Noel A. Card - Böcker

This volume reviews the challenges and alternative approaches to modeling how individuals change across time and provides methodologies and data analytic strategies for behavioral and social science researchers. This accessible guide provides concrete, clear examples of how contextual factors can be included in most research studies. Each chapter can be understood independently, allowing readers to first focus on areas most relevant to their work. The opening chapter demonstrates the various ways contextual factors are represented—as covariates, predictors, outcomes, moderators, mediators, or mediated effects. Succeeding chapters review "best practice" techniques for treating missing data, making model comparisons, and scaling across developmental age ranges. Other chapters focus on specific statistical techniques such as multilevel modeling and multiple-group and multilevel SEM, and how to incorporate tests of mediation, moderation, and moderated mediation. Critical measurement and theoretical issues are discussed, particularly how age can be represented and the ways in which context can be conceptualized. The final chapter provides a compelling call to include contextual factors in theorizing and research.This book will appeal to researchers and advanced students conducting developmental, social, clinical, or educational research, as well as those in related areas such as psychology and linguistics.

This book reviews methods of conceptualizing, measuring, and analyzing interdependent data in developmental and behavioral sciences. Quantitative and developmental experts describe best practices for modeling interdependent data that stem from interactions within families, relationships, and peer groups, for example. Complex models for analyzing longitudinal data, such as growth curves and time series, are also presented.Many contributors are innovators of the techniques and all are able to clearly explain the methodologies and their practical problems including issues of measurement, missing data, power and sample size, and the specific limitations of each method.Featuring a balance between analytic strategies and applications, the book addresses:The Actor-Partner Interdependence Model for analyzing influence between two individualsThe Intraclass Correlational Approach for analyzing distinguishable roles (parent-child) or exchangeable (same-sex) dyadic dataThe Social Relations Model for analyzing group interdependencySocial Network Analysis approaches for relationships between individualsThis book is intended for graduate students and researchers across the developmental, social, behavioral, and educational sciences. It is an excellent research guide and a valuable resource for advanced methods courses.

This book reviews methods of conceptualizing, measuring, and analyzing interdependent data in developmental and behavioral sciences. Quantitative and developmental experts describe best practices for modeling interdependent data that stem from interactions within families, relationships, and peer groups, for example. Complex models for analyzing longitudinal data, such as growth curves and time series, are also presented.Many contributors are innovators of the techniques and all are able to clearly explain the methodologies and their practical problems including issues of measurement, missing data, power and sample size, and the specific limitations of each method.Featuring a balance between analytic strategies and applications, the book addresses:The Actor-Partner Interdependence Model for analyzing influence between two individualsThe Intraclass Correlational Approach for analyzing distinguishable roles (parent-child) or exchangeable (same-sex) dyadic dataThe Social Relations Model for analyzing group interdependencySocial Network Analysis approaches for relationships between individualsThis book is intended for graduate students and researchers across the developmental, social, behavioral, and educational sciences. It is an excellent research guide and a valuable resource for advanced methods courses.

This volume reviews the challenges and alternative approaches to modeling how individuals change across time and provides methodologies and data analytic strategies for behavioral and social science researchers. This accessible guide provides concrete, clear examples of how contextual factors can be included in most research studies. Each chapter can be understood independently, allowing readers to first focus on areas most relevant to their work. The opening chapter demonstrates the various ways contextual factors are represented—as covariates, predictors, outcomes, moderators, mediators, or mediated effects. Succeeding chapters review "best practice" techniques for treating missing data, making model comparisons, and scaling across developmental age ranges. Other chapters focus on specific statistical techniques such as multilevel modeling and multiple-group and multilevel SEM, and how to incorporate tests of mediation, moderation, and moderated mediation. Critical measurement and theoretical issues are discussed, particularly how age can be represented and the ways in which context can be conceptualized. The final chapter provides a compelling call to include contextual factors in theorizing and research.This book will appeal to researchers and advanced students conducting developmental, social, clinical, or educational research, as well as those in related areas such as psychology and linguistics.

The book, a special issue of the Journal of Research on Adolescence, includes a number of invited contributions by international leaders in the interdisciplinary field of adolescence development. Each paper is intended to review a major subfield of study within the interdisciplinary field of adolescence studies. Each article reviews scientific and scholarly research published during the first decade of the 21st century (2000-2010). Authors were asked to consider contributions from multiple disciplines, and to review important and influential theoretical methodological innovations, and key empirical findings with attention to diversity in representations and populations of adolescents. The Decade in Review issue has the potential for becoming a signature contribution of JRA. As such, the Decade in Review will further establish JRA as the premier journal in the field, and will be an anchor for the journal's impact factor. With a diverse set of topics and contributors, the issue could attract a national and international new readership to the journal and potential membership for the Society. Established scholars and undergraduate and graduate students will cite the papers in their scholarly work in the next decade and beyond.

Appropriate for use in developmental research methods or analysis of change courses, this is the first methods handbook specifically designed to meet the needs of those studying development. Leading developmental methodologists present cutting-edge analytic tools and describe how and when to use them, in accessible, nontechnical language. They also provide valuable guidance for strengthening developmental research with designs that anticipate potential sources of bias. Throughout the chapters, research examples demonstrate the procedures in action and give readers a better understanding of how to match research questions to developmental methods. The companion website (www.guilford.com/laursen-materials) supplies data and program syntax files for many of the chapter examples.

Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both published and unpublished studies, create a coding manual, use traditional and unique effect size indices, and write a meta-analytic review. An ongoing example illustrates meta-analytic techniques. In addition to the fundamentals, the book discusses more advanced topics, such as artifact correction, random- and mixed-effects models, structural equation representations, and multivariate procedures. User-friendly features include annotated equations; discussions of alternative approaches; and Practical Matters sections that give advice on topics not often discussed in other books, such as linking meta-analytic results with theory and the utility of meta-analysis software programs.

Appropriate for use in developmental research methods or analysis of change courses, this is the first methods handbook specifically designed to meet the needs of those studying development. Leading developmental methodologists present cutting-edge analytic tools and describe how and when to use them in accessible, nontechnical language. They also provide valuable guidance for strengthening developmental research with designs that anticipate potential sources of bias. Throughout the chapters, research examples demonstrate the procedures in action and give readers a better understanding of how to match research questions to developmental methods. The companion website (www.guilford.com/laursen-materials) supplies data and program syntax files for many of the chapter examples.Â