rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Multilevel modeling using R
  name:
    name part: Finch, W. Holmes (William Holmes)
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Bolin, Jocelyn E.
    role:
      role term: author.
  name:
    name part: Kelley, Ken
    name part: (Professor of information technology)
    role:
      role term: author.
  name:
    name part: ProQuest (Firm)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2019
    edition: Second edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  table of contents: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- 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 -- 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 Two-Level MLMs -- Overview of Three-Level MLMs -- Overview of Longitudinal Designs and Their Relationship to MLMs -- Summary -- 3: Fitting Two-Level Models in R -- 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 -- Note -- 4: Three-Level and Higher Models -- Defining Simple Three-Level 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 -- Note -- 5: Longitudinal Data Analysis Using Multilevel Models -- The Multilevel Longitudinal Framework -- Person Period Data Structure -- Fitting Longitudinal Models Using the lme4 Package.
  table of contents: Benefits of Using Multilevel Modeling for Longitudinal Analysis -- Summary -- Note -- 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 -- 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 Coefficients 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 -- Random Coefficient Poisson Regression -- Inclusion of Additional Level-2 Effects to the Multilevel Poisson Regression Model -- Summary -- 9: Bayesian Multilevel Modeling -- MCMCglmm for a Normally Distributed Response Variable -- Including Level-2 Predictors with MCMCglmm -- User Defined Priors -- MCMCglmm for a Dichotomous Dependent Variable -- MCMCglmm for a Count-Dependent Variable -- Summary -- 10: Advanced Issues in Multilevel Modeling -- Robust Statistics in the Multilevel Context -- Identifying Potential Outliers in Single-Level Data -- Identifying Potential Outliers in Multilevel Data -- Identifying Potential Multilevel Outliers Using R -- Robust and Rank-Based Estimation for Multilevel Models -- Fitting Robust and Rank-Based Multilevel Models in R -- Cauchy -- Slash -- Contaminated -- Multilevel Lasso -- Fitting the Multilevel Lasso in R -- Multivariate Multilevel Models.
  table of contents: Multilevel Generalized Additive Models -- Fitting GAMM using R -- Predicting Level-2 Outcomes with Level-1 Variables -- Power Analysis for Multilevel Models -- Summary -- References -- Index.
  note: W. Holmes Finch, Jocelyn E. Bolin, Ken Kelley.
  note: 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 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 2 level MLMs Overview of 3 level MLMs Overview of longitudinal designs and their relationships to MLMs Summary 3: Fitting 2-level Models 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 4: 3 Level and Higher Models Defining simple 3-level 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 5: Longitudinal Data Analysis using Multilevel Models The Multilevel Longitudinal Framework Person Period Data Structure Fitting Longitudinal Models using the lme4 package Changing the Covariance Structure of Longitudinal Models Benefits of Multilevel Modeling for Longitudinal Analysis Summary 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…
  note: Electronic resource.
  note: Includes bibliographical references and index.
  subject:
    topic: Social sciences
    topic: Statistical methods
  subject:
    topic: Multivariate analysis
  subject:
    topic: R (Computer program language)
  classification: HA31.35 .F56 2019
  classification: 005.55
  related item:
    title info:
      title: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
  identifier: 9781351062244
  identifier: 1351062247
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  identifier: 9781351062237
  identifier: 1351062239
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  identifier: 9781138480711
  identifier: 9781138480674
  identifier: 9781351062244 Ingram Content Group
  identifier: https://ebookcentral.proquest.com/lib/tamucs/detail.action
  record info:
    record content source: NhCcYBP
    record creation date: 190721
    record change date: 20221130131541.1
    record identifier: in00004071827