Comparing Regression Coefficients Between Nested Models For Clustered Data With Generalized Estimating Equations
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Comparing Regression Coefficients Between Nested Models For Clustered Data With Generalized Estimating Equations
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Author : Jun Yan
language : en
Publisher:
Release Date : 2010
Comparing Regression Coefficients Between Nested Models For Clustered Data With Generalized Estimating Equations written by Jun Yan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematical statistics categories.
Topics In Modelling Of Clustered Data
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Author : Marc Aerts
language : en
Publisher: CRC Press
Release Date : 2002-05-29
Topics In Modelling Of Clustered Data written by Marc Aerts and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-05-29 with Mathematics categories.
Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and s
Comparison Of Methods For Estimating The Variance Of The Regression Coefficient From Clustered Surveys
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Author : Rita Faye Engelhardt
language : en
Publisher:
Release Date : 1991
Comparison Of Methods For Estimating The Variance Of The Regression Coefficient From Clustered Surveys written by Rita Faye Engelhardt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Analysis of variance categories.
The Sage Handbook Of Multilevel Modeling
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Author : Marc A. Scott
language : en
Publisher: SAGE
Release Date : 2013-08-31
The Sage Handbook Of Multilevel Modeling written by Marc A. Scott and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-31 with Social Science categories.
In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Methods Of Statistical Model Estimation
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Author : Joseph Hilbe
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Methods Of Statistical Model Estimation written by Joseph Hilbe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.
Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting.The text presents algorith
Practical Multilevel Modeling Using R
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Author : Francis L. Huang
language : en
Publisher: SAGE Publications
Release Date : 2022-12-08
Practical Multilevel Modeling Using R written by Francis L. Huang and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-08 with Social Science categories.
Practical Multilevel Modeling Using R provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The book covers all the basics but also important advanced topics such as diagnostics, detecting and handling heteroscedasticity, power analysis, and missing data handling methods. Unlike other detailed texts on MLM which are written at a very high level, this text with its applied focus and use of R software to run the analyses is much more suitable for students who have substantive research areas but are not training to be methodologists or statisticians. Each chapter concludes with a "Test Yourself" section, and solutions are available on the instructor website for the book. A companion R package is available for use with this text.
Data Analytics For The Social Sciences
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Author : G. David Garson
language : en
Publisher: Routledge
Release Date : 2021-11-30
Data Analytics For The Social Sciences written by G. David Garson and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-30 with Psychology categories.
Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.
Applied Regression And Anova Using Sas
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Author : Patricia F. Moodie
language : en
Publisher: CRC Press
Release Date : 2022-06-07
Applied Regression And Anova Using Sas written by Patricia F. Moodie and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-07 with Mathematics categories.
Applied Regression and ANOVA Using SAS® has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps. Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided. Features: •Statistical concepts presented in words without matrix algebra and calculus •Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics •Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection •Suggestions of alternative approaches when a method’s ideal inference conditions are unreasonable for one’s data This book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics.
Fixed Effects Regression Methods For Longitudinal Data Using Sas
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Author : Paul D. Allison
language : en
Publisher:
Release Date : 2019-07-12
Fixed Effects Regression Methods For Longitudinal Data Using Sas written by Paul D. Allison and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-12 with categories.
Fixed Effects Regression Methods for Longitudinal Data Using SAS, written by Paul Allison, is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regression models for count data, and PROC CALIS for estimating fixed effects structural equation models. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required.
Hierarchical Linear Models
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Author : Stephen W. Raudenbush
language : en
Publisher: SAGE
Release Date : 2002
Hierarchical Linear Models written by Stephen W. Raudenbush and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Social Science categories.
New edition of a text in which Raudenbush (U. of Michigan) and Bryk (sociology, U. of Chicago) provide examples, explanations, and illustrations of the theory and use of hierarchical linear models (HLM). New material in Part I (Logic) includes information on multivariate growth models and other topics.