Practical Multilevel Modeling Using R
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Practical Multilevel Modeling Using R
Author: Francis L. Huang
language: en
Publisher: SAGE Publications
Release Date: 2022-12-08
Practical Multilevel Modeling Using R provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The book covers all the basics but also important advanced topics such as diagnostics, detecting and handling heteroscedasticity, power analysis, and missing data handling methods. Unlike other detailed texts on MLM which are written at a very high level, this text with its applied focus and use of R software to run the analyses is much more suitable for students who have substantive research areas but are not training to be methodologists or statisticians. Each chapter concludes with a "Test Yourself" section, and solutions are available on the instructor website for the book. A companion R package is available for use with this text.
Levels of Explanation
Author: Katie Robertson
language: en
Publisher: Oxford University Press
Release Date: 2024-11-30
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. The different sciences furnish us with a wide variety of explanations: some work at macroscopic scales, some work at microscopic scales, and some operate across different levels. How do these different explanatory levels relate to one another, and what is an explanatory level in the first place? Over the last 50 years, more and more philosophers--both reductionists and anti-reductionists--no longer subscribe to the idea that the best explanation resides at the fundamental physical level. New challenges arise from the success of scientific explanations employing multi-level models which mix levels of explanation, from distinctive differences between levels structures in biology, cognitive science, and social science, from the apparently radical reimagining of the explanatory role of spacetime in our current best theories of fundamental physics, and from the enduring mystery of how higher-level explanations are possible in the first place. These questions naturally connect to classic philosophical ways of thinking about the relationships between levels: reduction, emergence, and fundamentality. This volume presents a snapshot of cutting-edge research on explanatory levels, from their conceptual foundations to the details of how they are used in scientific practice.
Multilevel Modeling Using R
Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader 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. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models, including rank based, heavy tailed distributions, and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT, Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.