Notre Dame Series on Quantitative Methodology – serie

Methodological Issues in Aging Research is the first volume in the "Notre Dame Series on Quantitative Methodology." This new series provides practical training on the latest quantitative methods used in social and behavioral research. Each volume features contributions from leading experts in state-of-the-art techniques applicable to a selected substantive topic.The first series volume provides researchers with innovative techniques for the collection and analyses of data focusing on aging and lifespan development. The book addresses such techniques as structural equation modeling, latent class analysis, hierarchical linear growth curve modeling, dynamical systems analysis, multivariate Rasch models, survival analysis, multilevel modeling, and quantitative genetic methods. These new techniques provide:better estimates of the direct effect of environmental or treatment effects and the dynamic pattern of genetic and environmental influences on adult development more precise predictions of outcomes which in turn increase the diagnostic power of test instrumentsthe potential for developing new treatments that take advantage of the intrinsic dynamics of the course of a disease or age-related change to enhance treatmentMethodological Issues in Aging Research appeals to advanced students and researchers in lifespan development, gerontology, health psychology, and other fields related to human development. It can be used as a main or supplemental text for advanced courses related to developmental research methods.

Methodological Issues in Aging Research is the first volume in the "Notre Dame Series on Quantitative Methodology." This new series provides practical training on the latest quantitative methods used in social and behavioral research. Each volume features contributions from leading experts in state-of-the-art techniques applicable to a selected substantive topic.The first series volume provides researchers with innovative techniques for the collection and analyses of data focusing on aging and lifespan development. The book addresses such techniques as structural equation modeling, latent class analysis, hierarchical linear growth curve modeling, dynamical systems analysis, multivariate Rasch models, survival analysis, multilevel modeling, and quantitative genetic methods. These new techniques provide:better estimates of the direct effect of environmental or treatment effects and the dynamic pattern of genetic and environmental influences on adult development more precise predictions of outcomes which in turn increase the diagnostic power of test instrumentsthe potential for developing new treatments that take advantage of the intrinsic dynamics of the course of a disease or age-related change to enhance treatmentMethodological Issues in Aging Research appeals to advanced students and researchers in lifespan development, gerontology, health psychology, and other fields related to human development. It can be used as a main or supplemental text for advanced courses related to developmental research methods.

Each volume in the Notre Dame Series on Quantitative Methodology features leading methodologists and substantive experts who provide instruction on innovative techniques designed to enhance quantitative skills in a substantive area. This latest volume focuses on the methodological issues and analyses pertinent to understanding psychological data from a dynamical system perspective. Dynamical systems analysis (DSA) is increasingly used to demonstrate time-dependent variable change. It is used more and more to analyze a variety of psychological phenomena such as relationships, development and aging, emotional regulation, and perceptual processes. The book opens with the best occasions for using DSA methods. The final two chapters focus on the application of dynamical systems methods to problems in psychology such as substance use and gestural dynamics. In addition, it reviews how and when to use:time series models from a discrete time perspective stochastic differential equations in continuous time estimating continuous time differential equation models multilevel models of differential equations to estimate within-person dynamics and the corresponding population means new SEM models for dynamical systems dataData Analytic Techniques for Dynamical Systems is beneficial to advanced students and researchers in the areas of developmental psychology, family studies, language processes, cognitive neuroscience, social and personality psychology, medicine, and emotion. Due to the book’s instructive nature, it serves as an excellent text for advanced courses on this particular technique.

Statistical and Process Models for Cognitive Neuroscience and Aging addresses methodological techniques for researching cognitive impairment, Alzheimer's disease, the biophysics and structure of the nervous system, the physiology of memory, and the analysis of EEG data. Each chapter, written by the expert in the area, provides a carefully crafted introduction to the subject at hand and the key methodological challenges facing that area of study.Although the chapters describe sophisticated techniques, each is accessible to scientists from a variety of fields. The editors' goal is to expose researchers working on a range of issues associated with cognitive aging to a variety of approaches and technologies, in an effort to cross disciplinary boundaries and further research in cognitive aging.Intended for researchers in cognitive, behavioral, and computational neuroscience, psychometrics, gerontology, cognitive, health, and developmental psychology, radiology, and medical research, this book also serves as a text for graduate level courses in cognitive science and cognitive aging.

Each volume in the Notre Dame Series on Quantitative Methodology features leading methodologists and substantive experts who provide instruction on innovative techniques designed to enhance quantitative skills in a substantive area. This latest volume focuses on the methodological issues and analyses pertinent to understanding psychological data from a dynamical system perspective. Dynamical systems analysis (DSA) is increasingly used to demonstrate time-dependent variable change. It is used more and more to analyze a variety of psychological phenomena such as relationships, development and aging, emotional regulation, and perceptual processes. The book opens with the best occasions for using DSA methods. The final two chapters focus on the application of dynamical systems methods to problems in psychology such as substance use and gestural dynamics. In addition, it reviews how and when to use:time series models from a discrete time perspective stochastic differential equations in continuous time estimating continuous time differential equation models multilevel models of differential equations to estimate within-person dynamics and the corresponding population means new SEM models for dynamical systems dataData Analytic Techniques for Dynamical Systems is beneficial to advanced students and researchers in the areas of developmental psychology, family studies, language processes, cognitive neuroscience, social and personality psychology, medicine, and emotion. Due to the book’s instructive nature, it serves as an excellent text for advanced courses on this particular technique.

Each volume in the Notre Dame Series on Quantitative Methodology features leading methodologists and substantive experts who provide instruction on innovative techniques designed to enhance quantitative skills in a substantive area. This latest volume focuses on the methodological issues and analyses pertinent to understanding psychological data from a dynamical system perspective. Dynamical systems analysis (DSA) is increasingly used to demonstrate time-dependent variable change. It is used more and more to analyze a variety of psychological phenomena such as relationships, development and aging, emotional regulation, and perceptual processes. The book opens with the best occasions for using DSA methods. The final two chapters focus on the application of dynamical systems methods to problems in psychology such as substance use and gestural dynamics. In addition, it reviews how and when to use:time series models from a discrete time perspective stochastic differential equations in continuous time estimating continuous time differential equation models multilevel models of differential equations to estimate within-person dynamics and the corresponding population means new SEM models for dynamical systems dataData Analytic Techniques for Dynamical Systems is beneficial to advanced students and researchers in the areas of developmental psychology, family studies, language processes, cognitive neuroscience, social and personality psychology, medicine, and emotion. Due to the book’s instructive nature, it serves as an excellent text for advanced courses on this particular technique.

This interdisciplinary volume features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. State-of-the-art techniques and applications used to analyze data obtained from studies in cognition, emotion, and electrophysiology are reviewed along with techniques for modeling in real time and for examining lifespan cognitive changes, for conceptualizing change using item response, nonparametric and hierarchical models, and control theory-inspired techniques for deriving diagnoses in medical and psychotherapeutic settings. The syntax for running the analyses presented in the book is provided on the Psychology Press site. Most of the programs are written in R while others are for Matlab, SAS, Win-BUGS, and DyFA.Readers will appreciate a review of the latest methodological techniques developed in the last few years. Highlights include an examination of:Statistical and mathematical modeling techniques for the analysis of brain imaging such as EEGs, fMRIs, and other neuroscience dataDynamic modeling techniques for intensive repeated measurement dataPanel modeling techniques for fewer time points dataState-space modeling techniques for psychological dataTechniques used to analyze reaction time data.Each chapter features an introductory overview of the techniques needed to understand the chapter, a summary, and numerous examples. Each self-contained chapter can be read on its own and in any order. Divided into three major sections, the book examines techniques for examining within-person derivations in change patterns, intra-individual change, and inter-individual differences in change and interpersonal dynamics. Intended for advanced students and researchers, this book will appeal to those interested in applying state-of-the-art dynamic modeling techniques to the the study of neurological, developmental, cognitive, and social/personality psychology, as well as neuroscience, computer science, and engineering.

Each volume in the Notre Dame Series on Quantitative Methodology features leading methodologists and substantive experts who provide instruction on innovative techniques designed to enhance quantitative skills in a substantive area. This latest volume focuses on the methodological issues and analyses pertinent to understanding psychological data from a dynamical system perspective. Dynamical systems analysis (DSA) is increasingly used to demonstrate time-dependent variable change. It is used more and more to analyze a variety of psychological phenomena such as relationships, development and aging, emotional regulation, and perceptual processes. The book opens with the best occasions for using DSA methods. The final two chapters focus on the application of dynamical systems methods to problems in psychology such as substance use and gestural dynamics. In addition, it reviews how and when to use:time series models from a discrete time perspective stochastic differential equations in continuous time estimating continuous time differential equation models multilevel models of differential equations to estimate within-person dynamics and the corresponding population means new SEM models for dynamical systems dataData Analytic Techniques for Dynamical Systems is beneficial to advanced students and researchers in the areas of developmental psychology, family studies, language processes, cognitive neuroscience, social and personality psychology, medicine, and emotion. Due to the book’s instructive nature, it serves as an excellent text for advanced courses on this particular technique.