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Multilevel and Longitudinal Modeling with IBM SPSS: Multilevel And Longitudinal Modeling With Ibm Spss (Quantitative Methodology Series)
by Ronald H. Heck Scott L. Thomas Lynn N. TabataThis book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110. Highlights of the new edition include: Updated throughout to reflect IBM SPSS Version 21. Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change and longitudinal experimental designs (Ch.5). Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental) over time (Ch.6). New examples specifying multiple latent constructs and parallel growth processes (Ch. 7). Discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures (Ch.1). The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and cross-classified data sets. Chapters 3 and 4 introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting common programming and modeling problems. Models for investigating individual and organizational change are presented in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 provides an illustration of multilevel models with cross-classified data structures. The book concludes with ways to expand on the various multilevel and longitudinal modeling techniques and issues when conducting multilevel analyses. Ideal as a supplementary text for graduate courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology, this book’s practical approach also appeals to researchers in these fields. The book provides an excellent supplement to Heck & Thomas’s An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text.
Multilevel and Longitudinal Modeling with IBM SPSS (Quantitative Methodology Series)
by Ronald H. Heck Scott L. Thomas Lynn N. TabataMultilevel and Longitudinal Modeling with IBM SPSS, Third Edition, demonstrates how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Versions 25-27. Annotated screenshots with all relevant output provide readers with a step-by-step understanding of each technique as they are shown how to navigate the program. Throughout, diagnostic tools, data management issues, and related graphics are introduced. SPSS commands show the flow of the menu structure and how to facilitate model building, while annotated syntax is also available for those who prefer this approach. Extended examples illustrating the logic of model development and evaluation are included throughout the book, demonstrating the context and rationale of the research questions and the steps around which the analyses are structured. The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques that facilitate working with multilevel, longitudinal, or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, developing a multilevel model, extensions of the basic two-level model (e.g., three-level models, models for binary and ordinal outcomes), and troubleshooting techniques for everyday-use programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are next developed, followed by models with multivariate outcomes and, finally, models with cross-classified and multiple membership data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues (e.g., missing data, sample weights) to keep in mind in conducting multilevel analyses. Key features of the third edition: Thoroughly updated throughout to reflect IBM SPSS Versions 26-27. Introduction to fixed-effects regression for examining change over time where random-effects modeling may not be an optimal choice. Additional treatment of key topics specifically aligned with multilevel modeling (e.g., models with binary and ordinal outcomes). Expanded coverage of models with cross-classified and multiple membership data structures. Added discussion on model checking for improvement (e.g., examining residuals, locating outliers). Further discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures. Supported by online data sets, the book's practical approach makes it an essential text for graduate-level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in departments of business, education, health, psychology, and sociology. The book will also prove appealing to researchers in these fields. The book is designed to provide an excellent supplement to Heck and Thomas's An Introduction to Multilevel Modeling Techniques, Fourth Edition; however, it can also be used with any multilevel or longitudinal modeling book or as a stand-alone text.
Multilevel Model Foundations: Monopoly® Data and Stata
by Ralph B. TaylorThis book introduces the foundations of multilevel models, using Monopoly® rent data, from the classic board game, and the statistical program Stata®. Widespread experience with the game means many readers have a head start on understanding these models. The small-data set, 132 rent values for 22 properties clustered by the four sides of the playing board, combines with extensive graphical displays of data and results so all readers can see core multilevel ideas in action at a granular level. Two chapters on standard statistical models, one-way analysis of variance and multiple regression, help readers see how multilevel models rely on but also extend these monolevel ideas. Chapters present three basic multilevel models for cross-sectional analyses – analysis of variance, analysis of covariance, and random coefficients regression – and one basic developmental model for longitudinal analyses. Troubleshooting guidance, combined with close examination of data patterns, and careful inspection of model parameters, all help readers better grasp what model results mean, when model results should or should not be trusted, and how model results link back to core theoretical questions. Consequently, readers will develop a sense of best practices for building and diagnosing their own multilevel models. Those who complete the volume can readily apply what they have learned to more complex datasets and models and adapt available online Stata do files to those projects. Any social scientist working with data clustered in time, in space, or in both, and seeking to learn more about how to use, interpret, or teach these models, will find the book useful.
Multilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R, & HLM™
by Professor George David GarsonMultilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R & HLM™ provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson’s step-by-step instructions for the software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.
Multilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R, & HLM™
by Professor George David GarsonMultilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R & HLM™ provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson’s step-by-step instructions for the software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.
Multilevel Modeling (Quantitative Applications in the Social Sciences #143)
by Dr. Douglas A. LukeMultilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.
Multilevel Modeling (Quantitative Applications in the Social Sciences #143)
by Dr. Douglas A. LukeMultilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.
Multilevel Modeling of Categorical Outcomes Using IBM SPSS: Multilevel Modeling Of Categorical Outcomes Using Ibm Spss (Quantitative Methodology Series)
by Scott Thomas Ronald H Heck Lynn TabataThis is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.
Multilevel Modeling of Educational Data (Quantitative Methods in Education and the Behavioral Sciences Series)
by Ann A. O'Connell D. Betsy McCoachMultilevel Modeling of Educational Data, co-edited by Ann A. O’Connell, Ed.D., and D. Betsy McCoach, Ph.D., is the next volume in the series: Quantitative Methods in Education and the Behavioral Sciences: Issues, Research and Teaching (Information Age Publishing), sponsored by the Educational Statisticians' Special Interest Group (Ed-Stat SIG) of the American Educational Research Association. <p><p>The use of multilevel analyses to examine effects of groups or contexts on individual outcomes has burgeoned over the past few decades. Multilevel modeling techniques allow educational researchers to more appropriately model data that occur within multiple hierarchies (i.e.- the classroom, the school, and/or the district). Examples of multilevel research problems involving schools include establishing trajectories of academic achievement for children within diverse classrooms or schools or studying school-level characteristics on the incidence of bullying. <p><p>Multilevel models provide an improvement over traditional single-level approaches to working with clustered or hierarchical data; however, multilevel data present complex and interesting methodological challenges for the applied education research community.
Multilevel Modeling of Social Problems
by Robert B. SmithUniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models. This book innovatively: *Develops procedures for studying social, economic, and human development. * Uses typologies to group (i.e., classify or nest) the level of random macro-level factors. * Estimates models with Poisson, binomial, and Gaussian end points using SAS's generalized linear mixed models (GLIMMIX) procedure. * Selects appropriate covariance structures for generalized linear mixed models. * Applies difference-in-differences study designs in the multilevel modeling of intervention studies. *Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data. * Uses the Kenward-Rogers correction in mixed models of repeated measures. * Explicates differences between associational and causal analysis of multilevel models. * Consolidates research findings via meta-analysis and methodological critique. *Develops criteria for assessing a study's validity and zone of causality. Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures; policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.
Multilevel Modeling Using Mplus (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
by Holmes Finch Jocelyn BolinThis book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.
Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences #16)
by W. Holmes Finch Jocelyn E. Bolin Ken KelleyLike its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. <P><P>They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. <P><P>New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models, including rank based, heavy tailed distributions, and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. <P><P>This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. <P><P>About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT, Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.
Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences #16)
by W. Holmes Finch Jocelyn E. Bolin Ken KelleyLike its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data.The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class models. The third edition also includes new sections in Chapter 11 describing two useful alternatives to standard multilevel models, fixed effects models and generalized estimating equations. These approaches are particularly useful with small samples and when the researcher is interested in modeling the correlation structure within higher-level units (e.g., schools). The third edition also includes a new section on mediation modeling in the multilevel context, in Chapter 11. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.
Multilevel Modelling for Public Health and Health Services Research: Health in Context
by Alastair H. Leyland Peter P. GroenewegenThis open access book is a practical introduction to multilevel modelling or multilevel analysis (MLA) – a statistical technique being increasingly used in public health and health services research. The authors begin with a compelling argument for the importance of researchers in these fields having an understanding of MLA to be able to judge not only the growing body of research that uses it, but also to recognise the limitations of research that did not use it. The volume also guides the analysis of real-life data sets by introducing and discussing the use of the multilevel modelling software MLwiN, the statistical package that is used with the example data sets. Importantly, the book also makes the training material accessible for download – not only the datasets analysed within the book, but also a freeware version of MLwiN to allow readers to work with these datasets. The book’s practical review of MLA comprises: Theoretical, conceptual, and methodological backgroundStatistical backgroundThe modelling process and presentation of researchTutorials with example datasets Multilevel Modelling for Public Health and Health Services Research: Health in Context is a practical and timely resource for public health and health services researchers, statisticians interested in the relationships between contexts and behaviour, graduate students across these disciplines, and anyone interested in utilising multilevel modelling or multilevel analysis. “Leyland and Groenewegen’s wealth of teaching experience makes this book and its accompanying tutorials especially useful for a practical introduction to multilevel analysis.” ̶ Juan Merlo, Professor of Social Epidemiology, Lund University “Comprehensive and insightful. A must for anyone interested in the applications of multilevel modelling to population health”. ̶ S. (Subu) V. Subramanian, Professor of Population Health and Geography, Harvard University
Multilevel Statistical Models
by Harvey GoldsteinThroughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models.Key Features:Provides a clear introduction and a comprehensive account of multilevel models.New methodological developments and applications are explored.Written by a leading expert in the field of multilevel methodology.Illustrated throughout with real-life examples, explaining theoretical concepts.This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial.
Multilevel Strategic Interaction Game Models for Complex Networks (Understanding Complex Systems Ser.)
by Eitan Altman Konstantin Avrachenkov Francesco De Pellegrini Rachid El-Azouzi Huijuan WangThis book provides a state-of-the-art overview on the dynamics and coevolution in multi-level strategic interaction games. As such it summarizes the results of the European CONGAS project, which developed new mathematical models and tools for the analysis, prediction and control of dynamical processes in systems possessing a rich multi-level structure and a web of interwoven interactions among elements with autonomous decision-making capabilities. The framework is built around game theoretical concepts, in particular evolutionary and multi-resolution games, and includes also techniques drawn from graph theory, statistical mechanics, control and optimization theory. Specific attention is devoted to systems that are prone to intermittency and catastrophic events due to the effect of collective dynamics.
Multilevel Structural Equation Modeling (Quantitative Applications in the Social Sciences #179)
by Levente Littvay Bruno Castanho Silva Constantin Manuel BosancianuMultilevel Structural Equation Modeling by Bruno Castanho Silva, Constantin Manuel Bosancianu, and Levente Littvay serves as a minimally technical overview of multilevel structural equation modeling (MSEM) for applied researchers and advanced graduate students in the social sciences. As the first book of its kind, this title is an accessible, hands-on introduction for beginners of the topic. The authors predict a growth in this area, fueled by both data availability and also the availability of new and improved software to run these models. The applied approach, combined with a graphical presentation style and minimal reliance on complex matrix algebra guarantee that this volume will be useful to social science graduate students wanting to utilize such models.
Multilevel Structural Equation Modeling (Quantitative Applications in the Social Sciences #179)
by Levente Littvay Bruno Castanho Silva Constantin Manuel BosancianuMultilevel Structural Equation Modeling by Bruno Castanho Silva, Constantin Manuel Bosancianu, and Levente Littvay serves as a minimally technical overview of multilevel structural equation modeling (MSEM) for applied researchers and advanced graduate students in the social sciences. As the first book of its kind, this title is an accessible, hands-on introduction for beginners of the topic. The authors predict a growth in this area, fueled by both data availability and also the availability of new and improved software to run these models. The applied approach, combined with a graphical presentation style and minimal reliance on complex matrix algebra guarantee that this volume will be useful to social science graduate students wanting to utilize such models.
Multilinear Algebra (Algebra, Logic and Applications)
by Russell MerrisThe prototypical multilinear operation is multiplication. Indeed, every multilinear mapping can be factored through a tensor product. Apart from its intrinsic interest, the tensor product is of fundamental importance in a variety of disciplines, ranging from matrix inequalities and group representation theory, to the combinatorics of symmetric func
Multilinear Operator Integrals: Theory and Applications (Lecture Notes in Mathematics #2250)
by Anna Skripka Anna TomskovaThis book provides a comprehensive treatment of multilinear operator integral techniques. The exposition is structured to be suitable for a course on methods and applications of multilinear operator integrals and also as a research aid. The ideas and contributions to the field are surveyed and up-to-date results and methods are presented. Most practical constructions of multiple operator integrals are included along with fundamental technical results and major applications to smoothness properties of operator functions (Lipschitz and Hölder continuity, differentiability), approximation of operator functions, spectral shift functions, spectral flow in the setting of noncommutative geometry, quantum differentiability, and differentiability of noncommutative L^p-norms. Main ideas are demonstrated in simpler cases, while more involved, technical proofs are outlined and supplemented with references. Selected open problems in the field are also presented.
Multilingual Education Yearbook 2021: Policy and Practice in STEM Multilingual Contexts (Multilingual Education Yearbook)
by Anthony A. Essien Audrey MsimangaThis edited book attempts to foreground how challenges and complexities between policy and practice intertwine in the teaching and learning of the STEM subjects in multilingual settings, and how they (policy and practice) impact on educational processes, developments and outcomes. The unique feature of this book, thus, lies in its combination of not just language issues in the teaching and learning of the STEM subjects, but also in how these issues relate to policy and practice in multilingual contexts and how STEM research and practice may inform and shape language policies and their implementation in multilingual contexts. This book is of interest to stakeholders involved in STEM education such as researchers, undergraduate and graduate students, tertiary level teachers, teacher educators, curriculum developers as well as other professionals with responsibilities in STEM education subjects. The book is written in a way that is accessible to a wide range of backgrounds, including those who are in language education.
Multilingual Entity Linking (Synthesis Lectures on Human Language Technologies)
by Chen-Tse Tsai Shyam Upadhyay Dan RothThis book focuses on Entity Discovery and Linking (EDL), which is the problem of identifying concepts and entities, disambiguating them, and grounding them to one or more knowledge bases (KBs). The authors first provide background on the topic and emphasize why it is a crucial step toward understanding natural language text. As most of the content on the internet is not in English, the book also discusses cross-lingual EDL. The authors present the challenges associated with EDL problems and explain the existing solutions. The book covers the core challenges that apply to all EDL problems, as well as the additional challenges associated with cross-lingual EDL problems. The authors also survey relevant research papers, highlight recent trends, and identify areas for future research.
MULTIMATHEMACY: Anthropology and Mathematics Education
by Rik PinxtenThis book defends that math education should systematically start out from the diverse out-of-school knowledge of children and develop trajectories from there to the Academic Mathematics tower of knowledge. Learning theories of the sociocultural school (Vygotsky and on) are used here, and ethnographic knowledge from around the world is shown to offer a rich and varied base for curricula. The book takes a political stand against the exclusively western focus in OECD analyses and proposals on math education. This book aimsat agents in education and social actions in every cultural environment. But itis also attractive to mathematicians, anthropologists and other specialists. Itoffers a broad and scholarly view of knowledge and culture and a veryoriginal transcultural and transdisciplinarian approach to education. Ubiratan D'Ambrosio, UNICAMP/Universidade Estadual de Campinas, Brazil
Multimedia, Communication and Computing Application: Proceedings of the 2014 International Conference on Multimedia, Communication and Computing Application (MCCA 2014), Xiamen, China, October 16-17, 2014
by Ally Leung2014 International Conference on Multimedia, Communication and Computing Application (MCCA2014), Xiamen, China, Oct 16-17, 2014, provided a forum for experts and scholars of excellence from all over the world to present their latest work in the area of multimedia, communication and computing applications. In recent years, the multimedia techno
Multimedia Environmental Models: The Fugacity Approach
by J. Mark Parnis Donald MackayMultimedia Environmental Models: The Fugacity Approach, Third Edition, takes a broad approach of viewing chemical behavior in the total biosphere of connected biotic and abiotic compartments. Chemicals are subject to the laws of "mass balance," a constraint that provides the opportunity to establish quantitative expressions for chemical fate that are central to chemical management and regulatory legislation. This book employs both the conventional concentration-based procedures and those based on application of the more elegant and powerful concept of fugacity to characterize equilibrium, steady-state distribution, and time-dependent transport between environmental phases such as air, water, and soil. Organic chemicals are emphasized because they are more easily generalized when assessing environmental behavior.Features Illustrates professional approaches to calculating the fate of chemicals in the environment Explicitly details all worked examples in an annotated step-by-step fashion Presents real-life freely downloadable models of use to government, industry, and private consulting professionals and students alike Clarifies symbols and notation