Li Y., Baron J. Behavioral Research Data Analysis with R

Springer – 2012, 256 pages
ISBN: 1461412374For behavioral research practitioners who are interested in learning R
Provides practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples
Emphasizes practical data analytic skills
This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool. The authors aim primarily to help practitioners of behavioral research make the transition to R. The focus is to provide practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples. The book will also help beginners learn more about statistics and behavioral research. These are statistical techniques used by psychologists who do research on human subjects, but of course they are also relevant to researchers in others fields that do similar kinds of research.
The authors emphasize practical data analytic skills so that readers can quickly incorporated the data in their own research.I Introduction
An Example R Session
A Few Useful Concepts and Commands
Concepts
Commands
Data Objects and Data Types
Vectors of Character Strings
Matrices, Lists, and Data Frames
Functions and DebuggingII Reading and Transforming Data Format
Reading and Transforming Data
Data Layout
A Simple Questionnaire Example
Other Ways to Read in Data
Other Ways to Transform Variables
Using R to Compute Course Grades
Reshape and Merge Data Frames
Data Management with a SQL Database
SQL Database ConsiderationsIII Statistics for Comparing Means and Proportions
Comparing Means of Continuous Variables
More on Manual Checking of Data
Comparing Sample Proportions
Moderating Effect in loglin()
Assessing Change of Correlated Proportions
McNemar Test Across Two SamplesIV R Graphics and Trellis Plots
Default Behavior of Basic Commands
Other Graphics
Saving Graphics
Multiple Figures on One Screen
Other Graphics Tricks
Examples of Simple Graphs in Publications:
http://journal.sjdm.org/8827/jdm8827.pdf
http://journal.sjdm.org/8814/jdm8814.pdf
http://journal.sjdm.org/8801/jdm8801.pdf
http://journal.sjdm.org/8319/jdm8319.pdf
http://journal.sjdm.org/8221/jdm8221.pdf
http://journal.sjdm.org/8210/jdm8210.pdf
Shaded Areas Under a Curve
Vectors in polygon()
Lattice GraphicsV Analysis of Variance: Repeated-Measures
Example : Two Within-Subject Factors
Unbalanced Designs
Example : Maxwell and Delaney
Example : More Than Two Within-Subject Factors
Example : A Simpler Design with Only One
Within-Subject Variable
Example : One Between, Two Within
Other Useful Functions for ANOVA
Graphics with Error Bars
AnotherWay to do Error Bars Using plotCI()
Use Error() for Repeated-Measure ANOVA
Sphericity
How to Estimate the Greenhouse–Geisser Epsilon?
Huynh–Feldt CorrectionVI Linear and Logistic Regression
Linear Regression
An Application of Linear Regression on Diamond Pricing
Plotting Data Before Model Fitting
Checking Model Distributional Assumptions
Assessing Model Fit
Logistic Regression
Log–Linear Models
Regression in Vector–Matrix Notation
Caution on Model Overfit and Classification ErrorsVII Statistical Power and Sample Size Considerations
A Simple Example
Basic Concepts on Statistical Power Estimation
t-Test with Unequal Sample Sizes
Binomial Proportions
Power to Declare a Study Feasible
Repeated-Measures ANOVA
Cluster-Randomized Study DesignVIII Item Response Theory
Overview
Rasch Model for Dichotomous Item Responses
Fitting a rasch() Model
Graphing Item Characteristics and Item Information
Scoring New Item Response Data
Person Fit and Item Fit Statistics
Generalized Partial Credit Model for Polytomous Item
Responses
Neuroticism Data
Category Response Curves and Item Information
Curves
Bayesian Methods for Fitting IRT Models
GPCM
Explanatory IRTIX Imputation ofMissing Data
Missing Data in Smoking Cessation Study
Multiple Imputation with aregImpute()
Imputed Data
Pooling Results Over Imputed Datasets
Multiple Imputation with the mi Package
Multiple Imputation with the Amelia and Zelig Packages
Further ReadingX Linear Mixed-Effects Models in Analyzing Repeated-
Measures Data
The Language-as-Fixed-Effect Fallacy
Recall Scores Example: One Between and One Within Factor
Data Preparations
Data Visualizations
Initial Modeling
Model Interpretation
Alternative Models
Checking Model Fit Visually
Modeling Dependence
Generalized Least Squares Using gls()
Example on Random and Nested Effects
Treatment by Therapist InteractionXI Linear Mixed-Effects Models in Cluster-Randomized Studies
The Television, School, and Family Smoking
Prevention and Cessation Project
Data Import and Preparations
Exploratory Analyses
Testing Intervention Efficacy with Linear Mixed-Effects Models
Model Equation
Multiple-Level Model Equations
Model Equation in Matrix Notations
Intraclass Correlation Coefficients
ICCs from a Mixed-EffectsModel
Statistical Power Considerations for a Group-Randomized Design
Calculate Statistical Power by SimulationA Data Management with a Database:
Create Database and Database Tables
Enter Data
Using RODBC to Import Data from an Access Database
Step 1: Adding an ODBC Data Source Name
Step 2: ODBC Data Source Name Points to the Access File
Step 3: Run RODBC to Import Data