• Mathematics

Data Analysis Using Regression and Multilevel/Hierarchical Models


Author: Andrew Gelman,Jennifer Hill
Publisher: Cambridge University Press
ISBN: 9780521686891
Category: Mathematics
Page: 625
View: 8228
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

    • Mathematics

Data Analysis Using Regression and Multilevel/Hierarchical Models


Author: Andrew Gelman,Jennifer Hill
Publisher: Cambridge University Press
ISBN: 1139460935
Category: Mathematics
Page: N.A
View: 9756
Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

    • Mathematics

Data Analysis Using Regression and Multilevel/Hierarchical Models


Author: Andrew Gelman,Jennifer Hill
Publisher: N.A
ISBN: 9780521867061
Category: Mathematics
Page: 625
View: 8525
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

    • Mathematics

Hierarchical Linear Models

Applications and Data Analysis Methods
Author: Stephen W. Raudenbush,Anthony S. Bryk
Publisher: SAGE
ISBN: 9780761919049
Category: Mathematics
Page: 485
View: 8230
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.

    • Political Science

Event History Modeling

A Guide for Social Scientists
Author: Janet M. Box-Steffensmeier,Bradford S. Jones
Publisher: Cambridge University Press
ISBN: 9780521546737
Category: Political Science
Page: 218
View: 4834
Event History Modeling provides an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences. The authors explain the foundational principles of event-history analysis, and analyse numerous examples which they estimate and interpret using standard statistical packages, such as STATA and S-Plus. They review recent and critical innovations in diagnostics, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. They also discuss common problems encountered with time-to-event data, and make recommendations regarding the implementation of duration modeling methods.

    • Psychology

Multilevel Analysis for Applied Research

It's Just Regression!
Author: Robert Bickel
Publisher: Guilford Press
ISBN: 1609181069
Category: Psychology
Page: 355
View: 1061
This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual-level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical techniques they already know, Robert Bickel emphasizes the parallels with more familiar regression models, shows how to do multilevel modeling using SPSS, and demonstrates how to interpret the results. He discusses the strengths and limitations of multilevel analysis and explains specific circumstances in which it offers (or does not offer) methodological advantages over more traditional techniques. Over 300 dataset examples from research on educational achievement, income attainment, voting behavior, and other timely issues are presented in numbered procedural steps.

    • Mathematics

Essential Mathematics for Political and Social Research


Author: Jeff Gill
Publisher: Cambridge University Press
ISBN: 0521834260
Category: Mathematics
Page: 448
View: 4977
This 2006 book addresses the comprehensive introduction to the mathematical principles needed by modern social scientists.

    • Social Science

Multilevel Modeling in Plain Language


Author: Karen Robson,David Pevalin
Publisher: SAGE
ISBN: 1473934311
Category: Social Science
Page: 160
View: 2228
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.

    • Mathematics

Regression Models for Categorical and Limited Dependent Variables


Author: J. Scott Long
Publisher: SAGE
ISBN: 9780803973749
Category: Mathematics
Page: 297
View: 5297
A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible.

    • Mathematics

Statistical Rethinking

A Bayesian Course with Examples in R and Stan
Author: Richard McElreath
Publisher: CRC Press
ISBN: 1315362619
Category: Mathematics
Page: 487
View: 1350
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    • Reference

Multilevel Analysis

An Introduction to Basic and Advanced Multilevel Modeling
Author: Tom A B Snijders,Roel J Bosker
Publisher: SAGE
ISBN: 144625433X
Category: Reference
Page: 368
View: 596
The Second Edition of this classic text introduces the main methods, techniques and issues involved in carrying out multilevel modeling and analysis. Snijders and Bosker's book is an applied, authoritative and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis. This book provides step-by-step coverage of: • multilevel theories • ecological fallacies • the hierarchical linear model • testing and model specification • heteroscedasticity • study designs • longitudinal data • multivariate multilevel models • discrete dependent variables There are also new chapters on: • missing data • multilevel modeling and survey weights • Bayesian and MCMC estimation and latent-class models. This book has been comprehensively revised and updated since the last edition, and now discusses modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and SuperMix. This is a must-have text for any student, teacher or researcher with an interest in conducting or understanding multilevel analysis. Tom A.B. Snijders is Professor of Statistics in the Social Sciences at the University of Oxford and Professor of Statistics and Methodology at the University of Groningen. Roel J. Bosker is Professor of Education and Director of GION, Groningen Institute for Educational Research, at the University of Groningen.

    • Social Science

Applied Regression Analysis and Generalized Linear Models


Author: John Fox
Publisher: SAGE Publications
ISBN: 1483321312
Category: Social Science
Page: 816
View: 9449
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.

    • Psychology

Applied Missing Data Analysis


Author: Craig K. Enders
Publisher: Guilford Press
ISBN: 1606236407
Category: Psychology
Page: 377
View: 1660
Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website (www.appliedmissingdata.com) includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists.

    • Mathematics

Teaching Statistics

A Bag of Tricks
Author: Andrew Gelman,Deborah Nolan
Publisher: Oxford University Press
ISBN: 0191088641
Category: Mathematics
Page: 384
View: 5548
Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, activities, examples, and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and has chapters such as 'First week of class'— with exercises to break the ice and get students talking; then descriptive statistics, graphics, linear regression, data collection (sampling and experimentation), probability, inference, and statistical communication. Part II gives tips on what works and what doesn't, how to set up effective demonstrations, how to encourage students to participate in class and to work effectively in group projects. Course plans for introductory statistics, statistics for social scientists, and communication and graphics are provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics, sampling, and data science.

    • Computers

Bootstrap Methods and Their Application


Author: A. C. Davison,D. V. Hinkley
Publisher: Cambridge University Press
ISBN: 9780521574716
Category: Computers
Page: 582
View: 8819
This book gives a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis. Applications include stratified data; finite populations; censored and missing data; linear, nonlinear, and smooth regression models; classification; time series and spatial problems. Special features of the book include: extensive discussion of significance tests and confidence intervals; material on various diagnostic methods; and methods for efficient computation, including improved Monte Carlo simulation. Each chapter includes both practical and theoretical exercises. Included with the book is a disk of purpose-written S-Plus programs for implementing the methods described in the text. Computer algorithms are clearly described, and computer code is included on a 3-inch, 1.4M disk for use with IBM computers and compatible machines. Users must have the S-Plus computer application. Author resource page: http://statwww.epfl.ch/davison/BMA/

    • Philosophy

Unifying Political Methodology

The Likelihood Theory of Statistical Inference
Author: Gary King
Publisher: University of Michigan Press
ISBN: 9780472085545
Category: Philosophy
Page: 274
View: 6649
Argues that likelihood theory is a unifying approach to statistical modeling in political science

    • Science

Mixed Effects Models and Extensions in Ecology with R


Author: Alain Zuur,Elena N. Ieno,Neil Walker,Anatoly A. Saveliev,Graham M. Smith
Publisher: Springer Science & Business Media
ISBN: 9780387874586
Category: Science
Page: 574
View: 1530
This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

    • Business & Economics

Regression

Models, Methods and Applications
Author: Ludwig Fahrmeir,Thomas Kneib,Stefan Lang,Brian Marx
Publisher: Springer Science & Business Media
ISBN: 3642343333
Category: Business & Economics
Page: 698
View: 5774
The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

    • Mathematics

Applied Longitudinal Analysis


Author: Garrett M. Fitzmaurice,Nan M. Laird,James H. Ware
Publisher: John Wiley & Sons
ISBN: 1118551796
Category: Mathematics
Page: 740
View: 9516
Praise for the First Edition ". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis." —Journal of the American Statistical Association Features newly developed topics and applications of the analysis of longitudinal data Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences. The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including: Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material. With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.

    • Mathematics

Applied Regression Analysis


Author: Norman R. Draper,Harry Smith
Publisher: John Wiley & Sons
ISBN: 1118625684
Category: Mathematics
Page: 736
View: 2181
An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.