MULTILEVEL MODELING USING R.

Author/creator FINCH, W. HOLMES. BOLIN, JOCELYN E.. KELLEY, KEN
Format Electronic
Publication Info[S.l.] : CHAPMAN & HALL CRC, 2024.
Description1 online resource
Supplemental ContentEBSCOhost
Subjects

SeriesChapman and Hall/CRC Statistics in the Social and Behavioral Sciences Series
Chapman and Hall/CRC Statistics in the Social and Behavioral Sciences Series.
Contents Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- About the Authors -- 1. Linear Models -- Simple Linear Regression -- Estimating Regression Models with Ordinary Least Squares -- Distributional Assumptions Underlying Regression -- Coefficient of Determination -- Inference for Regression Parameters -- Multiple Regression -- Example of Simple Linear Regression by Hand -- Regression in R -- Interaction Terms in Regression -- Categorical Independent Variables -- Checking Regression Assumptions with R -- Summary
Contents 2. An Introduction to Multilevel Data Structure -- Nested Data and Cluster Sampling Designs -- Intraclass Correlation -- Pitfalls of Ignoring Multilevel Data Structure -- Multilevel Linear Models -- Random Intercept -- Random Slopes -- Centering -- Basics of Parameter Estimation with MLMs -- Maximum Likelihood Estimation -- Restricted Maximum Likelihood Estimation -- Assumptions Underlying MLMs -- Overview of Level-2 MLMs -- Overview of Level-3 MLMs -- Overview of Longitudinal Designs and Their Relationship to MLMs -- Summary -- 3. Fitting Level-2 Models in R
Contents Simple (Intercept Only) Multilevel Models -- Interactions and Cross-Level Interactions Using R -- Random Coefficients Models Using R -- Centering Predictors -- Additional Options -- Parameter Estimation Method -- Estimation Controls -- Comparing Model Fit -- Lme4 and Hypothesis Testing -- Summary -- Notes -- 4. Level-3 and Higher Models -- Defining Simple Level-3 Models Using the lme4 Package -- Defining Simple Models with More Than Three Levels in the lme4 Package -- Random Coefficients Models with Three or More Levels in the lme4 Package -- Summary -- Notes
Contents 5. Longitudinal Data Analysis Using Multilevel Models -- The Multilevel Longitudinal Framework -- Person Period Data Structure -- Fitting Longitudinal Models Using the lme4 package -- Benefits of Using Multilevel Modeling for Longitudinal Analysis -- Summary -- Notes -- 6. Graphing Data in Multilevel Contexts -- Plots for Linear Models -- Plotting Nested Data -- Using the Lattice Package -- Plotting Model Results Using the Effects Package -- Summary -- 7. Brief Introduction to Generalized Linear Models -- Logistic Regression Model for a Dichotomous Outcome Variable
Contents Logistic Regression Model for an Ordinal Outcome Variable -- Multinomial Logistic Regression -- Models for Count Data -- Poisson Regression -- Models for Overdispersed Count Data -- Summary -- 8. Multilevel Generalized Linear Models (MGLMs) -- MGLMs for a Dichotomous Outcome Variable -- Random Intercept Logistic Regression -- Random Coefficient Logistic Regression -- Inclusion of Additional Level-1 and Level-2 Effects in MGLM -- MGLM for an Ordinal Outcome Variable -- Random Intercept Logistic Regression -- MGLM for Count Data -- Random Intercept Poisson Regression
Abstract Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.
Issued in other formPrint version: 1032363967 9781032363967 1032363940 9781032363943
Issued in other formPrint version: Finch, W. Holmes Multilevel Modeling Using R Milton : CRC Press LLC,c2024 9781032363943
ISBN9781040004531 (electronic bk.)
ISBN1040004539 (electronic bk.)
ISBN9781040004524 (electronic bk.)
ISBN1040004520 (electronic bk.)

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