Multilevel modeling using R /
| Main Authors: | , , |
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| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton :
Chapman & Hall/CRC,
2019.
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| Edition: | Second edition. |
| Series: | Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Item Description: | <P><B>1: Linear Models</B> </P><P>Simple Linear Regression </P><P>Estimating Regression Models with Ordinary Least Squares </P><P>Distributional Assumptions Underlying Regression </P><P>Coefficient of Determination</P><P>Inference for Regression Parameters </P><P>Multiple Regression </P><P>Example of Simple Linear Regression by Hand </P><P>Regression in R </P><P>Interaction Terms in Regression </P><P>Categorical Independent Variables</P><P>Checking Regression Assumptions with R</P><P>Summary</P><P></P><P><B>2: An Introduction to Multilevel Data Structure</B> </P><P>Nested Data and Cluster Sampling Designs </P><P>Intraclass Correlation </P><P>Pitfalls of Ignoring Multilevel Data Structure </P><P><I>Multilevel Linear Models</I> </P><P>Random Intercept </P><P>Random Slopes </P><P>Centering </P><P>Basics of Parameter Estimation with MLMs </P><P>Maximum Likelihood Estimation </P><P>Restricted Maximum Likelihood Estimation </P><P>Assumptions Underlying MLMs </P><P>Overview of 2 level MLMs </P><P>Overview of 3 level MLMs</P><P>Overview of longitudinal designs and their relationships to MLMs</P><P>Summary</P><P></P><P><B>3: Fitting 2-level Models</B> </P><P>Simple (Intercept only) Multilevel Models </P><P>Interactions and Cross Level Interactions using R</P><P>Random Coefficients Models using R </P><P>Centering Predictors </P><P><I>Additional Options </P></I><P>Parameter Estimation Method </P><P>Estimation Controls </P><P>Comparing Model fit </P><P>Lme4 and hypothesis testing </P><P>Summary</P><P></P><P><B>4: 3 Level and Higher Models</B> </P><P>Defining simple 3-level Models using the lme4 package </P><P>Defining simple models with more than three levels in the lme4 package Random Coefficients models with Three or More Levels in the lme4 </P><P>Package</P><P>Summary</P><P></P><P>5<B>: Longitudinal Data Analysis using Multilevel Models</B> </P><P>The Multilevel Longitudinal Framework </P><P>Person Period Data Structure </P><P>Fitting Longitudinal Models using the lme4 package</P><P>Changing the Covariance Structure of Longitudinal Models </P><P>Benefits of Multilevel Modeling for Longitudinal Analysis </P><P>Summary</P><P></P><P><B>6: Graphing Data in Multilevel Contexts</B> </P><P>Plots for Linear Models</P><P>Plotting Nested Data </P><P>Using the Lattice Package </P><P>Plotting Model Results using the Effects Package </P><P>Summary</P><P></P><P><B>7: Brief Introduction to Generalized Linear Models </P></B><P>Logistic Regression Model for a Dichotomous Outcome Variable</P><P>Logistic Regression Model for an Ordinal Outcome Variable</P><P>Multinomial Logistic Regression </P><P><I>Models for Count Data</I> </P><P>Poisson Regression </P><P>Models for Overdispersed Count data </P><P>Summary</P><P></P><P><B>8: Multilevel Generalized Linear Models (MGLM)</B> </P><P><I>MGLMs for a Dichotomous Outcome Variable </P></I><P>Random Intercept Logistic Regression </P><P>Random Coefficient Logistic Regression </P><P>Inclusion of Additional level 1 and level 2 effects in MGLM </P><P><I>MLGM for an Ordinal Outcome Variable</I> </P><P>Random Intercept Logistic Regression</P><P><I>MGLM for Count Data</I> </P><P>Random Intercept Poisson Regression </P><P>Random Coefficient Poisson Regression </P><P>Inclusion of additional level-2 effects to the multilevel Poisson regression </P><P>model </P><P>Summary</P><P></P><P><B>9: Bayesian Multilevel Modeling </P></B><P>MCMCglmm For a Normally Distributed Response Variable </P><P>Including level-2 Predictors with MCMCglmm </P><P>User Defined Priors </P><P>MCMCglmm For a Dichotomous Dependent Variable </P><P>MCMCglmm for a Count Dependent Variable </P><P>Summary</P><P></P><P><B>10: Advanced Issues in Multilevel Modeling</P></B><P>Robust statistics in the multilevel context</P><P>Identifying potential outliers in single level data</P><P>Identifying potential outliers in multilevel data</P><P>Identifying potential multilevel outliers using R</P><P>Robust and Rank Based Estimation for multilevel models</P><P>Fitting Robust and Rank Based Multilevel Models in R</P><P>Multilevel Lasso</P><P>Fitting the Multilevel Lasso in R</P><P>Multivariate Multilevel Models</P><P>Multilevel Generalized Additive Models</P><P>Fitting GAMM using R</P><P>Predicting Level-2 Outcomes with Level-1 Variables</P><P>Power Analysis for Multilevel Models</P><P>Summary</P><P></P><B><P>Appendix:</B> <B>An Introduction to R</B> </P><P>Running Statistical Analyses in R </P><P>Reading Data into R </P><P>Missing Data </P><P>Types of Data </P><P>Additional R Environment Options </P><P></P> Electronic resource. |
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| Physical Description: | 1 online resource. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781351062244 1351062247 9781351062251 1351062255 9781351062237 1351062239 9781351062268 1351062263 |