models for intensive longitudinal data

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Models For Intensive Longitudinal Data

Author : Theodore A. Walls
ISBN : 0198038666
Genre : Medical
File Size : 71. 7 MB
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Rapid technological advances in devices used for data collection have led to the emergence of a new class of longitudinal data: intensive longitudinal data (ILD). Behavioral scientific studies now frequently utilize handheld computers, beepers, web interfaces, and other technological tools for collecting many more data points over time than previously possible. Other protocols, such as those used in fMRI and monitoring of public safety, also produce ILD, hence the statistical models in this volume are applicable to a range of data. The volume features state-of-the-art statistical modeling strategies developed by leading statisticians and methodologists working on ILD in conjunction with behavioral scientists. Chapters present applications from across the behavioral and health sciences, including coverage of substantive topics such as stress, smoking cessation, alcohol use, traffic patterns, educational performance and intimacy. Models for Intensive Longitudinal Data (MILD) is designed for those who want to learn about advanced statistical models for intensive longitudinal data and for those with an interest in selecting and applying a given model. The chapters highlight issues of general concern in modeling these kinds of data, such as a focus on regulatory systems, issues of curve registration, variable frequency and spacing of measurements, complex multivariate patterns of change, and multiple independent series. The extraordinary breadth of coverage makes this an indispensable reference for principal investigators designing new studies that will introduce ILD, applied statisticians working on related models, and methodologists, graduate students, and applied analysts working in a range of fields. A companion Web site at www.oup.com/us/MILD contains program examples and documentation.

Multilevel Models For Intensive Longitudinal Data With Heterogeneous Error Structure

Author : Seungmin Jahng
ISBN : 1249888107
Genre :
File Size : 58. 34 MB
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Enjoy a wide range of dissertations and theses published from graduate schools and universities from around the world. Covering a wide range of academic topics, we are happy to increase overall global access to these works and make them available outside of traditional academic databases. These works are packaged and produced by BiblioLabs under license by ProQuest UMI. The description for these dissertations was produced by BiblioLabs and is in no way affiliated with, in connection with, or representative of the abstract meta-data associated with the dissertations published by ProQuest UMI. If you have any questions relating to this particular dissertation, you may contact BiblioLabs directly.

Intensive Longitudinal Methods

Author : Niall Bolger
ISBN : 9781462506781
Genre : Psychology
File Size : 70. 92 MB
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This book offers a complete, practical guide to doing an intensive longitudinal study with individuals, dyads, or groups. It provides the tools for studying social, psychological, and physiological processes in everyday contexts, using methods such as diary and experience sampling. A range of engaging, worked-through research examples with datasets are featured. Coverage includes how to: select the best intensive longitudinal design for a particular research question, apply multilevel models to within-subject designs, model within-subject change processes for continuous and categorical outcomes, assess the reliability of within-subject changes, assure sufficient statistical power, and more. Several end-of-chapter write-ups illustrate effective ways to present study findings for publication. Datasets and output in SPSS, SAS, Mplus, HLM, MLwiN, and R for the examples are available on the companion website (www.intensivelongitudinal.com).

Handling Missing Data In The Modeling Of Intensive Longitudinal Data

Author : Linying Ji
ISBN : OCLC:951477328
Genre :
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The availability of intensive longitudinal data has helped spur the use more sophisticated methods for studying change. Unfortunately, missing data issues also arise frequently in such studies. Conventional missing data approaches are fraught with additional computational challenges when applied to intensive longitudinal data, and may not always be applicable due to the broad-ranging measurement characteristics of the covariates. In this study, we consider and illustrate the use of two approaches for implementing multiple imputing (MI) to cope with the missingness in fitting multivariate time series models, including a full MI approach, in which all missing cases are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with multiple imputation, while missingness in dependent variables are handled with full information maximum likelihood estimation. The performance of these approaches was examined under assumptions of missing completely at random, missing at random, and nonignorable missingness. The advantages and limitations of each approach are evaluated using a simulation study. We further demonstrate the implementation of the procedure in R using empirical data, involving n=111 families in which children's influences on parental conflicts are modeled as covariates over the course of 15 days.

Longitudinal Analysis

Author : Lesa Hoffman
ISBN : 9781317591092
Genre : Business & Economics
File Size : 29. 72 MB
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Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text features: A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers’ understanding of the overall model-building process Sample results sections for each example to provide useful templates for published reports Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis—general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data. Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book’s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.

Behavioral Healthcare And Technology

Author : Lisa Marsch
ISBN : 9780199314041
Genre : Medical
File Size : 72. 10 MB
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In recent years, there has been an explosion of research focused on using technology in healthcare, including web- and mobile- health assessment and intervention tools, as well as smartphone sensors and smart environments for monitoring and promoting health behavior. This work has shown that technology-based therapeutic tools offer considerable promise for monitoring and responding to individuals' health behavior in real-time. They may also function as important "clinician-extenders" or stand-alone tools, may be cost-effective and may offer countless opportunities for tailoring behavioral monitoring and intervention delivery in a manner that is optimally responsive to each individual's profile and health behavior trajectory over time. Additionally, informational and communication technologies may be used in the context of decision support tools to help individuals better understand and access treatment. Technology may enable entirely new models of healthcare both within and outside of formal systems of care and thus offers the opportunity to revolutionize healthcare delivery. This edited book defines the state of scientific research related to the development, experimental evaluation, and effective dissemination of technology-based therapeutic tools targeting behavioral health. Behavioral Healthcare and Technology provides an overview of current evidence-based approaches to leverage technology to promote behavioral health, including management of substance use, mental health, diet/exercise, medication adherence, as well as chronic disease self-management. Additionally, the book defines the state of implementation research examining models for deploying technology-based behavioral healthcare systems and integrating them into various care settings to increase the quality and reach of evidence-based behavioral healthcare while reducing costs.

Advances In Configural Frequency Analysis

Author : Alexander von Eye
ISBN : 9781606237205
Genre : Psychology
File Size : 23. 65 MB
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Using real-world data examples, this authoritative book shows how to use the latest configural frequency analysis (CFA) techniques to analyze categorical data. Some of the techniques are presented here for the first time. In contrast to methods that focus on relationships among variables, such as log-linear modeling, CFA allows researchers to evaluate differences and change at the level of individual cells in a table. Illustrated are ways to identify and test for cell configurations that are either consistent with or contrary to hypothesized patterns (the types and antitypes of CFA); control for potential covariates that might influence observed results; develop innovative prediction models; address questions of moderation and mediation; and analyze intensive longitudinal data. The book also describes free software applications for executing CFA.

Latent Markov Models For Longitudinal Data

Author : Francesco Bartolucci
ISBN : 9781466583719
Genre : Mathematics
File Size : 89. 89 MB
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Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors’ website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

Longitudinal Data Analysis

Author : Donald Hedeker
ISBN : 0470889187
Genre : Mathematics
File Size : 29. 61 MB
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This book presents and describes methods for analysis of longitudinal data, with a strong emphasis on the application of these methods to problems in the biomedical and behavioral sciences. This is an important book because longitudinal data are increasingly common in many areas of research, and methods of analysis of such data are not well understood by data analysts. Therefore, the book is geared more toward users, and not developers, of statistics. The Second Edition features six new chapters on: Bivariate and Multivariate Models; Growth Mixture Models; Grouped and Discrete Time Survival Analysis Models; Mixed–effects Regression Models for Higher–Level Data; Intensive Longitudinal Data; and Sample Size and Power Determination in Longitudinal Studies. In addition, the introductory chapter now describes the pertinent features of each presented dataset in an effort to streamline the presentation of the datasets. The relevant variables of the datasets and the scientific aim of the studies are described. Specific statistical procedures covered within the book include: repeated measures analysis of variance, multivariate analysis of variance for repeated measures, random–effects regression models (RRM), covariance–structure models, generalized–estimating equations (GEE) models, and generalizations of RRM and GE for categorical outcomes. This book emphasizes methods for analysis of longitudinal data analysis, which are extensively illustrated using real examples. The authors have chosen not to focus on software in the book, though some syntax examples are provided. Many programs are available for the analyses presented in this book including SAS, SPSS, SYSTAT, HLM, MLwiN, MIXREG/MIXOR and Mplus. Several datasets and computer syntax examples are posted on the book′s related website. It is the aim of the authors to keep these syntax examples current as new versions of the software programs emerge.

Longitudinal Data Analysis

Author : Jason Newsom
ISBN : 9781136705472
Genre : Psychology
File Size : 46. 75 MB
Format : PDF
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First Published in 2012. Routledge is an imprint of Taylor & Francis, an informa company.

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