*Continuous Responses, Third Edition*

Author: Sophia Rabe-Hesketh,Anders Skrondal

Publisher: Stata Press

ISBN: 9781597181037

Category: Mathematics

Page: 514

View: 5357

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Search Results for: multilevel-and-longitudinal-modeling-using-stata-volume-i-continuous-responses-volume-1

## Multilevel and Longitudinal Modeling Using Stata

Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. The first part provides a review of the methods of linear regression. The second part provides an in-depth coverage of the two-level models, the simplest extensions of a linear regression model. The mixed-model foundation and the in-depth coverage of the mixed-model principles provided in volume I for continuous outcomes, make it straightforward to transition to generalized linear mixed models for noncontinuous outcomes described in volume II.
## Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Third Edition

This book examines Stata's treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are "mixed" because they allow fixed and random effects, and they are "generalized" because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables. Volume I covers continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. Volume II discusses generalized linear mixed models for binary, categorical, count, and survival outcomes.
## Multilevel and Longitudinal Modeling Using Stata, Second Edition

This is a book about applied multilevel and longitudinal modeling. Other terms for multilevel models include hierarchical models, random-effects or random-coefficient models, mixed-effects models, or simply mixed models. Longitudinal data are also referred to as panel data, repeated measures, or cross-sectional time series. A popular type of multilevel model for longitudinal data is the growth-curve model. Our emphasis is on explaining the models and their assumptions, applying the methods to real data, and interpreting results.
## Handbook of Multilevel Analysis

This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.
## A Course in Item Response Theory and Modeling with Stata

Over the past several decades, item response theory (IRT) and item response modeling (IRM) have become increasingly popular in the behavioral, educational, social, business, marketing, clinical, and health sciences. In this book, Raykov and Marcoulides begin with a nontraditional approach to IRT and IRM that is based on their connections to classical test theory, (nonlinear) factor analysis, generalized linear modeling, and logistic regression. Application-oriented discussions follow next. These cover the one-, two-, and three-parameter logistic models, polytomous item response models (with nominal or ordinal items), item and test information functions, instrument construction and development, hybrid models, differential item functioning, and an introduction to multidimensional IRT and IRM. The pertinent analytic and modeling capabilities of Stata are thoroughly discussed, highlighted, and illustrated on empirical examples from behavioral and social research.
## Data Processing Mathematics

## Generalized Latent Variable Modeling

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wide range of estimation and prediction methods from biostatistics, psychometrics, econometrics, and statistics. They present exciting and realistic applications that demonstrate how researchers can use latent variable modeling to solve concrete problems in areas as diverse as medicine, economics, and psychology. The examples considered include many nonstandard response types, such as ordinal, nominal, count, and survival data. Joint modeling of mixed responses, such as survival and longitudinal data, is also illustrated. Numerous displays, figures, and graphs make the text vivid and easy to read. About the authors: Anders Skrondal is Professor and Chair in Social Statistics, Department of Statistics, London School of Economics, UK Sophia Rabe-Hesketh is a Professor of Educational Statistics at the Graduate School of Education and Graduate Group in Biostatistics, University of California, Berkeley, USA.
## Multilevel Modeling in Plain Language

Have you been told you need to do multilevel modeling, but you can't get past the forest of equations? Do you need the techniques explained with words and practical examples so they make sense? Help is here! This book unpacks these statistical techniques in easy-to-understand language with fully annotated examples using the statistical software Stata. The techniques are explained without reliance on equations and algebra so that new users will understand when to use these approaches and how they are really just special applications of ordinary regression. Using real life data, the authors show you how to model random intercept models and random coefficient models for cross-sectional data in a way that makes sense and can be retained and repeated. This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
## Interpreting and Visualizing Regression Models Using Stata

Michael Mitchell's Interpreting and Visualizing Regression Models Using Stata is a clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model in a clear fashion, regardless of the audience. As an example, many experienced researchers start to squirm when asked to give a simple explanation of the applied meaning of interactions in nonlinear models such as logistic regression. The tools in Mitchell's book make this task much more enjoyable and comprehensible. Mitchell starts with simple linear regression (which is simple in all ways), and then adds polynomials and discontinuities. This is followed by 2-way and 3-way interaction until interpretation of coefficients through words is difficult. By careful use of Stata's marginsplot command, Mitchell shows how well graphs can be used to show effects. He also includes careful verbal interpretation of coefficients to make communications complete. He then extends the methods from linear regression to various types of nonlinear regression, such as multilevel or survival models. A significant difference between this book and most others on regression models is that Mitchell spends quite some time on fitting and visualizing discontinuous models' models where the outcome can change value suddenly at thresholds. Such models are natural in settings such as education and policy evaluation, where graduation or policy changes can make sudden changes in income or revenue. This book is a worthwhile addition to the library of anyone involved in statistical consulting, teaching, or collaborative applied statistical environments.
## An Introduction to Survival Analysis Using Stata, Second Edition

An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject. This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata's new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression. After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata's survival analysis routines.
## Family Formation in 21st Century Australia

This book provides a detailed, up-to-date snapshot of Australian family formation, answering such questions as ‘what do our families look like?’ and ‘how have they come to be this way?’ The book applies sociological insights to a broad range of demographic trends, painting a comprehensive picture of the changing ways in which Australians are creating families. The first contemporary volume on the subject, Family Formation in 21st Century Australia chronicles significant changes in partnering and fertility. In the late 20th century, cohabitation, divorce and births outside marriage rose dramatically. Yet family formation patterns continue to evolve, requiring fresh analysis. Even since the turn of the century, divorce has stabilized and fertility has increased. Using information from the 2011 Australian Census and from large-scale surveys, leading Australian academics dissect recent trends in cohabitation, ‘living apart together’, marriage, interethnic partnering, relationship dissolution, repartnering, contraceptive use and fertility. Since there is more diversity in family formation patterns than ever before, the book also considers differences between groups within the Australian population. Which groups are more likely to marry, cohabit or have higher fertility? And how do patterns differ among indigenous, migrant or same sex attracted Australians?.
## Power Analysis of Trials with Multilevel Data

Power Analysis of Trials with Multilevel Data covers using power and sample size calculations to design trials that involve nested data structures. The book gives a thorough overview of power analysis that details terminology and notation, outlines key concepts of statistical power and power analysis, and explains why they are necessary in trial design. It guides you in performing power calculations with hierarchical data, which enables more effective trial design. The authors are leading experts in the field who recognize that power analysis has attracted attention from applied statisticians in social, behavioral, medical, and health science. Their book supplies formulae that allow statisticians and researchers in these fields to perform calculations that enable them to plan cost-efficient trials. The formulae can also be applied to other sciences. Using power analysis in trial design is increasingly important in a scientific community where experimentation is often expensive, competition for funding among researchers is intense, and agencies that finance research require proposals to give thorough justification for funding. This handbook shows how power analysis shapes trial designs that have high statistical power and low cost, using real-life examples. The book covers multiple types of trials, including cluster randomized trials, multisite trials, individually randomized group treatment trials, and longitudinal intervention studies. It also offers insight on choosing which trial is best suited to a given project. Power Analysis of Trials with Multilevel Data helps you craft an optimal research design and anticipate the necessary sample size of data to collect to give your research maximum effectiveness and efficiency.
## A Visual Guide to Stata Graphics, Second Edition

Whether you are new to Stata graphics or a seasoned veteran, A Visual Guide to Stata Graphics, Second Edition will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results. With over 900 illustrated examples and quick-reference tabs, this book quickly guides you to the information you need for creating and customizing high-quality graphs for any types of statistical data.
## Structural Equation Modeling

Structural Equation Modeling (SEM) has long been used in social work research, but the writing on the topic is typically fragmented and highly technical. This pocket guide fills a major gap in the literature by providing social work researchers and doctoral students with an accessible synthesis. The authors demonstrate two SEM programs with distinct user interfaces and capabilities (Amos and Mplus) with enough specificity that readers can conduct their own analyses without consulting additional resources. Examples from social work literature highlight best practices for the specification, estimation, interpretation, and modification of structural equation models. Unlike most sources on SEM, this book provides clear guidelines on how to evaluate SEM output and how to proceed when model fit is not acceptable.Oftentimes, confirmatory factor analysis and general structure modeling are the most flexible, powerful, and appropriate choices for social work data. Richly illustrated with figures, equations, matrices, and tables, this pocket guide empowers social workers with a set of defensible analysis strategies that allows for competent, confident use of SEM.
## Statistical Methods for Communication Science

Statistical Methods for Communication Science is the only statistical methods volume currently available that focuses exclusively on statistics in communication research. Writing in a straightforward, personal style, author Andrew F. Hayes offers this accessible and thorough introduction to statistical methods, starting with the fundamentals of measurement and moving on to discuss such key topics as sampling procedures, probability, reliability, hypothesis testing, simple correlation and regression, and analyses of variance and covariance. Hayes takes readers through each topic with clear explanations and illustrations. He provides a multitude of examples, all set in the context of communication research, thus engaging readers directly and helping them to see the relevance and importance of statistics to the field of communication. Highlights of this text include: *thorough and balanced coverage of topics; *integration of classical methods with modern "resampling" approaches to inference; *consideration of practical, "real world" issues; *numerous examples and applications, all drawn from communication research; *up-to-date information, with examples justifying use of various techniques; and *a CD with macros, data sets, figures, and additional materials. This unique book can be used as a stand-alone classroom text, a supplement to traditional research methods texts, or a useful reference manual. It will be invaluable to students, faculty, researchers, and practitioners in communication, and it will serve to advance the understanding and use of statistical methods throughout the discipline.
## Regression Models for Categorical Dependent Variables Using Stata, Second Edition

After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias.
## Maximum Likelihood Estimation with Stata, Third Edition

Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.
## Hierarchical Linear Models

Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: an intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.
## Data Management Using Stata

Using simple language and illustrative examples, this book comprehensively covers data management tasks that bridge the gap between raw data and statistical analysis. Rather than focus on clusters of commands, the author takes a modular approach that enables readers to quickly identify and implement the necessary task without having to access background information first. Each section in the chapters presents a self-contained lesson that illustrates a particular data management task via examples, such as creating data variables and automating error checking. The text also discusses common pitfalls and how to avoid them and provides strategic data management advice. Ideal for both beginning statisticians and experienced users, this handy book helps readers solve problems and learn comprehensive data management skills.
## Exploring Inequality in Europe

Europe has become a dominant frame for the generation, regulation and perception of social inequalities. This trend was solidified by the current economic crisis, which is characterized by increasing inequalities between central and peripheral countries and groups. By analysing the double polarization between winners and losers of the crisis, the segmentation of labour markets and the perceived quality of life in Europe, this book contributes to a better understanding of patterns and dynamics of inequality in an integrated Europe.

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*Continuous Responses, Third Edition*

Author: Sophia Rabe-Hesketh,Anders Skrondal

Publisher: Stata Press

ISBN: 9781597181037

Category: Mathematics

Page: 514

View: 5357

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Publisher: Stata Press

ISBN: 9781597181082

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ISBN: 1597180408

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Publisher: Springer Science & Business Media

ISBN: 9780387731865

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Publisher: N.A

ISBN: 9781597182669

Category: Item reponse modeling

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ISBN: N.A

Category: Computer programming

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Author: Anders Skrondal,Sophia Rabe-Hesketh

Publisher: CRC Press

ISBN: 9780203489437

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Publisher: SAGE

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*Applications and Data Analysis Methods*

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Publisher: SAGE

ISBN: 9780761919049

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ISBN: N.A

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ISBN: 1783476664

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