Examining the Dimensionality of the Self-Perceptions of Creativity Scale Using Psychometric Network Analysis
A Tutorial
Bibliographic Data
| ID | 21611927 |
|---|---|
| Authors | Qianyi Gao (0000-0002-3161-8909, University of Iowa, corresponding author), Meihua Qian (0000-0002-9383-7939, Clemson University), Andrew Xianyong Wang (Clemson University), Dai (0000-0002-7829-0621, Washington State University) |
| Year | 2026 |
| Volume | 38 |
| Issue | 2 |
| Pages | 318-331 |
| Publication date | 2026-04-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Creativity Research Journal (JOURNAL) |
| Journal identifiers | ISSN: 1040-0419 • E-ISSN: 1532-6934 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10400419.2024.2433359 |
| OpenAlex | W4404997371 |
| Language | EN |
| References cited | 53 |
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
Cognitive psychology · Creativity · Curse of dimensionality · Perception · Psychometrics · Applied Psychology · Clinical Psychology · Computer Science · Creativity in Education and Neuroscience · Mental Health Research Topics · Motivation and Self-Concept in Sports · Psychology · Social Psychology · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |