Advances in Multilevel Modeling for Educational Research
Advances in Multilevel Modeling for Educational Research

Author: Jeffrey R. Harring

Publisher: IAP

Published: 2015-12-01

Total Pages: 413

ISBN-13: 1681233290

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The significance that practitioners are placing on the use of multilevel models is undeniable as researchers want to both accurately partition variance stemming from complex sampling designs and understand relations within and between variables describing the hierarchical levels of these nested data structures. Simply scan the applied literature and one can see evidence of this trend by noticing the number of articles adopting multilevel models as their primary modeling framework. Helping to drive the popularity of their use, governmental funding agencies continue to advocate the use of multilevel models as part of a comprehensive analytic strategy for conducting rigorous and relevant research to improve our nation’s education system. Advances in Multilevel Modeling for Educational Research: Addressing Practical Issues Found in Real?World Applications is a resource intended for advanced graduate students, faculty and/or researchers interested in multilevel data analysis, especially in education, social and behavioral sciences. The chapters are written by prominent methodological researchers across diverse research domains such as educational statistics, quantitative psychology, and psychometrics. Each chapter exposes the reader to some of the latest methodological innovations, refinements and state?of?the?art developments and perspectives in the analysis of multilevel data including current best practices of standard techniques. We believe this volume will be particularly appealing to researchers in domains including but not limited to: educational policy and administration, educational psychology including school psychology and special education, and clinical psychology. In fact, we believe this volume will be a desirable resource for any research area that uses hierarchically nested data. The book will likely be attractive to applied and methodological researchers in several professional organizations such as the American Educational Research Association (AERA), the American Psychological Association (APA), the American Psychological Society (APS), the Society for Research on Educational Effectiveness (SREE), and other related organizations.

Multilevel Analysis
Multilevel Analysis

Author: Tom A. B. Snijders

Publisher: SAGE

Published: 1999

Total Pages: 282

ISBN-13: 9780761958901

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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.

Multilevel Analysis of Educational Data
Multilevel Analysis of Educational Data

Author: R. Darrell Bock

Publisher: Elsevier

Published: 2014-06-28

Total Pages: 367

ISBN-13: 1483295605

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Multilevel Analysis of Educational Data - Bayesian methods - Empirical Bayes - Generalized least squares - Profile likelihoods - E-M algorithm - Fisher scoring procedures - Both educational and social science applications

Data Science in Education Using R
Data Science in Education Using R

Author: Ryan A. Estrellado

Publisher: Routledge

Published: 2020-10-26

Total Pages: 331

ISBN-13: 1000200906

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Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.

Multilevel Analysis for Applied Research
Multilevel Analysis for Applied Research

Author: Robert Bickel

Publisher: Guilford Press

Published: 2007-03-19

Total Pages: 385

ISBN-13: 1609181069

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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
Multilevel Modeling

Author: Douglas A. Luke

Publisher: SAGE Publications

Published: 2019-12-13

Total Pages: 96

ISBN-13: 1544310285

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Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.

Classification, Data Analysis, and Data Highways
Classification, Data Analysis, and Data Highways

Author: Ingo Balderjahn

Publisher: Springer Science & Business Media

Published: 2013-03-12

Total Pages: 416

ISBN-13: 3642720870

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This volume presents 43 articles dealing with models and methods of data analysis and classification, statistics and stochastics, information systems and WWW- and Internet-related topics as well as many applications. These articles are selected from more than 100 papers presented at the 21st Annual Conference of the Gesellschaft für Klassifikation. Based on the submitted and revised papers six sections have been arranged: - Classification and Data Analysis - Mathematical and Statistical Methods - World Wide Web and the Internet - Speech and Pattern Recognition - Marketing.

The SAGE Handbook of Multilevel Modeling
The SAGE Handbook of Multilevel Modeling

Author: Marc A. Scott

Publisher: SAGE

Published: 2013-08-31

Total Pages: 954

ISBN-13: 1473971314

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In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.

Methodology and Epistemology of Multilevel Analysis
Methodology and Epistemology of Multilevel Analysis

Author: D. Courgeau

Publisher: Springer Science & Business Media

Published: 2003-07-31

Total Pages: 268

ISBN-13: 9781402014758

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The purpose of the multilevel approach is to understand individual behaviors taking into account the social context in which they occur. This book deals with concepts and methods underlying this approach. This book is of interest to a broad audience of social scientists, statisticians and philosophers concerned with new issues raised by the multilevel approach, and more generally with explanation in the social sciences.