Linear Mixed Models: A Practical Guide ... | WHSmith Books
Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition (2nd New edition)

Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition (2nd New edition)

By: Brady T. West (author), Kathleen B. Welch (author), Andrzej T. Galecki (author)Hardback

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Description

Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM. New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulationsUse of the lmer() function in the lme4 R package New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMsUpdated graphical procedures in the software packagesSubstantially revised index to enable more efficient reading and easier location of material on selected topics or software optionsMore practical recommendations on using the software for analysisA new R package (WWGbook) that contains all of the data sets used in the examples Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.

Contents

INTRODUCTIONWhat Are Linear Mixed Models (LMMs)?A Brief History of Linear Mixed Models LINEAR MIXED MODELS: AN OVERVIEWIntroductionSpecification of LMMsThe Marginal Linear ModelEstimation in LMMsComputational IssuesTools for Model SelectionModel-Building StrategiesChecking Model Assumptions (Diagnostics)Other Aspects of LMMsPower Analysis for Linear Mixed ModelsChapter Summary TWO-LEVEL MODELS FOR CLUSTERED DATA: THE RAT PUP EXAMPLEIntroductionThe Rat Pup StudyOverview of the Rat Pup Data AnalysisAnalysis Steps in the Software ProceduresResults of Hypothesis TestsComparing Results across the Software ProceduresInterpreting Parameter Estimates in the Final ModelEstimating the Intraclass Correlation Coefficients (ICCs)Calculating Predicted ValuesDiagnostics for the Final ModelSoftware Notes and Recommendations THREE-LEVEL MODELS FOR CLUSTERED DATA; THE CLASSROOM EXAMPLEIntroductionThe Classroom StudyOverview of the Classroom Data AnalysisAnalysis Steps in the Software ProceduresResults of Hypothesis TestsComparing Results across the Software ProceduresInterpreting Parameter Estimates in the Final ModelEstimating the Intraclass Correlation Coefficients (ICCs)Calculating Predicted ValuesDiagnostics for the Final ModelSoftware NotesRecommendations MODELS FOR REPEATED-MEASURES DATA: THE RAT BRAIN EXAMPLEIntroductionThe Rat Brain StudyOverview of the Rat Brain Data AnalysisAnalysis Steps in the Software ProceduresResults of Hypothesis Tests Comparing Results across the Software ProceduresInterpreting Parameter Estimates in the Final ModelThe Implied Marginal Variance-Covariance Matrix for the Final ModelDiagnostics for the Final ModelSoftware NotesOther Analytic ApproachesRecommendations RANDOM COEFFICIENT MODELS FOR LONGITUDINAL DATA: THE AUTISM EXAMPLEIntroductionThe Autism StudyOverview of the Autism Data AnalysisAnalysis Steps in the Software ProceduresResults of Hypothesis TestsComparing Results across the Software ProceduresInterpreting Parameter Estimates in the Final ModelCalculating Predicted ValuesDiagnostics for the Final ModelSoftware Note: Computational Problems with the D MatrixAn Alternative Approach: Fitting the Marginal Model with an Unstructured Covariance Matrix MODELS FOR CLUSTERED LONGITUDINAL DATA: THE DENTAL VENEER EXAMPLEIntroductionThe Dental Veneer StudyOverview of the Dental Veneer Data AnalysisAnalysis Steps in the Software ProceduresResults of Hypothesis TestsComparing Results across the Software ProceduresInterpreting Parameter Estimates in the Final ModelThe Implied Marginal Variance-Covariance Matrix for the Final ModelDiagnostics for the Final ModelSoftware Notes and RecommendationsOther Analytic Approaches MODELS FOR DATA WITH CROSSED RANDOM FACTORS: THE SAT SCORE EXAMPLE Introduction The SAT Score Study Overview of the SAT Score Data Analysis Analysis Steps in the Software ProceduresResults of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Recommended Diagnostics for the Final Model Software Notes and Additional Recommendations APPENDIX A: STATISTICAL SOFTWARE RESOURCESAPPENDIX B: CALCULATION OF THE MARGINAL VARIANCE-COVARIANCE MATRIXAPPENDIX C: ACRONYMS/ABBREVIATIONS BIBLIOGRAPHY INDEX

Product Details

  • ISBN13: 9781466560994
  • Format: Hardback
  • Number Of Pages: 440
  • ID: 9781466560994
  • weight: 930
  • ISBN10: 1466560991
  • edition: 2nd New edition

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