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This study compared the analogical reasoning of three groups that differed in their creative expertise: professional actors, undergraduate acting majors, and nonactors. Using an Analogy Finding Task, in which participants identified valid and nonvalid verbal analogies, three aspects of participants' analogical reasoning were measured: the number of analogies participants selected as valid (Quantity), the rate of true‐positive analogical identification (Sensitivity), and the rate of true‐negative identification of nonvalid analogies (Selectivity). The Analogy Finding Task was administered under both a baseline and a “think creatively” prompt. Results showed that actors (professional or student) were significantly more Sensitive to valid analogies than nonactors, and these creative experts were significantly more influenced by the “think creatively” prompt, which increased the Quantity, and decreased the Selectivity, of actors' analogical reasoning. To explain these results, we forward the general hypothesis that creative experts may be more flexible in response to creativity‐relevant contextual cues than nonexperts.

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Among scientists who study scientific production, the relationship between the quantity of a scientist’s production and the quality of their work has long been a topic of empirical research and theoretical debate. One principal theoretical perspective on the quantity–quality relationship has been the equal odds baseline, which posits that a scientist’s number of high-quality products increases linearly with their total number of products, and that there is a zero correlation between a scientist’s total number of products and the average quality of those products. While these central tenets of the equal odds baseline are well known, it also posits a number of more specific and less discussed aspects of the quality–quantity relation, including the expected residual variance and heteroscedastic errors when quality is regressed on quantity. After a careful examination of the expected variance by means of a non-parametric bootstrap approach, we forward a further prediction based on the heteroscedasticity implied by the equal-odds baseline that we term the tilted funnel hypothesis, that describes the shape of a bivariate scatterplot when quality is regressed on quantity, as well as the change in the strength of slope coefficients at different conditional quantiles of the quality distribution. In this study, we empirically test the expected residual variance and the tilted funnel hypothesis across three large datasets (including approximately 1.5 million inventors, 1800 psychologists, and 20,000 multidisciplinary scientists). Across all of the data sets, the results empirically supported the tilted funnel hypothesis, and therefore the results provided further evidence of the utility of the equal odds baseline.

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Abstract. The degree to which test scores can support justified and fair decisions about demographically diverse participants has been an important aspect of educational and psychological testing for millennia. In the last 30 years, this aspect of measurement has come to be known as consequential validity, and it has sparked scholarly debate as to how responsible psychometricians should be for the fairness of the tests they create and how the field might be able to quantify that fairness and communicate it to applied researchers and other stakeholders of testing programs. Here, we formulate a relatively simple-to-calculate ratio coefficient that is meant to capture how well the scores from a given test can predict a criterion free from the undue influence of student demographics. We posit three example calculations of this Consequential Validity Ratio (CVR): one where the CVR is quite strong, another where the CVR is more moderate, and a third where the CVR is weak. We provide preliminary suggestions for interpreting the CVR and discuss its utility in instances where new tests are being developed, tests are being adapted to a new population, or the fairness of an established test has become an empirical question.

Psychometric work with young children faces the particular challenge that children’s attention spans are relatively short, and therefore, shorter assessments are required while retaining comprehensive coverage. This article reports on three empirical studies that encompass the development and validation of the research-based early mathematics assessment-short form (REMA-SF), an instrument that measures the early mathematical competency of children from 3 to 8 years of age. The developed measure captures both children’s mathematical performance and the strategies children use to solve math problems. Results indicated that the REMA-SF can produce valid scores for measuring children’s math skills in early childhood, and the validity of the measure can be well-generalized to an external (or independent) sample. Additionally, we also equated the REMA scores between the long and short forms of the assessment: anchor items common across the forms were selected and refined in the equating process.

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