of natural language processing technology has made it possible to measure mental states and reactions through open-ended questions, rather than predefined numerical rating scales, which have traditionally been the dominant method in psychological research. This study presented three ambiguity-related situations and responses collected online from 591 participants in an open-ended format. After the analysis with bidirectional encoder representations from transformers, correlations were calculated using scores from the numerical evaluation by conventional questionnaire, and a significant moderate positive correlation was found. Therefore, this study found that attitudes toward ambiguity can be measured using an open-ended response method of reporting everyday life states. It is a novel methodology that can be expanded to other scales in psychology and can potentially be used in educational and clinical situations where participants can be asked to respond with minimal burden.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / Actitud Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / Actitud Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Reino Unido