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. 2023 Jan 18:13:1093343.
doi: 10.3389/fpsyg.2022.1093343. eCollection 2022.

Measuring flexibility: A text-mining approach

Affiliations

Measuring flexibility: A text-mining approach

Katalin Grajzel et al. Front Psychol. .

Abstract

In creativity research, ideational flexibility, the ability to generate ideas by shifting between concepts, has long been the focus of investigation. However, psychometric work to develop measurement procedures for flexibility has generally lagged behind other creativity-relevant constructs such as fluency and originality. Here, we build from extant research to theoretically posit, and then empirically validate, a text-mining based method for measuring flexibility in verbal divergent thinking (DT) responses. The empirical validation of this method is accomplished in two studies. In the first study, we use the verbal form of the Torrance Test of Creative Thinking (TTCT) to demonstrate that our novel flexibility scoring method strongly and positively correlates with traditionally used TTCT flexibility scores. In the second study, we conduct a confirmatory factor analysis using the Alternate Uses Task to show reliability and construct validity of our text-mining based flexibility scoring. In addition, we also examine the relationship between personality facets and flexibility of ideas to provide criterion validity of our scoring methodology. Given the psychometric evidence presented here and the practicality of automated scores, we recommend adopting this new method which provides a less labor-intensive and less costly objective measurement of flexibility.

Keywords: creativity; divergent thinking; flexibility; psychometrics; text-mining.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Measuring flexibility as a distance between consecutive responses. These responses are alternate uses of a bottle scored using GLoVe text-mining system.
Figure 2
Figure 2
Flexibility as semantic distances between consecutive answers. The differing lengths of the arrows are meant to conceptually depict the varying semantic distance between prompt and responses for originality and between responses for flexibility. The model is based on Acar and Runco (2014).
Figure 3
Figure 3
Path diagram of mean and maximum flexibility scoring.
Figure 4
Figure 4
Path diagram of mean flexibility, mean originality, and fluency scoring.
Figure 5
Figure 5
Path diagram of maximum flexibility, maximum originality, and fluency scoring.

References

    1. Abraham A. (2018). The Neuroscience of Creativity. Cambridge, UK: Cambridge University Press.
    1. Acar S., Abdulla A., Ahmed M., Runco M. A., Beketayev K. (2019). Latency as a predictor of originality in divergent thinking. Think. Skills Creat. 33:100574. doi: 10.1016/J.TSC.2019.100574 - DOI
    1. Acar S., Berthuiaume K., Grajzel K., Dumas D., Flemister T. (2021). Applying automated originality scoring to the verbal form of Torrance tests of creative thinking. Gifted Child Quarterly. Manuscript submitted for publication.
    1. Acar S., Ogurlu U., Zorychta A. (2022). Exploration of discriminant validity in divergent thinking tasks: a meta-analysis. Psychol. Aesthet. Creat. Arts. Advance online publication. doi: 10.1037/aca0000469 - DOI
    1. Acar S., Runco M. A. (2012). “Creative abilities: Divergent thinking,” in Handbook of Organizational Creativity. Academic Press, 115–139.

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