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Hierarchical Linear Modeling
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Book Synopsis Hierarchical Linear Models by : Anthony S. Bryk
Download or read book Hierarchical Linear Models written by Anthony S. Bryk and published by SAGE Publications, Incorporated. This book was released on 1992 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hierarchical Linear Models launches a new Sage series, Advanced Quantitative Techniques in the Social Sciences. This introductory text explicates the theory and use of hierarchical linear models (HLM) through rich, illustrative examples and lucid explanations. The presentation remains reasonably nontechnical by focusing on three general research purposes - improved estimation of effects within an individual unit, estimating and testing hypotheses about cross-level effects, and partitioning of variance and covariance components among levels. This innovative volume describes use of both two and three level models in organizational research, studies of individual development and meta-analysis applications, and concludes with a formal derivation of the statistical methods used in the book.
Book Synopsis Hierarchical Linear Modeling by : G. David Garson
Download or read book Hierarchical Linear Modeling written by G. David Garson and published by SAGE. This book was released on 2013 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a brief, easy-to-read guide to implementing hierarchical linear modeling using three leading software platforms, followed by a set of original how-to applications articles following a standardard instructional format. The "guide" portion consists of five chapters by the editor, providing an overview of HLM, discussion of methodological assumptions, and parallel worked model examples in SPSS, SAS, and HLM software. The "applications" portion consists of ten contributions in which authors provide step by step presentations of how HLM is implemented and reported for introductory to intermediate applications.
Book Synopsis Hierarchical Linear Models by : Stephen W. Raudenbush
Download or read book Hierarchical Linear Models written by Stephen W. Raudenbush and published by SAGE. This book was released on 2002 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Analysis Using Regression and Multilevel/Hierarchical Models by : Andrew Gelman
Download or read book Data Analysis Using Regression and Multilevel/Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Book Synopsis Data Analysis Using Hierarchical Generalized Linear Models with R by : Youngjo Lee
Download or read book Data Analysis Using Hierarchical Generalized Linear Models with R written by Youngjo Lee and published by CRC Press. This book was released on 2017-07-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.
Download or read book HLM 5 written by Stephen W. Raudenbush and published by . This book was released on 2001 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multilevel Modeling by : G. David Garson
Download or read book Multilevel Modeling written by G. David Garson and published by SAGE Publications. This book was released on 2019-07-31 with total page 910 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R & HLMTM provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson’s step-by-step instructions for software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.
Book Synopsis Multilevel Analysis by : Tom A. B. Snijders
Download or read book Multilevel Analysis written by Tom A. B. Snijders and published by SAGE. This book was released on 1999 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multilevel analysis covers all the main methods, techniques and issues for carrying out multilevel modeling and analysis. The approach is applied, and less mathematical than many other textbooks.
Download or read book Bayes Rules! written by Alicia A. Johnson and published by CRC Press. This book was released on 2022-03-03 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.
Author :Stephen W. Raudenbush Publisher :Scientific Software International ISBN 13 :9780894980541 Total Pages :324 pages Book Rating :4.9/5 (85 download)
Download or read book HLM 6 written by Stephen W. Raudenbush and published by Scientific Software International. This book was released on 2004 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis SPSS Statistics for Data Analysis and Visualization by : Keith McCormick
Download or read book SPSS Statistics for Data Analysis and Visualization written by Keith McCormick and published by John Wiley & Sons. This book was released on 2017-04-17 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive deeper into SPSS Statistics for more efficient, accurate,and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goesbeyond the basics of SPSS Statistics to show you advancedtechniques that exploit the full capabilities of SPSS. The authorsexplain when and why to use each technique, and then walk youthrough the execution with a pragmatic, nuts and bolts example.Coverage includes extensive, in-depth discussion of advancedstatistical techniques, data visualization, predictive analytics,and SPSS programming, including automation and integration withother languages like R and Python. You'll learn the best methods topower through an analysis, with more efficient, elegant, andaccurate code. IBM SPSS Statistics is complex: true mastery requires a deepunderstanding of statistical theory, the user interface, andprogramming. Most users don't encounter all of the methods SPSSoffers, leaving many little-known modules undiscovered. This bookwalks you through tools you may have never noticed, and shows youhow they can be used to streamline your workflow and enable you toproduce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create bettervisualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient,more powerful code These "hidden tools" can help you produce charts that simplywouldn't be possible any other way, and the support for otherprogramming languages gives you better options for solving complexproblems. If you're ready to take advantage of everything thispowerful software package has to offer, SPSS Statistics for DataAnalysis and Visualization is the expert-led training youneed.
Book Synopsis Multilevel Modeling of Educational Data by : Ann A. O'Connell
Download or read book Multilevel Modeling of Educational Data written by Ann A. O'Connell and published by IAP. This book was released on 2008-04-01 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: (sponsored by the Educational Statisticians, SIG) Multilevel Modeling of Educational Data, co-edited by Ann A. O’Connell, Ed.D., and D. Betsy McCoach, Ph.D., is the next volume in the series: Quantitative Methods in Education and the Behavioral Sciences: Issues, Research and Teaching (Information Age Publishing), sponsored by the Educational Statisticians' Special Interest Group (Ed-Stat SIG) of the American Educational Research Association. The use of multilevel analyses to examine effects of groups or contexts on individual outcomes has burgeoned over the past few decades. Multilevel modeling techniques allow educational researchers to more appropriately model data that occur within multiple hierarchies (i.e.- the classroom, the school, and/or the district). Examples of multilevel research problems involving schools include establishing trajectories of academic achievement for children within diverse classrooms or schools or studying school-level characteristics on the incidence of bullying. Multilevel models provide an improvement over traditional single-level approaches to working with clustered or hierarchical data; however, multilevel data present complex and interesting methodological challenges for the applied education research community. In keeping with the pedagogical focus for this book series, the papers this volume emphasize applications of multilevel models using educational data, with chapter topics ranging from basic to advanced. This book represents a comprehensive and instructional resource text on multilevel modeling for quantitative researchers who plan to use multilevel techniques in their work, as well as for professors and students of quantitative methods courses focusing on multilevel analysis. Through the contributions of experienced researchers and teachers of multilevel modeling, this volume provides an accessible and practical treatment of methods appropriate for use in a first and/or second course in multilevel analysis. A supporting website links chapter examples to actual data, creating an opportunity for readers to reinforce their knowledge through hands-on data analysis. This book serves as a guide for designing multilevel studies and applying multilevel modeling techniques in educational and behavioral research, thus contributing to a better understanding of and solution for the challenges posed by multilevel systems and data.
Book Synopsis Regression & Linear Modeling by : Jason W. Osborne
Download or read book Regression & Linear Modeling written by Jason W. Osborne and published by SAGE Publications. This book was released on 2016-03-24 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. The author returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
Book Synopsis Piecewise Linear Modeling and Analysis by : Domine Leenaerts
Download or read book Piecewise Linear Modeling and Analysis written by Domine Leenaerts and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Piecewise Linear (PL) approximation of non-linear behaviour is a well-known technique in synthesis and analysis of electrical networks. However, the PL description should be efficient in data storage and the description should allow simple retrieval of the stored information. Furthermore, it would be useful if the model description could handle a large class of piecewise linear mappings. Piecewise Linear Modeling and Analysis explains in detail all possible model descriptions for efficiently storing piecewise linear functions, starting with the Chua descriptions. Detailed explanation on how the model parameter can be obtained for a given mapping is provided and demonstrated by examples. The models are ranked to compare them and to show which model can handle the largest class of PL mappings. All model descriptions are implicitly related to the Linear Complementarity Problem and most solution techniques for this problem, like Katzenelson and Lemke, are discussed according to examples that are explained in detail. To analyse PL electrical networks a simulator is mandatory. Piecewise Linear Modeling and Analysis provides a detailed outline of a possible PL simulator, including pseudo-programming code. Several simulation domains like transient, AC and distortion are discussed. The book explains the attractive features of PL simulators with respect to mixed-level and mixed-signal simulation while paying due regard also to hierarchical simulation. Piecewise Linear Modeling and Analysis shows in detail how many existing components in electrical networks can be modeled. These range from digital logic and analog basic elements such as transistors to complex systems like Phase-Locked Loops and detection systems. Simulation results are also provided. The book concludes with a discussion on how to find multiple solutions for PL functions or networks. Again, the most common techniques are outlined using clear examples. Piecewise Linear Modeling and Analysis is an indispensable guide for researchers and designers interested in network theory, network synthesis and network analysis.
Book Synopsis Beyond Multiple Linear Regression by : Paul Roback
Download or read book Beyond Multiple Linear Regression written by Paul Roback and published by CRC Press. This book was released on 2021-01-14 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Book Synopsis Probability and Bayesian Modeling by : Jim Albert
Download or read book Probability and Bayesian Modeling written by Jim Albert and published by CRC Press. This book was released on 2019-12-06 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
Book Synopsis Generalized Linear Models by : Jeff Gill
Download or read book Generalized Linear Models written by Jeff Gill and published by SAGE Publications. This book was released on 2000-08-07 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data and the author includes his Web address where additional resources can be found.
