• 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: 2115
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.

    • Mathematics

Hierarchical linear models

applications and data analysis methods
Author: Anthony S. Bryk,Stephen W. Raudenbush
Publisher: Sage Publications, Inc
ISBN: N.A
Category: Mathematics
Page: 265
View: 6158
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.

    • Mathematics

Hierarchical linear models

applications and data analysis methods
Author: Anthony S. Bryk,Stephen W. Raudenbush
Publisher: Sage Publications, Inc
ISBN: N.A
Category: Mathematics
Page: 265
View: 7866
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.

    • Mathematics

Propensity Score Analysis


Author: Shenyang Guo,Mark W. Fraser
Publisher: SAGE
ISBN: 1452235007
Category: Mathematics
Page: 421
View: 6497
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.

    • Education

Multilevel Modeling of Educational Data


Author: Ann A. O'Connell,D. Betsy McCoach
Publisher: IAP
ISBN: 1607527294
Category: Education
Page: 541
View: 462
(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.

    • Mathematics

Regression Models for Categorical and Limited Dependent Variables


Author: J. Scott Long
Publisher: SAGE
ISBN: 9780803973749
Category: Mathematics
Page: 297
View: 9933
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.

    • Psychology

Multilevel Analysis

Techniques and Applications, Third Edition
Author: Joop J. Hox,Mirjam Moerbeek,Rens van de Schoot
Publisher: Routledge
ISBN: 1317308670
Category: Psychology
Page: 348
View: 6329
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.

    • Mathematics

Multilevel Modeling


Author: Douglas A. Luke
Publisher: SAGE
ISBN: 9780761928799
Category: Mathematics
Page: 79
View: 4665
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.

    • Computers

Hierarchical Linear Modeling

Guide and Applications
Author: G. David Garson
Publisher: SAGE
ISBN: 1412998859
Category: Computers
Page: 371
View: 996
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.

    • Psychology

Multilevel Analysis for Applied Research

It's Just Regression!
Author: Robert Bickel
Publisher: Guilford Press
ISBN: 1609181069
Category: Psychology
Page: 355
View: 8184
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.

    • 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: 4104
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.

    • Datenanalyse

HLM 6

Hierarchical Linear and Nonlinear Modeling
Author: Stephen W. Raudenbush
Publisher: Scientific Software International
ISBN: 9780894980541
Category: Datenanalyse
Page: 297
View: 9139

    • Psychology

Multilevel Modeling for Social and Personality Psychology


Author: John B Nezlek
Publisher: SAGE
ISBN: 1446209458
Category: Psychology
Page: 120
View: 3538
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.

    • Technology & Engineering

Quantitative Methods and Applications in GIS


Author: Fahui Wang
Publisher: CRC Press
ISBN: 142000428X
Category: Technology & Engineering
Page: 304
View: 6592
Quantitative Methods and Applications in GIS integrates GIS, spatial analysis, and quantitative methods to address various issues in socioeconomic studies and public policy. Methods range from basic regression analysis to advanced topics such as linear programming and system of equations. Applications vary from typical themes in urban and regional analysis - trade area analysis, accessibility measures, analysis of regional growth patterns, land use simulation - to issues related to crime and health analyses. The book covers common tasks such as distance and travel time estimation, spatial smoothing and interpolation, and accessibility measures. It also covers the major issues that are encountered in spatial analysis including modifiable areal unit problems, rate estimate of rare events in small populations, and spatial autocorrelation. Each chapter has one subject theme, introduces the method (or a group of related methods) most relevant to the theme, and then uses case studies to implement the method in a GIS environment.

Introducing Multilevel Modeling


Author: Ita G G Kreft,Jan de Leeuw
Publisher: SAGE
ISBN: 9781446230923
Category:
Page: 160
View: 3864
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

    • Mathematics

Regression Analysis

A Constructive Critique
Author: Richard A. Berk
Publisher: SAGE
ISBN: 0761929045
Category: Mathematics
Page: 259
View: 9653
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

    • Social Science

Monte Carlo Simulation and Resampling Methods for Social Science


Author: Thomas M. Carsey,Jeffrey J. Harden
Publisher: SAGE Publications
ISBN: 1483324923
Category: Social Science
Page: 304
View: 1063
Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.

    • Mathematics

Data Analysis Using Hierarchical Generalized Linear Models with R


Author: Youngjo Lee,Lars Ronnegard,Maengseok Noh
Publisher: CRC Press
ISBN: 135181155X
Category: Mathematics
Page: 322
View: 5531
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.

    • Business & Economics

New Quantitative Techniques for Economic Analysis


Author: Giorgio P. Szegö
Publisher: Academic Press
ISBN: 1483273466
Category: Business & Economics
Page: 336
View: 5157
Economic Theory, Econometrics, and Mathematical Economics: New Quantitative Techniques for Economic Analysis provides a critical appraisal of the results, the limits, and the developments of well-established quantitative techniques. This book presents a detailed analysis of the quantitative techniques for economic analysis. Organized into four parts encompassing 16 chapters, this book begins with an overview of the general questions concerning models and model making. This text then provides the main results and various interesting economic applications of some quantitative techniques that have not been widely used in the economic field. Other chapters consider the principle of optimality in dynamic programing wherein the infinite sequence of consumption-saving decisions can be reduced to one decision. This book discusses as well the methods for online control and management of large-scale systems. The final chapter deals with special problems. This book is a valuable resource for economists, social scientists, epistemologists, economic historians, and research workers.

    • Social Science

Multilevel Modeling in Plain Language


Author: Karen Robson,David Pevalin
Publisher: SAGE
ISBN: 1473934311
Category: Social Science
Page: 160
View: 4503
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.