RESUMO
One of the most important questions that educators try to answer is how to prepare new generations of students for an unpredictable future. Students need to learn several skills, such as creativity, critical thinking, collaboration, and communication (the 4 Cs). Creativity, especially, is an essential skill in a complex and unforeseeable world/era, and an important step in any effort to enhance creativity is to identify students' creative strengths and relative weaknesses. This review aims to offer school psychologists and other educators such as teachers, policymakers, and curriculum designers a comprehensive and practical guide to one of the most well-known creativity assessments-the Torrance Tests of Creative Thinking (TTCT) that was developed by E. Paul Torrance in the 1960s. The paper discusses the history, components, training, psychometric properties, and uses of the TTCT. Contrary to the notion that the TTCT is only a measure of divergent thinking skills, the current article presents its other uses. It is the authors' hope that teachers, school psychologists, and other educators will find the information reported in this article useful to better understand the TTCT and use it most effectively.
RESUMO
BACKGROUND: The originality of divergent thinking (DT) production is one of the most critical indicators of creative potential. It is commonly scored using the statistical infrequency of responses relative to all responses provided in a given sample. AIMS: Response frequency estimates vary in terms of measurement precision. This issue has been widely overlooked and is addressed in the current study. SAMPLE AND METHOD: Secondary data analysis of 202 participants was performed. A total of 900 uniquely identified responses were generated on three DT tasks and subjected to a 1-parameter logistic model with a response as the unit of measurement which allowed for the calculation of response-level conditional reliability (and marginal reliability as an overall summary of measurement precision). RESULTS: Marginal reliability of response propensity estimates ranged from .62 to .67 across the DT tasks. Unique responses in the sample (the basis for the classic uniqueness scoring) displayed the lowest conditional reliability (across tasks: ≈ .50). Reliability increased nonlinearly as a function of both the frequency of occurrence predicted by the model (conditional reliability) and sample size (conditional and marginal reliability). CONCLUSIONS: This study indicates that the common practice of frequency-based originality scoring with typical sample sizes (e.g., N = 100 to N = 200) yields unacceptable levels of measurement precision (i.e., in particular for highly original responses). We further offer recommendations to mitigate the lack of measurement precision of frequency-based originality scores for DT research.
