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In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the “metafor” and “clubSandwich” packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

Gold (Au) catalysts exhibit a significant size effect, but its origin has been puzzling for a long time. It is generally believed that supported Au clusters are more or less rigid in working condition, which inevitably leads to the general speculation that the active sites are immobile. Here, by using atomic resolution in situ environmental transmission electron microscopy, we report size-dependent structure dynamics of single Au nanoparticles on ceria (CeO₂) in CO oxidation reaction condition at room temperature. While large Au nanoparticles remain rigid in the catalytic working condition, ultrasmall Au clusters lose their intrinsic structures and become disordered, featuring vigorous structural rearrangements and formation of dynamic low-coordinated atoms on surface. Ab initio molecular-dynamics simulations reveal that the interaction between ultrasmall Au cluster and CO molecules leads to the dynamic structural responses, demonstrating that the shape of the catalytic particle under the working condition may totally differ from the shape under the static condition. The present observation provides insight on the origin of superior catalytic properties of ultrasmall gold clusters.

Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, education, and other social sciences. Special attention is paid to confidence intervals, the statistical assumptions of the methods, and robust estimators of effect sizes. The extensive reference section is appreciated by all. With more than 40% new material, highlights of the new editon include: three new multivariate chapters covering effect sizes for analysis of covariance, multiple regression/correlation, and multivariate analysis of variance more learning tools in each chapter including introductions, summaries, \"Tips and Pitfalls\" and more conceptual and computational questions more coverage of univariate effect sizes, confidence intervals, and effect sizes for repeated measures to reflect their increased use in research more software references for calculating effect sizes and their confidence intervals including SPSS, SAS, R, and S-Plus the data used in the book are now provided on the web along with new data and suggested calculations with IBM SPSS syntax for computational practice. Effect Sizes for Research covers standardized and unstandardized differences between means, correlational measures, strength of association, and parametric and nonparametric measures for between- and within-groups data. Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is also an excellent supplement for advanced statistics courses in psychology, education, the social sciences, business, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended. \"This book presents an excellent summary of the debate around the use of null hypothesis significance testing and includes a lot of examples and practical advice to researchers about the software and methods needed to report effect size. Coverage of different models is wide and much of the material will be highly relevant to psychologists working in many fields.\" - Chris Beeley, Senior Evaluation Manager, Institute of Mental Health, Nottingham, UK, in The Psychologist \"This book is the single-best, definitive treatment of effect sizes, and is best characterized by the following adjectives: comprehensive, contemporary, clear, concrete, accessible, practical, and well-organized.\" - Bruce Thompson, Texas A&M University and Baylor College of Medicine, USA \"This is THE essential guide to effect sizes and confidence intervals: authoritative yet accessible, detailed yet entirely practical. It needs to be close to the elbow of every serious researcher and graduate student.\" - Geoff Cumming, La Trobe University, Melbourne, Australia \"Effect sizes and associated confidence intervals are increasingly recognized as a more informative basis for inferences than p-values. This helpful book introduces a wide array of tools to enable researchers to fulfill this agenda.\" - Robert G. Newcombe, Cardiff University, UK \"This book provides accessible, comprehensive coverage of effect sizes and their interpretation. I have used the first edition as a required text for a course on interpretation of data and plan to continue to use it. Researchers, both novice and experienced, will also find the book to be a valuable reference.\" - Patricia A. Shewokis, Drexel University, USA \"A thousand copies of this book should be dropped from a low flying airplane over ... the 4,200 college and univeristy campuses in America. ... No ... quantitative professor, researcher, or student should be without this text. ... The writing style is clear ... and a pleasure to read. This book should be required reading for all graduate faculty, students, and workers in the field.\" - Shlomo Sawilowsky, Wayne State University, USA \"It is important for graduate students to understand effect size in multivariate statistics. ... The book presents a good theoretical background on the effect sizes for various statistical procedures and it is ... way ahead of other books. ... The authors write well... It [is] an excellent text on effect sizes. ... I would ... use the book in my course.\" - Aman Yadav, Purdue University, USA Robert J. Grissom is a Professor Emeritus and Adjunct Professor of Psychology at San Francisco State University and a Consultant in Statistics. He received his Ph.D. in Psychology from Princeton University. Co-founder of the Graduate Program in Psychological Research at San Francisco State, Dr. Grissom has written numerous chapters and articles on effect size methodology. John J. Kim is a Professor of Psychology at San Francisco State University. He received his Ph.D. from the Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology in 1993. The current Associate Vice President for Academic Resources at San Francisco State, Dr. Kim has written numerous chapters and articles on effect size methodology. 1. Introduction 2. Confidence Intervals for Comparing the Averages of Two Groups 3. The Standardized Difference Between Means 4. Correlational Effect Sizes and Related Topics 5. Parametric and Nonparametric Effect Size Measures that Go Beyond Comparing Two Averages 6. Effect Sizes for One-Way ANOVA and Nonparametric Approaches 7. Effect Sizes for Factorial Designs 8. Effect Sizes for Categorical Variables 9. Effect Sizes for Ordinal Categorical Dependent Variables (Rating Scales) 10. Effect Sizes for Multiple Regression/Correlation 11. Effect Sizes for Analysis of Covariance 12. Effect Sizes for Multivariate Analysis of Variance

The interactions between dislocations (dislocations and deformation twins) and boundaries (grain boundaries, twin boundaries and phase interfaces) during deformation at ambient temperatures are reviewed with focuses on interaction behaviors, boundary resistances and energies during the interactions, transmission mechanisms, grain size effects and other primary influencing factors. The structure of boundaries, interactions between dislocations and boundaries in coarse-grained, ultrafine-grained and nano-grained metals during deformation at ambient temperatures are summarized, and the advantages and drawbacks of different in-situ techniques are briefly discussed based on experimental and simulation results. The latest studies as well as fundamental concepts are presented with the aim that this paper can serve as a reference in the interactions between dislocations and boundaries during deformation.

Structural components with different scales normally show different fatigue behaviors, which are virtually dominated by defects originated from multiple sources, including manufacturing processes. This paper reviews three types of size effects (statistical, geometrical, technological) as well as their recent advances in metal fatigue, aiming to provide a guide for fatigue strength assessment of engineering components containing defects, inclusions and material inhomogeneity. Firstly, the background of inherent defects and defect-based failure mechanism are briefly outlined, and fatigue failure analysis based on fracture mechanics as well as statistics theory are emphasized. Then, two approaches commonly applied in statistical size effect modeling, i.e. critical defect method and weakest link method, are elaborated. In addition, the highly stressed volume method is introduced for considering the geometrical size effects, and the technological (production and surface) size effect is briefly overviewed. Finally, further directions on size effect in metal fatigue under defects are explored.

Particle size strongly influences the shear strength of granular materials. However, previous studies of the particle size effect have focused mainly on the macroscopic behavior of granular materials, neglecting the associated micro-mechanism. In this study, the effect of particle size on the shear strength of uncrushable granular materials in biaxial testing is investigated using the discrete element method (DEM). First, a comprehensive calibration against experimental results is conducted to obtain the DEM parameters for two types of quartz sand. Then, a series of biaxial tests are simulated on sands with parallel particle size distributions to investigate the effect of particle size on macro- and microscopic behaviors. Finally, by adopting the rolling resistance method and the clump method, irregular-shaped particles are simulated to investigate how the particle size effect will be influenced by the particle shape. Simulation results demonstrate that (1) the peak shear strength increases with particle size, whereas the residual shear strength is independent of particle size; (2) the thickness of the shear band increases with the particle size, but its ratio decreases with particle size; (3) the particle size effect can be explained by the increase of friction utilization ratio with particle size; and (4) the particle size effect is more significant in granular materials that consist of particles with higher angularity.

An effect size (ES) provides valuable information regarding the magnitude of effects, with the interpretation of magnitude being the most important. Interpreting ES magnitude requires combining information from the numerical ES value and the context of the research. However, many researchers adopt popular benchmarks such as those proposed by Cohen. More recently, researchers have proposed interpreting ES magnitude relative to the distribution of observed ESs in a specific field, creating unique benchmarks for declaring effects small, medium or large. However, there is no valid rationale whatsoever for this approach. This study was carried out in two parts: (1) We identified articles that proposed the use of field-specific ES distributions to interpret magnitude (primary articles); and (2) We identified articles that cited the primary articles and classified them by year and publication type. The first type consisted of methodological papers. The second type included articles that interpreted ES magnitude using the approach proposed in the primary articles. There has been a steady increase in the number of methodological and substantial articles discussing or adopting the approach of interpreting ES magnitude by considering the distribution of observed ES in that field, even though the approach is devoid of a theoretical framework. It is hoped that this research will restrict the practice of interpreting ES magnitude relative to the distribution of ES values in a field and instead encourage researchers to interpret such by considering the specific context of the study.

This review of reviews presents an empirically based set of mean effect size distributions for judging the relative impact of the effects of universal mental health promotion and prevention programs for school-age youth (ages 5 through 18) across a range of program targets and types of outcomes. Mean effect size distributions were established by examining the findings from 74 meta-analyses of universal prevention and promotion programs that included more than 1100 controlled outcome studies involving over 490,000 school-age youth. The distributions of mean effect sizes from these meta-analyses indicated considerable variability across program targets and outcomes that differed substantially from Cohen’s (1988, Statistical power analysis for the behavioral sciences (2nd ed.)) widely used set of conventions for assessing if effects are small, medium, or large. These updated mean effect size distributions will provide researchers, practitioners, and funders with more appropriate evidence-based standards for judging the relative effects of universal prevention programs for youth. Limitations in current data and directions for future work are also discussed.

A transversely isotropic rock, slate, was utilized to investigate the size effect and anisotropy on its deformation, tensile strength, and failure mechanism. A series of Brazilian tests were conducted on slate samples of six different sizes from 25 to 100 mm in diameter at seven different loading-foliation angles from 0° to 90°. The results indicate that the Young’s modulus in the plane of transverse isotropy increases, while the Young’s modulus and shear modulus perpendicular to the plane of transverse isotropy decrease with specimen size. The tensile strength of the slate increases with increasing loading-foliation angle, the variation of which is well captured by the Nova–Zaninetti criterion. Furthermore, the tensile strength of the slate increases with specimen size at loading-foliation angles from 0° to 45°, while it increases first and then decreases with specimen size at loading-foliation angles from 60° to 90°. A unified size-effect relation including two equations is proposed and verified against the experimental data on slate. The size-effect relation reveals the relationship among the tensile strength, specimen size, and loading-foliation angle for the transversely isotropic rock. Finally, the slate samples exhibit an increased brittle failure with specimen size, which is consistent with the observations in various isotropic rocks. It is also found that the specimen size, loading-foliation angle, and loading configuration together control the failure mechanism of transversely isotropic rocks in the Brazilian test.

In the present study, the buckling problem of nonhomogeneous microbeams with a variable cross-section is analyzed. The microcolumn considered in this study is made of functionally graded materials in the longitudinal direction and the cross-section of the microcolumn varies continuously throughout the axial direction. The Bernoulli–Euler beam theory in conjunction with modified strain gradient theory are employed to model the structure by considering the size effect. The Rayleigh–Ritz numerical solution method is used to solve the eigenvalue problem for various conditions. The influences of changes in the cross-section and Young’s modulus, size dependency, and non-classical boundary conditions are examined in detail. It is observed that the size effect becomes more pronounced for smaller sizes and differences between the classical and non-classical buckling loads increase by increasing the taper ratios.