Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of applied work, and these contributions have been externally refereed to the high quality standards of leading journals in the field.

Advances in Growth Curve Models: Topics from the Indian Statistical Institute is developed from the Indian Statistical Institute's A National Conference on Growth Curve Models. This conference took place between March 28-30, 2012 in Giridih, Jharkhand, India. Jharkhand is a tribal area. Advances in Growth Curve Models: Topics from the Indian Statistical Institute shares the work of researchers in growth models used in multiple fields. A growth curve is an empirical model of the evolution of a quantity over time. Case studies and theoretical findings, important applications in everything from health care to population projection, form the basis of this volume. Growth curves in longitudinal studies are widely used in many disciplines including: Biology, Population studies, Economics, Biological Sciences, SQC, Sociology, Nano-biotechnology, and Fluid mechanics. Some included reports are research topics that have just been developed, whereas others present advances in existing literature. Both included tools and techniques will assist students and researchers in their future work. Also included is a discussion of future applications of growth curve models.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples. Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research. This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. Provided are complete proofs of theorems as well as practical data sets and MATLAB code.

This book explains and explores the growth curve model as a tool to gain insights into various research topics of interest to academics and practitioners alike. It includes studies on growth models for repeated measurement mixture experiments, and optimal designs for growth prediction in order to find an optimum design for the most efficient estimation of the parameters of the mixture models. It presents longitudinal studies conducted on the mathematical aptitude and intelligence quotient of tribal population in North Eastern states of India, and innovative statistical analysis showing that the status of tribes is improving over time. These results are supplemented by similar cross- sectional studies, and a retrospective longitudinal study of the social environment in North Eastern tribes indicating that the growth status of the social environment is improving. Child health is an important topic in developing countries, and as such the book features an overview of the growth and nutritional status of children aged 5 to 18 in India. Characterization of Extended Uniform Distribution and its applications for quality control in industrial production, and in yield data of tuber crops among others are discussed. Characterizations of distribution in terms of performance rate are also proved. There is also a contribution examining the past and present status of mangroves in Sunderban region of the Indian state of West Bengal from an ecological viewpoint using a growth curve model set-up. Lastly, it includes a chapter on a statistical study of platelet size decomposition and related growth model. Highlighting the importance of growth curve modelling as it applies to actual field data and encouraging more theoretically inclined statisticians to look into theoretical issues that need investigation, the book disseminates applications of the growth curve model to real-world problems and addresses related theoretical issues for the attention of theoreticians and practitioners.

Features recent trends and advances in the theory and techniques used to accurately measure and model growth Growth Curve Modeling: Theory and Applications features an accessible introduction to growth curve modeling and addresses how to monitor the change in variables over time since there is no “one size fits all” approach to growth measurement. A review of the requisite mathematics for growth modeling and the statistical techniques needed for estimating growth models are provided, and an overview of popular growth curves, such as linear, logarithmic, reciprocal, logistic, Gompertz, Weibull, negative exponential, and log-logistic, among others, is included. In addition, the book discusses key application areas including economic, plant, population, forest, and firm growth and is suitable as a resource for assessing recent growth modeling trends in the medical field. SAS® is utilized throughout to analyze and model growth curves, aiding readers in estimating specialized growth rates and curves. Including derivations of virtually all of the major growth curves and models, Growth Curve Modeling: Theory and Applications also features: • Statistical distribution analysis as it pertains to growth modeling • Trend estimations • Dynamic site equations obtained from growth models • Nonlinear regression • Yield-density curves • Nonlinear mixed effects models for repeated measurements data Growth Curve Modeling: Theory and Applications is an excellent resource for statisticians, public health analysts, biologists, botanists, economists, and demographers who require a modern review of statistical methods for modeling growth curves and analyzing longitudinal data. The book is also useful for upper-undergraduate and graduate courses on growth modeling.

This book describes some recent trends in GCM research on different subject areas, both theoretical and applied. This includes tools and possibilities for further work through new techniques and modification of existing ones. A growth curve is an empirical model of the evolution of a quantity over time. Growth curves in longitudinal studies are used in disciplines including biology, statistics, population studies, economics, biological sciences, sociology, nano-biotechnology, and fluid mechanics. The volume includes original studies, theoretical findings and case studies from a wide range of applied work. This volume builds on presentations from a GCM workshop held at the Indian Statistical Institute, Giridih, January 18-19, 2014. This book follows the volume Advances in Growth Curve Models, published by Springer in 2013. The results have meaningful application in health care, prediction of crop yield, child nutrition, poverty measurements, estimation of growth rate, and other research areas.​ ​​

"Latent Growth Curve Modeling introduces students to a strategy for modeling change over time. This volume offers a unique chance to study this useful research method with easy-to-follow examples of common growth modeling approaches. It addresses ways to fit a variety of advanced statistical models to repeated-measures data, to model change over time, and to assess individual differences in change." "This graduate-level volume is a resource for individual researchers or courses covering longitudinal data analysis, structural equation modeling, developmental methodology, and multivariate techniques."--BOOK JACKET.

This volume is a tribute to Professor Dietrich von Rosen on the occasion of his 65th birthday. It contains a collection of twenty original papers. The contents of the papers evolve around multivariate analysis and random matrices with topics such as high-dimensional analysis, goodness-of-fit measures, variable selection and information criteria, inference of covariance structures, the Wishart distribution and growth curve models.

Longitudinal Structural Equation Modeling is a comprehensive resource that reviews structural equation modeling (SEM) strategies for longitudinal data to help readers determine which modeling options are available for which hypotheses. This accessibly written book explores a range of models, from basic to sophisticated, including the statistical and conceptual underpinnings that are the building blocks of the analyses. By exploring connections between models, it demonstrates how SEM is related to other longitudinal data techniques and shows when to choose one analysis over another. Newsom emphasizes concepts and practical guidance for applied research rather than focusing on mathematical proofs, and new terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues and each chapter also includes examples of each model type, descriptions of model extensions, comment sections that provide practical guidance, and recommended readings. Expanded with new and updated material, this edition includes many recent developments, a new chapter on growth mixture modeling, and new examples. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this new edition will continue to appeal to researchers in these fields.