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Examining the Dimensionality of the Self-Perceptions of Creativity Scale Using Psychometric Network Analysis: A Tutorial.

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Title: Examining the Dimensionality of the Self-Perceptions of Creativity Scale Using Psychometric Network Analysis: A Tutorial.
Authors: Gao, Qianyi (AUTHOR), Qian, Meihua (AUTHOR), Wang, Andrew Xianyong (AUTHOR), Dai, Shenghai (AUTHOR)
Source: Creativity Research Journal. Apr-Jun2026, Vol. 38 Issue 2, p318-331. 14p.
Subjects: Factor structure, Psychometrics, Factor analysis, Creative ability, Statistical accuracy, Computational statistics
Abstract: Creativity has been well studied over the past several decades. However, our understanding regarding the nature of creativity remains limited, partially due to all kinds of methodological challenges that researchers have been facing. For example, the widely used factor-analytic techniques require researchers to make decisions about rotation methods, the number of factors to retain, and the interpretation of factor loadings. As a result, achieving a consensus on the factor structure of a latent construct is often difficult, especially when there is a lack of strong theoretical guidance in developing items. Recent trends in creativity research have shown the need and efforts to reduce subjectivity and increase the accuracy of creativity measurements. Hence, this study provides an example of how to estimate the number of dimensions underlying multivariate creativity data using psychometric network analysis (PNA). This new approach to dimensionality assessment can estimate the factor structure of a latent construct without extensive decision making from researchers. Step-by-step instructions on how to conduct the analysis in R using the Self-perceptions of Creativity data are presented. The interpretation of outputs and recommendations for beginners are also discussed. [ABSTRACT FROM AUTHOR]
Copyright of Creativity Research Journal 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: Examining the Dimensionality of the Self-Perceptions of Creativity Scale Using Psychometric Network Analysis: A Tutorial.
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  Data: <searchLink fieldCode="AR" term="%22Gao%2C+Qianyi%22">Gao, Qianyi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qian%2C+Meihua%22">Qian, Meihua</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Andrew+Xianyong%22">Wang, Andrew Xianyong</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+Shenghai%22">Dai, Shenghai</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Creativity+Research+Journal%22">Creativity Research Journal</searchLink>. Apr-Jun2026, Vol. 38 Issue 2, p318-331. 14p.
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  Data: Creativity has been well studied over the past several decades. However, our understanding regarding the nature of creativity remains limited, partially due to all kinds of methodological challenges that researchers have been facing. For example, the widely used factor-analytic techniques require researchers to make decisions about rotation methods, the number of factors to retain, and the interpretation of factor loadings. As a result, achieving a consensus on the factor structure of a latent construct is often difficult, especially when there is a lack of strong theoretical guidance in developing items. Recent trends in creativity research have shown the need and efforts to reduce subjectivity and increase the accuracy of creativity measurements. Hence, this study provides an example of how to estimate the number of dimensions underlying multivariate creativity data using psychometric network analysis (PNA). This new approach to dimensionality assessment can estimate the factor structure of a latent construct without extensive decision making from researchers. Step-by-step instructions on how to conduct the analysis in R using the Self-perceptions of Creativity data are presented. The interpretation of outputs and recommendations for beginners are also discussed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Creativity Research Journal 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: 10.1080/10400419.2024.2433359
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        Text: English
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      – SubjectFull: Factor analysis
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      – SubjectFull: Creative ability
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      – SubjectFull: Statistical accuracy
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      – SubjectFull: Computational statistics
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              Text: Apr-Jun2026
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              Y: 2026
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