Divergent thinking (DT) tasks that solicit responses to open-ended stimuli are the most common form of assessment used in creativity research. Intended to measure creative potential, these tasks present researchers with multiple choices throughout administration and scoring that often result in widely varying scores. This study used a combined dataset from 9 independent samples (N = 1,066) containing DT responses (n = 15,935) to the alternate uses task to evaluate substantive validity under different scoring approaches. Human ratings of creative quality were compared to 11 additional quality scores based on statistical infrequency and semantic distance. Psychometric analyses were supplemented by review of response content to assess conceptual and operational correspondence. Results revealed several inconsistencies between and within scoring approaches, including considerable subjectivity required to group semantically equivalent responses before applying objective frequency rules; low reliability estimates for statistically unique responses; dictionary uses for some prompts scored as unique under 5% and 10% infrequency thresholds; variation in semantic distance scores of responses with similar meanings; and large within-subject differences across scoring types. Substantive review also indicated that how the creative dimension of appropriateness is operationalized can play a sizable role in score differences, such as treatment of responses featuring randomness, humor, fantasy, or cultural references. Recommendations for expanded reporting practices to support substantive validity in future DT research are discussed.

Ce travail de thèse doctorale traite l'analyse de la vulnérabilité des systèmes critiques pour la sécurité (par exemple, les centrales nucléaires) dans un cadre qui combine les disciplines de l'analyse des risques et de la prise de décision de multi-critères.La contribution scientifique suit quatre directions: (i) un modèle hiérarchique et quantitative est développé pour caractériser la susceptibilité des systèmes critiques pour la sécurité à plusieurs types de danger, en ayant la vue de `tous risques' sur le problème actuellement émergeant dans le domaine de l'analyse des risques; (ii) l'évaluation quantitative de la vulnérabilité est abordé par un cadre de classification empirique: à cette fin, un modèle, en se fondant sur la Majority Rule Sorting (MR-Sort) Méthode, généralement utilisés dans le domaine de la prise de décision, est construit sur la base d'un ensemble de données (en taille limitée) représentant (a priori connu) des exemples de classification de vulnérabilité; (iii) trois approches différentes (à savoir, une model-retrieval-based méthode, la méthode Bootstrap et la technique de validation croisée leave-one-out) sont élaborées et appliquées pour fournir une évaluation quantitative de la performance du modèle de classification (en termes de précision et de confiance dans les classifications), ce qui représente l'incertitude introduite dans l'analyse par la construction empirique du modèle de la vulnérabilité; (iv) basé sur des modèles développés, un problème de classification inverse est résolu à identifier un ensemble de mesures de protection qui réduisent efficacement le niveau de vulnérabilité du système critique à l’étude. Deux approches sont développées dans cet objectif: le premier est basé sur un nouvel indicateur de sensibilité, ce dernier sur l'optimisation.Les applications sur des études de cas fictifs et réels dans le domaine des risques de centrales nucléaires démontrent l'efficacité de la méthode proposée. / This Ph. D. work addresses the vulnerability analysis of safety-critical systems (e.g., nuclear power plants) within a framework that combines the disciplines of risk analysis and multi-criteria decision-making. The scientific contribution follows four directions: (i) a quantitative hierarchical model is developed to characterize the susceptibility of safety-critical systems to multiple types of hazard, within the needed `all-hazard' view of the problem currently emerging in the risk analysis field; (ii) the quantitative assessment of vulnerability is tackled by an empirical classification framework: to this aim, a model, relying on the Majority Rule Sorting (MR-Sort) Method, typically used in the decision analysis field, is built on the basis of a (limited-size) set of data representing (a priori-known) vulnerability classification examples; (iii) three different approaches (namely, a model-retrieval-based method, the Bootstrap method and the leave-one-out cross-validation technique) are developed and applied to provide a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments), accounting for the uncertainty introduced into the analysis by the empirical construction of the vulnerability model; (iv) on the basis of the models developed, an inverse classification problem is solved to identify a set of protective actions which effectively reduce the level of vulnerability of the critical system under consideration. Two approaches are developed to this aim: the former is based on a novel sensitivity indicator, the latter on optimization.Applications on fictitious and real case studies in the nuclear power plant risk field demonstrate the effectiveness of the proposed methodology.