The relation between creativity and students’ performance on different types of geometrical problems in elementary education

Aim: In the current study we aimed to investigate the relation between creativity and mathematical problem solving in the upper grades of elementary school. Methods: To examine how student’s levels of general creativity were related to their performance on different types of geometrical problems, a geometry test with diverse problems was administered to a sample of 1665 Dutch students from third to sixth grade, as well as a creativity test. The geometry test consisted of four closed-ended routine problems, six closed-ended non-routine problems (related to a visual artwork) and four open-ended non-routine problems (multiple solutions problems). The Test of Creative Thinking—Drawing Production was used to measure students’ creativity. Multivariate multilevel analyses were conducted to take the nested structure of the data into account. Results: The results showed that creativity was a significant predictor of students’ performance on all types of geometrical problems, but most strongly associated with performance on open-ended non-routine problems, suggesting that students with higher levels of creativity perform better in solving geometry problems in general, but especially in geometry problems asking for multiple solutions

A review of applications of the Bayes factor in psychological research

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The paper is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines

Reproducibility of preclinical animal research improves with heterogeneity of study samples

Single-laboratory studies conducted under highly standardized conditions are the gold standard in preclinical animal research. Using simulations based on 440 preclinical studies across 13 different interventions in animal models of stroke, myocardial infarction, and breast cancer, we compared the accuracy of effect size estimates between single-laboratory and multi-laboratory study designs. Single-laboratory studies generally failed to predict effect size accurately, and larger sample sizes rendered effect size estimates even less accurate. By contrast, multi-laboratory designs including as few as 2 to 4 laboratories increased coverage probability by up to 42 percentage points without a need for larger sample sizes. These findings demonstrate that within-study standardization is a major cause of poor reproducibility. More representative study samples are required to improve the external validity and reproducibility of preclinical animal research and to prevent wasting animals and resources for inconclusive research

Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study

<p>Abstract</p> <p>Background</p> <p>Multicentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs.</p> <p>Methods</p> <p>Assuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure.</p> <p>Results</p> <p>While all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis.</p> <p>Conclusions</p> <p>All six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.</p

Modelling survival : exposure pattern, species sensitivity and uncertainty

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans

Completeness of reporting and risks of overstating impact in cluster randomised trials : a systematic review

Acknowledgments We received no funding specifically for this systematic review. ELT is funded in part by awards R01-AI141444 from the National Institute of Allergy and Infectious Diseases and R01-MH120649 from the US National Institute of Mental Health; both Institutes are part of the National Institutes of Health (NIH). JAG and ACP's support of this project was made possible (in part) by grant number UL1TR002553 from the National Center for Advancing Translational Sciences of the NIH, and the NIH Roadmap for Medical Research. JEM is supported by an Australian National Health and Medical Research Council Career Development Fellowship (APP1143429). SN was supported by an award that is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement, and part of the EDCTP2 programme supported by the European Union (grant reference MR/R010161/1). ABF acknowledges funding support from the National Health and Medical Research Council of Australia (grant ID 1183303). KH is funded by a National Institute for Health Research Senior Research Fellowship SRF-2017-10-002. The contents of the research included in this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of any of the funders. The research contributed by all authors of this manuscript are independent of their funders. Specifically, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We wish to thank the three reviewers for their insightful comments and constructive feedback.Peer reviewedPublisher PD