Search Results (5)

Scoring divergent thinking (DT) tasks poses significant challenges as differences between raters affect the resulting scores. Item Response Theory (IRT) offers a statistical framework to handle differences in rater severity and discrimination. We applied the IRT framework by re-analysing an open access dataset including three scored DT tasks from 202 participants. After comparing different IRT models, we examined rater severity and discrimination parameters for individual response scoring and snapshot scoring using the best-fitting model—Graded Response Model. Secondly, we compared IRT-adjusted scores with non-adjusted average and max-scoring scores in terms of reliability and fluency confound effect. Additionally, we simulated missing data to assess the robustness of these approaches. Our results showed that IRT models can be applied to both individual response scoring and snapshot scoring. IRT-adjusted and unadjusted scores were highly correlated, indicating that, under conditions of high inter-rater agreement, rater variability in severity and discrimination does not substantially impact scores. Overall, our study confirms that IRT is a valuable statistical framework for modeling rater severity and discrimination for different DT scores, although further research is needed to clarify the conditions under which it offers the greatest practical benefit. Full article

The alternate uses task (AUT) is the most popular measure when it comes to the assessment of creative potential. Since their implementation, AUT responses have been rated by humans, which is a laborious task and requires considerable resources. Large language models (LLMs) have shown promising performance in automatically scoring AUT responses in English as well as in other languages, but it is not clear which method works best for German data. Therefore, we investigated the performance of different LLMs for the automated scoring of German AUT responses. We compiled German data across five research groups including ~50,000 responses for 15 different alternate uses objects from eight lab and online survey studies (including ~2300 participants) to examine generalizability across datasets and assessment conditions. Following a pre-registered analysis plan, we compared the performance of two fine-tuned, multilingual LLM-based approaches [Cross-Lingual Alternate Uses Scoring (CLAUS) and the Open Creativity Scoring with Artificial Intelligence (OCSAI)] with the Generative Pre-trained Transformer (GPT-4) in scoring (a) the original German AUT responses and (b) the responses translated to English. We found that the LLM-based scorings were substantially correlated with human ratings, with higher relationships for OCSAI followed by GPT-4 and CLAUS. Response translation, however, had no consistent positive effect. We discuss the generalizability of the results across different items and studies and derive recommendations and future directions. Full article

Even though a relationship between psychopathology and creativity has been postulated since the time of ancient Greece, systematic meta-analyses on this topic are still scarce. Thus, the meta-analysis described here can be considered the first to date that specifically focuses on the relationship between creative potential, as measured by divergent thinking, and bipolar disorder, as opposed to psychopathology in general. An extensive literature search of 4670 screened hits identified 13 suitable studies, including a total of 42 effect sizes and 1857 participants. The random-effects model showed an overall significant, positive, yet diminutively small effect (d = 0.11, 95% CI: [0.002, 0.209], p = 0.045) between divergent thinking and bipolar disorder. A handful of moderators were examined, which revealed a significant moderating effect for bipolar status, as either euthymic (d = 0.14, p = 0.043), subclinical (d = 0.17, p = 0.001), manic (d = 0.25, p = 0.097), or depressed (d = −0.51, p < 0.001). However, moderator analyses should be treated with caution because of the observed confounding of moderators. Finally, none of the employed methods for publication-bias detection revealed any evidence for publication bias. We discuss further results, especially regarding the differences between subclinical and clinical samples. Full article

This paper provides a meta-analytic update on the relationship between intelligence and divergent thinking (DT), as research on this topic has increased, and methods have diversified since Kim’s meta-analysis in 2005. A three-level meta-analysis was used to analyze 849 correlation coefficients from 112 studies with an overall N = 34,610. The overall effect showed a significant positive correlation of r = .25. This increase of the correlation as compared to Kim’s prior meta-analytic findings could be attributed to the correction of attenuation because a difference between effect sizes prior-Kim vs. post-Kim was non-significant. Different moderators such as scoring methods, instructional settings, intelligence facets, and task modality were tested together with theoretically relevant interactions between some of these factors. These moderation analyses showed that the intelligence–DT relationship can be higher (up to r = .31–.37) when employing test-like assessments coupled with be-creative instructions, and considering DT originality scores. The facet of intelligence (g vs. g_f vs. g_c) did not affect the correlation between intelligence and DT. Furthermore, we found two significant sample characteristics: (a) average sample age was positively associated with the intelligence–DT correlation, and (b) the intelligence–DT correlation decreased for samples with increasing percentages of females in the samples. Finally, inter-moderator correlations were checked to take potential confounding into account, and also publication bias was assessed. This meta-analysis provides a comprehensive picture of current research and possible research gaps. Theoretical implications, as well as recommendations for future research, are discussed. Full article

Mathematical creativity is perceived as an increasingly important aspect of everyday life and, consequently, research has increased over the past decade. However, mathematical creativity has mainly been investigated in children and adolescents so far. Therefore, the first goal of the current study was to develop a mathematical creativity measure for adults (MathCrea) and to evaluate its reliability and construct validity in a sample of 100 adults. The second goal was to investigate how mathematical creativity is related to intelligence, mathematical competence, and general creativity. The MathCrea showed good reliability, and confirmatory factor analysis confirmed that the data fitted the assumed theoretical model, in which fluency, flexibility, and originality constitute first order factors and mathematical creativity a second order factor. Even though intelligence, mathematical competence, and general creativity were positively related to mathematical creativity, only numerical intelligence and general creativity predicted unique variance of mathematical creativity. Additional analyses separating quantitative and qualitative aspects of mathematical creativity revealed differential relationships to intelligence components and general creativity. This exploratory study provides first evidence that intelligence and general creativity are important predictors for mathematical creativity in adults, whereas mathematical competence seems to be not as important for mathematical creativity in adults as in children. Full article