Mediated Modeling

'Mediated Modeling' is an approach to participatory environmental decision-making. It uses system dynamics models in a public setting to enable participants to learn about and see the consequences of various possible decision paths for their communities and ecosystems.

Mediated modeling is an innovative new approach that enhances the use of computer models as invaluable tools to guide policy and management decisions. Rather than having outside experts dispensing answers to local stakeholders, mediated modeling brings together diverse interests to raise the shared level of understanding and foster a broad and deep consensus. It provides a structured process based on system dynamics thinking in which community members, government officials, industry representatives, and other stakeholders can work together to produce a coherent, simple but elegant simulation model. Mediated Modeling by Marjan Van Den Belt is a practical guide to participatory modeling for both practitioners and students, one that is firmly theoretically grounded in the field of systems dynamics and environmental modeling. Five in-depth case studies describe the successful use of the technique in a variety of settings, and a final chapter synthesizes the lessons highlighted by the case studies. Mediated Modeling's step-by-step description of the techniques and practical advice regarding implementation offer a real-world solution for all those seeking to make sound decisions about the environment.

Mediated modeling is an innovative new approach that enhances the use of computer models as invaluable tools to guide policy and management decisions. Rather than having outside experts dispensing answers to local stakeholders, mediated modeling brings together diverse interests to raise the shared level of understanding and foster a broad and deep consensus. It provides a structured process based on system dynamics thinking in which community members, government officials, industry representatives, and other stakeholders can work together to produce a coherent, simple but elegant simulation model. Mediated Modeling by Marjan Van Den Belt is a practical guide to participatory modeling for both practitioners and students, one that is firmly theoretically grounded in the field of systems dynamics and environmental modeling. Five in-depth case studies describe the successful use of the technique in a variety of settings, and a final chapter synthesizes the lessons highlighted by the case studies. Mediated Modeling's step-by-step description of the techniques and practical advice regarding implementation offer a real-world solution for all those seeking to make sound decisions about the environment.

This book has been replaced by Introduction to Mediation, Moderation, and Conditional Process Analysis, Third Edition, ISBN 978-1-4625-4903-0.

Pharmaceutical companies are spending increasing amounts of money on drug discovery and development. Nevertheless, attrition rates in clinical development are still very high, and up to 90% of new compounds fail in clinical phase I - III trials, which is partially due to lack of clinical efficacy. This indicates a strong need for highly predictive in vitro and in vivo models. The "50th International Workshop of the Ernst Schering Research Foundation" focussed on "Animal Models of T Cell-Mediated Skin Diseases". Such animal models should have impact not only on inflammatory dermatoses but also on other inflammatory disorders due to their model character. The current volume summarises recent advances in animal research that are important for anti-inflammatory drug discovery.

This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details to help readers better understand the models and interpret the results. Every R command necessary for conducting the analyses is described along with the resulting output which provides readers with a template to follow when they apply the methods to their own data. The basic information pertinent to each model, the newest developments in these areas, and the relevant R code to use them are reviewed. Each chapter also features an introduction, summary, and suggested readings. A glossary of the text’s boldfaced key terms and key R commands serve as helpful resources. The book is accompanied by a website with exercises, an answer key, and the in-text example data sets. Latent Variable Modeling with R: -Provides some examples that use messy data providing a more realistic situation readers will encounter with their own data. -Reviews a wide range of LVMs including factor analysis, structural equation modeling, item response theory, and mixture models and advanced topics such as fitting nonlinear structural equation models, nonparametric item response theory models, and mixture regression models. -Demonstrates how data simulation can help researchers better understand statistical methods and assist in selecting the necessary sample size prior to collecting data. -www.routledge.com/9780415832458 provides exercises that apply the models along with annotated R output answer keys and the data that corresponds to the in-text examples so readers can replicate the results and check their work. The book opens with basic instructions in how to use R to read data, download functions, and conduct basic analyses. From there, each chapter is dedicated to a different latent variable model including exploratory and confirmatory factor analysis (CFA), structural equation modeling (SEM), multiple groups CFA/SEM, least squares estimation, growth curve models, mixture models, item response theory (both dichotomous and polytomous items), differential item functioning (DIF), and correspondance analysis. The book concludes with a discussion of how data simulation can be used to better understand the workings of a statistical method and assist researchers in deciding on the necessary sample size prior to collecting data. A mixture of independently developed R code along with available libraries for simulating latent models in R are provided so readers can use these simulations to analyze data using the methods introduced in the previous chapters. Intended for use in graduate or advanced undergraduate courses in latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, and social and health sciences, researchers in these fields also appreciate this book’s practical approach. The book provides sufficient conceptual background information to serve as a standalone text. Familiarity with basic statistical concepts is assumed but basic knowledge of R is not.