Longitudinal Data Analysis for the Behavioral Sciences Using R 1st Edition by Jeffrey D. Long – Ebook PDF Instant Download/Delivery: 1483305724 , 978-1483305721
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ISBN 10: 1483305724
ISBN 13: 978-1483305721
Author: Jeffrey D. Long
This book is unique in its focus on showing students in the behavioral sciences how to analyze longitudinal data using R software. The book focuses on application, making it practical and accessible to students in psychology, education, and related fields, who have a basic foundation in statistics. It provides explicit instructions in R computer programming throughout the book, showing students exactly how a specific analysis is carried out and how output is interpreted. “This text excels in the explanation of models with the side-by-side use of R, so the audience can see the models in action. There is a gentle coverage of the mathematics driving the models, which does not seem intimidating to a non technical audience.”—William Anderson, Cornell University
Longitudinal Data Analysis for the Behavioral Sciences Using R 1st Table of contents:
1 Introduction
1.1 Statistical Computing
1.2 Preliminary Issues
1.3 Conceptual Overview of Linear Mixed Effects Regression
1.4 Traditional Approaches
1.5 MPLS Data Set
1.6 Statistical Strategy
1.7 LMER and Multimodel Inference
1.8 Overview of the Remainder of the Book
2 Brief Introduction to R
2.1 Obtaining and Installing R
2.2 Functions and Packages
2.3 Essential Syntax
2.4 Data Types
2.5 Matrices, Data Frames, and Lists
2.6 Indexing
2.7 User-Defined Functions
2.8 Repetitive Operations
2.9 Linear Regression
2.10 Getting Help
2.11 Summary of Functions
3 Data Structures and Longitudinal Analysis
3.1 Longitudinal Data Structures
3.1.1 Wide Format
3.1.2 Long Format
3.2 Reading an External File
3.3 Basic Statistics for Wide-Format Data
3.4 Reshaping Data
3.5 Basic Statistics for Long-Format Data
3.6 Data Structures and Balance on Time
3.7 Missing Data in LMER Analysis
3.8 Missing Data Concepts
3.9 Extensions to More Complex Data Structures*
4 Graphing Longitudinal Data
4.1 Graphing and Statistical Strategy
4.2 Graphing With ggplot2
4.3 Graphing Individual-Level Curves
4.4 Graphing Group-Level Curves
4.5 Conditioning on Static Predictors
4.6 Customizing Graphs*
4.7 Summary of ggplot2 Components
5 Introduction to Linear Mixed Effects Regression
5.1 Traditional Regression and the Linear Model
5.2 Regression Examples
5.3 Linear Mixed Effects Regression
5.4 Estimating the LMER Model
5.5 LMER With Static Predictors
5.6 Additional Details of LMER*
6 Overview of Maximum Likelihood Estimation
6.1 Conceptual Overview
6.2 Maximum Likelihood and LM
6.3 Maximum Likelihood and LMER
6.4 Additional Details of ML for LMER*
7 Multimodel Inference and Akaike’s Information Criterion
7.1 Objects of Inference
7.2 Statistical Strategy
7.3 AIC and Predictive Accuracy
7.4 AICc and Effect Size
7.5 AICc and Multimodel Inference
7.6 Example of Multimodel Analysis
7.7 Example Write-up
7.8 Parametric Bootstrap of the Evidence Ratio*
7.9 Bayesian Information Criterion*
8 Likelihood Ratio Test
8.1 Why Use the Likelihood Ratio Test?
8.2 Fisher and Neyman-Pearson
8.3 Evaluation of Two Nested Models
8.4 Approaches to Testing Multiple Models
8.5 Step-Up Approach
8.6 Top-Down Approach
8.7 Comparison of Approaches
8.8 Parametric Bootstrap*
8.9 Planning a Study*
9 Selecting Time Predictors
9.1 Selection of Time Transformations
9.2 Group-Level Selection of Time Transformations
9.3 Multimodel Inference
9.4 Likelihood Ratio Test
9.5 Cautions Concerning Group-Level Selection
9.6 Subject-Level Selection of Time Transformations
10 Selecting Random Effects
10.1 Automatic Selection of Random Effects
10.2 Random Effects and Variance Components
10.3 Descriptive Methods
10.4 Inferential Methods
10.5 Variance Components and Static Predictors
10.6 Predicted Random Effects
11 Extending Linear Mixed Effects Regression
11.1 Graphing Fitted Curves
11.2 Static Predictors With Multiple Levels
11.3 Interactions Among Static Predictors
11.4 Indexes of Absolute Effect Size in LMER
11.5 Additional Transformations
12 Modeling Nonlinear Change
12.1 Data Set and Analysis Strategy
12.2 Global Versus Local Models
12.3 Polynomials
12.4 Alternatives to Polynomials
12.5 Trigonometric Functions
12.6 Fractional Polynomials
12.7 Spline Models
12.8 Additional Details*
13 Advanced Topics
13.1 Dynamic Predictors
13.2 Multiple Response Variables
13.3 Additional Levels of Nesting
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