Multilevel Modeling (Quantitative Applications in the Social Sciences)

Multilevel Modeling (Quantitative Applications in the Social Sciences)

Taking a practical, hands-on approach to multilevel modeling, this book provides readers with an accessible and concise introduction to HLM and how to use the technique to build models for hierarchical and longitudinal data. Each section of the book answers a basic question about multilevel modeling, such as, "How do you determine how well the model fits the data?" After reading this book, readers will understand research design issues associated with multilevel models, be able to accurately interpret the results of multilevel analyses, and build simple cross-sectional and longitudinal multilevel models.

  • Brand: imusti
  • ASIN: 0761928790

Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling

Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling

The Second Edition of this classic text introduces the main methods, techniques, and issues involved in carrying out multilevel modeling and analysis. Snijders and Boskers′ 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 has been comprehensively revised and updated since the last edition, and now includes guides to modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and Mix.

  • Brand: imusti
  • ASIN: 184920201X

Multilevel Modeling in Plain Language

Multilevel Modeling in Plain Language

Multilevel Modeling in Plain Language

  • Brand: imusti
  • ASIN: 0857029169

Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models 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. Author resource page: http://www.stat.columbia.edu/~gelman/arm/

  • Brand: Andrew Gelman
  • ASIN: 052168689X

Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Third Edition

Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Third Edition

This book examines Stata's treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are "mixed" because they allow fixed and random effects, and they are "generalized" because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables. Volume I covers continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. Volume II discusses generalized linear mixed models for binary, categorical, count, and survival outcomes.

  • Brand: Rabe Hesketh Sophia Skrondal
  • ASIN: 1597181080
  • UPC: 001597181080

Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

A powerful tool for analyzing nested designs in a variety of fields, multilevel/hierarchical modeling allows researchers to account for data collected at multiple levels. Multilevel Modeling Using R provides you with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The book concludes with Bayesian fitting of multilevel models. For those new to R, the appendix provides an introduction to this system that covers basic R knowledge necessary to run the models in the book. Through the R code and detailed explanations provided, this book gives you the tools to launch your own investigations in multilevel modeling and gain insight into your research.

  • Brand: imusti
  • ASIN: 1466515856

Multilevel and Longitudinal Modeling with IBM SPSS (Quantitative Methodology Series)

Multilevel and Longitudinal Modeling with IBM SPSS (Quantitative Methodology Series)

This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110. Highlights of the new edition include: Updated throughout to reflect IBM SPSS Version 21. Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change and longitudinal experimental designs (Ch.5). Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental) over time (Ch.6). New examples specifying multiple latent constructs and parallel growth processes (Ch. 7). Discussion of alternatives for dealing with ...

  • Brand: imusti
  • ASIN: 0415817110

Multilevel and Longitudinal Modeling Using Stata, Volume I: Continuous Responses, Third Edition (Volume 1)

Multilevel and Longitudinal Modeling Using Stata, Volume I: Continuous Responses, Third Edition (Volume 1)

Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. The first part provides a review of the methods of linear regression. The second part provides an in-depth coverage of the two-level models, the simplest extensions of a linear regression model. The mixed-model foundation and the in-depth coverage of the mixed-model principles provided in volume I for continuous outcomes, make it straightforward to transition to generalized linear mixed models for noncontinuous outcomes described in volume II.

  • Brand: Brand: Stata Press
  • ASIN: 159718103X

Multilevel Modeling of Categorical Outcomes Using IBM SPSS (Quantitative Methodology Series)

Multilevel Modeling of Categorical Outcomes Using IBM SPSS (Quantitative Methodology Series)

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix.  Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of ...

  • Brand: Ronald H Heck
  • ASIN: 1848729561

Multilevel Modeling of Educational Data (Quantitative Methods in Education and the Behavioral Science)

Multilevel Modeling of Educational Data (Quantitative Methods in Education and the Behavioral Science)

A volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching Series Editor Ron Serlin, University of Wisconsin (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; howev...

  • Brand: Brand: Information Age Publishing
  • ASIN: 1593116845
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An Introduction to Multilevel Modeling - basic terms and research examples - John Nezlek

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An Introduction to Multilevel Modeling - basic terms and research examples John B. Nezlek, College of William & Mary Warsaw, 15.10.2014

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What is Multilevel Modelling? by Mark Tranmer

Multilevel Modeling

Multilevel modelling is a quantitative statistical method to investigate variations and relationships for variables of interest, taking into account population structure and dependencies. These population structures may be hierarchical, such as pupils in classes in schools. For more methods reso...

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Recorded: Fall 2015 Lecturer: Dr. Erin M. Buchanan This video gives an example of multilevel modeling in R - covers data screening in wide format, melting to long format, nlme for analysis, and interpretation of predictors. Lecture materials and assignment available at statstools.com. http://st...

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