Search

So far, no study has used ability profiles to evaluate the "science, technology, engineering, and/or mathematics (STEM) potential" (i.e. the capacity to engage in STEM professions) of Swiss students at the end of their compulsory education. In the current study, we use the proportion of high achieving students with a math-tilted ability profile as an indicator of STEM potential. Based on data from the Program for International Student Assessment, we assess Swiss students' STEM potential, nationwide and in each Swiss canton, compared to students in other countries. Our analysis shows that Swiss students, especially Swiss female students, have high STEM potential compared with their peers in other countries. Despite regional differences in the compulsory education system, STEM potential was high across all of the Swiss cantons. Based on our results, we conclude that national as opposed to regional factors are mainly responsible for the high STEM potential of Swiss students.

The relationship between abilities and skill acquisition has been the subject of numerous controversies in psychology. However, while most researchers implicitly or explicitly accept the idea that abilities and skill acquisition should be related, empirical research has failed to provide evidence for a consistently strong correlation between the two constructs. Based on the reanalysis of a study on skill acquisition using the air traffic controller task TRACON [Ackerman, P. L., Kanfer, R., and Goff, M. (1995). Cognitive and Noncognitive Determinants and Consequences of Complex Skill Acquisition. Journal of Experimental Psychology. Applied, 1(4), 270-304], it will be shown how latent growth curve modeling can help to gain a better understanding of the relationship between human abilities and skill acquisition. A brief introduction into the basic concepts of latent growth curve modeling will be given, particularly with regard to the advantages for the analysis of skill acquisition and its determinants. The goal is thereby to provide evidence for a much closer association than commonly assumed and to offer a new, differential, perspective formerly obscured by traditional between-subject analyses.

Fit indexes were compared with respect to a specific type of model misspecification. Simple structure was violated with some secondary loadings that were present in the true models that were not specified in the estimated models. The c2 test, Comparative Fit Index, Goodness-of-Fit Index, Incremental Fit Index, Nonnormed Fit Index, root mean squared error of approximation, standardized root mean square residual, and the c2/df values were investigated. Simulated data sets with 3 sample sizes (250, 500, and 1,000 cases), 4 levels of main loadings (.40, .50, .60, and .80), 2 numbers of factors (4, 8), and 2 types of association matrix (covariance, correlation) were the basis for maximum likelihood estimation of orthogonal and oblique factor models. Some correlations between fit indexes were low. Moreover, small distortions from simple structure did not lead to misfit in the RMSEA and SRMR, but they often led to misfit in the incremental fit indexes. This result may be of interest for research on personality traits, where small violations of simple structure are very common.

The authors bring together approaches from cognitive and individual differences psychology to model characteristics of reaction time distributions beyond measures of central tendency. Ex-Gaussian distributions and a diffusion model approach are used to describe individuals' reaction time data. The authors identified common latent factors for each of the 3 ex-Gaussian parameters and for 3 parameters central to the diffusion model using structural equation modeling for a battery of choice reaction tasks. These factors had differential relations to criterion constructs. Parameters reflecting the tail of the distribution (i.e., [paragraph] in the ex-Gaussian and drift rate in the diffusion model) were the strongest unique predictors of working memory, reasoning, and psychometric speed. Theories of controlled attention and binding are discussed as potential theoretical explanations. Kevwords: reaction time distributions, ex-Gaussian, EZ diffusion model, working memory, intelligence

System dynamics is a field that emphasizes the complex demands on human decision-makers and the cognitive processes that are either wired into the brain by nature or by education or that are stimulated by nurture. The world can be regarded as a complex system and it is a fascinating question how the human brain has adapted to its complexity and survived. Of considerable interest are the human dispositions or characteristics that are responsible for survival and adaptation. Are individual differences in human ability, aptitude, personality, or knowledge the origin of failures and successes in complex environments? These constructs are at the center of much research in psychology and cognitive sciences. Much controversy has revolved around whether dispositions like intelligence or situational constraints and characteristics of the environment are the most important predictive and explanatory constructs. The present work describes recent psychological research on working memory, intelligence, and performance on complex tasks. This research shows that intelligence, combined with personality and interests, are indeed constructs of impressive power in predicting performance. Implications of this research for selecting, educating, and training managers and decision-makers in charge of handling such complex systems are proposed and elaborated. Copyright © 2004 John Wiley & Sons, Ltd., System dynamics is a scientific area that deals with the understanding and modeling of the most complex phenomena in the real world (Forrester, 1961). The real world is dynamic, full [...]

Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients' optimal treatment based on their pre-treatment characteristics. Methods: A disorder-heterogeneous, naturalistic sample of N = 1,379 outpatients treated with either cognitive behavioral therapy or psychodynamic therapy was analyzed. Based on a combination of random forest and linear regression, differential treatment response was modeled in the training data (n = 966) to indicate each individual's optimal treatment. A separate holdout dataset (n = 413) was used to evaluate personalized recommendations. Results: The difference in outcomes between patients treated with their optimal vs. non-optimal treatment was significant in the training data, but non-significant in the holdout data (b = –0.043, p =.280). However, for the 50% of patients with the largest predicted benefit of receiving their optimal treatment, the average percentage of change on the BSI in the holdout data was 52.6% for their optimal and 38.4% for their non-optimal treatment (p =.017; d = 0.33 [0.06, 0.61]). Conclusion: A treatment selection algorithm based on a combination of ML and statistical inference might improve treatment outcome for some, but not all outpatients and could support therapists' clinical decision-making. [ABSTRACT FROM AUTHOR]