Hierarchical Linear Models Applications And Data Analysis Methods Advanced Quantitative Techniques In The Social Sciences Book PDF, EPUB Download & Read Online Free

Hierarchical Linear Models
Author: Stephen W. Raudenbush, Anthony S. Bryk
Publisher: SAGE
ISBN: 076191904X
Pages: 485
Year: 2002
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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.
Hierarchical linear models
Author: Anthony S. Bryk, Stephen W. Raudenbush
Publisher: Sage Publications, Inc
Pages: 265
Year: 1992
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Much social and behavioral research involves hierarchical data structures. The effects of school characteristics on students, how differences in government policies from country to country influence demographic relations within them, and how individuals exposed to different environmental conditions develop over time are a few examples. This introductory text explicates the theory and use of hierarchical linear models through rich illustrative examples and lucid explanations.
Hierarchical linear models
Author: Anthony S. Bryk, Stephen W. Raudenbush
Publisher: Sage Publications, Inc
Pages: 265
Year: 1992
View: 1096
Read: 741
Much social and behavioral research involves hierarchical data structures. The effects of school characteristics on students, how differences in government policies from country to country influence demographic relations within them, and how individuals exposed to different environmental conditions develop over time are a few examples. This introductory text explicates the theory and use of hierarchical linear models through rich illustrative examples and lucid explanations.
Propensity Score Analysis
Author: Shenyang Guo, Mark W. Fraser
Publisher: SAGE
ISBN: 1452235007
Pages: 421
Year: 2014-07-11
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Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.
Multilevel Modeling of Educational Data
Author: Ann A. O'Connell, D. Betsy McCoach
Publisher: IAP
ISBN: 1607527294
Pages: 541
Year: 2008-04-01
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(sponsored by the Educational Statisticians, SIG) Multilevel Modeling of Educational Data, coedited 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 (EdStat 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 schoollevel characteristics on the incidence of bullying. Multilevel models provide an improvement over traditional singlelevel 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 handson 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.
Hierarchical Linear Modeling
Author: G. David Garson
Publisher: SAGE
ISBN: 1412998859
Pages: 371
Year: 2012-04-10
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This book provides a brief, easy-to-read guide to implementing hierarchical linear modelling using the three leading software platforms, followed by a set of application articles based on recent work published in leading journals and as part of doctoral dissertations. The "guide" portion consists of three chapters by the editor, covering basic to intermediate use of SPSS, SAS, and HLM for purposes for hierarchical linear modelling, while the "applications" portion consists of a dozen contributions in which the authors emphasize how-to and methodological aspects and show how they have used these techniques in practice.
Multilevel Analysis
Author: Joop J. Hox, Mirjam Moerbeek, Rens van de Schoot
Publisher: Routledge
ISBN: 1317308670
Pages: 348
Year: 2017-09-14
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Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.
Multilevel Modeling
Author: Douglas A. Luke
Publisher: SAGE
ISBN: 0761928790
Pages: 79
Year: 2004-07-08
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A practical introduction to multi-level modelling, this book offers an introduction to HLM & illustrations of how to use this technique to build models for hierarchical & longitudinal data.
Multilevel Analysis
Author: Tom A B Snijders, Roel J Bosker
Publisher: SAGE
ISBN: 144625433X
Pages: 368
Year: 2011-10-30
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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.
Multilevel Analysis for Applied Research
Author: Robert Bickel
Publisher: Guilford Press
ISBN: 1609181069
Pages: 355
Year: 2007-03-19
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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.
Multilevel Modeling for Social and Personality Psychology
Author: John B Nezlek
Publisher: SAGE
ISBN: 1446209458
Pages: 120
Year: 2011-02-15
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Electronic Inspection Copy available here The volume begins with a rationale for multilevel modeling (MLM). Different aspects of MLM such as centering and modeling error terms are discussed, and examining hypotheses within the multilevel framework is considered in detail. Step by step instructions for conducting multilevel analyses using the program HLM are presented, and these instructions are linked to data sets and program files on a website. The SAGE Library in Social and Personality Psychology Methods provides students and researchers with an understanding of the methods and techniques essential to conducting cutting-edge research. Each volume within the Library explains a specific topic and has been written by an active scholar (or scholars) with expertise in that particular methodological domain. Assuming no prior knowledge of the topic, the volumes are clear and accessible for all readers. In each volume, a topic is introduced, applications are discussed, and readers are led step by step through worked examples. In addition, advice about how to interpret and prepare results for publication are presented.
Regression Models for Categorical and Limited Dependent Variables
Author: J. Scott Long
Publisher: SAGE
ISBN: 0803973748
Pages: 297
Year: 1997-01-09
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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.
Author: Stephen W. Raudenbush
Publisher: Scientific Software International
ISBN: 0894980548
Pages: 297
Year: 2004
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Introducing Multilevel Modeling
Author: Ita G G Kreft, Jan de Leeuw
Publisher: SAGE
ISBN: 1446230929
Pages: 160
Year: 1998-04-07
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This is the first accessible and practical guide to using multilevel models in social research. Multilevel approaches are becoming increasingly important in social, behavioural, and educational research and it is clear from recent developments that such models are seen as being more realistic, and potentially more revealing, than ordinary regression models. While other books describe these multilevel models in considerable detail none focuses on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling. The authors' approach is user-oriented and the formal mathematics and statistics are kept to a minimum. Other key features include the use of worked examples using real data sets, analyzed using the leading computer package for multilevel modeling - "MLn." Discussion site at: http: \\www.stat.ucla.edu\phplib\w-agora\w-agora.phtml?bn=Sagebook Data files mentioned in the book are available from: http: \\www.stat.ucla.edu\ deleeuw\sagebook
Regression Analysis
Author: Richard A. Berk
Publisher: SAGE
ISBN: 0761929045
Pages: 259
Year: 2004
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Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved. Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can do, so that readers can better match regression analysis with useful empirical questions and improved policy-related research. "An interesting and lively text, rich in practical wisdom, written for people who do empirical work in the social sciences and their graduate students." --David A. Freedman, Professor of Statistics, University of California, Berkeley