Performance of the models was assessed in terms of estimability (percentage of meta analyses that successfully converged and percentage where the between study correlation was estimable), bias, mean square error and coverage of the 95% confidence intervals. Our results indicate that simpler hierarchical models are valid in situations with few studies or sparse data. For synthesis of sensitivity and specificity, univariate random effects logistic regression models are appropriate when a bivariate model cannot be fitted.
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In this paper, we study the cognitive effectiveness of dia grammatic reasoning with proportional quantifiers such as most. We first examine how Euler style diagrams can represent syllogistic reasoning with proportional quantifiers, building on previous work on diagrams for the so called plurative syllogism (Rescher and Gallagher, 1965). We then conduct an experiment to compare performances on syllogistic reasoning tasks of two groups: those who use only linguistic material (two sentential premises and one conclusion) and those who are also given Euler diagrams corresponding to the two premises.