Li / Baron Behavioral Research Data Analysis with R

2012
ISBN: 978-1-4614-1238-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 245 Seiten

Reihe: Use R!

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 they can be quickly incorporated into readers' own research.

Yuelin Li is a research psychologist and a behavioral statistician.  His appointment at Memorial Sloan-Kettering Cancer Center allows him to apply a range of statistical techniques in understanding complex human behaviors---social network influence of young adult smoking, genetic-environment interaction in cognitive impairment, health behavior change, psychosocial and quality of life outcomes in  cancer treatment, survivorship, and end of life care.Jonathan Baron is Professor of Psychology at the University of Pennsylvania, where he teaches Judgments and Decision and does research people's judgments and decisions about public policies.  He has been fascinated by the promise of computers since about 1960 and has come of age with them and used them in his research.  In 2000, he began the Web site (http://finzi.psych.upenn.edu) and document that led to this book, which was then mostly about data layout, until Yuelin Li (who shared the same PhD advisor, David Krantz) volunteered to help with the more substantive parts.  Baron is founding and current editor of the journal Judgment and Decision Making.

Autoren/Hrsg.

Li, Yuelin

Baron, Jonathan

Inhaltsverzeichnis

1;Behavioral Research Data Analysis with R;3
1.1;Preface;5
1.2;Contents;9
1.3;Chapter 1 Introduction;13
1.3.1;1.1 An Example R Session;13
1.3.2;1.2 A Few Useful Concepts and Commands;15
1.3.2.1;1.2.1 Concepts;15
1.3.2.2;1.2.2 Commands;16
1.3.2.2.1;1.2.2.1 Working Directory;16
1.3.2.2.2;1.2.2.2 Getting Help;17
1.3.2.2.3;1.2.2.3 Installing Packages;18
1.3.2.2.4;1.2.2.4 Assignment, Logic, and Arithmetic;18
1.3.2.2.5;1.2.2.5 Loading and Saving;20
1.3.2.2.6;1.2.2.6 Dealing with Objects;21
1.3.3;1.3 Data Objects and Data Types;21
1.3.3.1;1.3.1 Vectors of Character Strings;22
1.3.3.2;1.3.2 Matrices, Lists, and Data Frames;24
1.3.3.2.1;1.3.2.1 Summaries and Calculations by Row, Column, or Group;26
1.3.4;1.4 Functions and Debugging;27
1.4;Chapter 2 Reading and Transforming Data Format ;30
1.4.1;2.1 Reading and Transforming Data;30
1.4.1.1;2.1.1 Data Layout;30
1.4.1.2;2.1.2 A Simple Questionnaire Example;30
1.4.1.2.1;2.1.2.1 Extracting Subsets of Data;31
1.4.1.2.2;2.1.2.2 Finding Means (or Other Things) of Sets of Variables;32
1.4.1.2.3;2.1.2.3 One Row Per Observation;32
1.4.1.3;2.1.3 Other Ways to Read in Data;36
1.4.1.4;2.1.4 Other Ways to Transform Variables;37
1.4.1.4.1;2.1.4.1 Contrasts;37
1.4.1.4.2;2.1.4.2 Averaging Items in a Within-Subject Design;38
1.4.1.4.3;2.1.4.3 Selecting Cases or Variables;39
1.4.1.4.4;2.1.4.4 Recoding and Replacing Data;39
1.4.1.4.5;2.1.4.5 Replacing Characters with Numbers;41
1.4.1.5;2.1.5 Using R to Compute Course Grades;41
1.4.2;2.2 Reshape and Merge Data Frames;42
1.4.3;2.3 Data Management with a SQL Database;44
1.4.4;2.4 SQL Database Considerations;46
1.5;Chapter 3 Statistics for Comparing Means and Proportions;49
1.5.1;3.1 Comparing Means of Continuous Variables;49
1.5.2;3.2 More on Manual Checking of Data;52
1.5.3;3.3 Comparing Sample Proportions;53
1.5.4;3.4 Moderating Effect in loglin();55
1.5.5;3.5 Assessing Change of Correlated Proportions;59
1.5.5.1;3.5.1 McNemar Test Across Two Samples;60
1.6;Chapter 4 R Graphics and Trellis Plots;65
1.6.1;4.1 Default Behavior of Basic Commands;65
1.6.2;4.2 Other Graphics;66
1.6.3;4.3 Saving Graphics;66
1.6.4;4.4 Multiple Figures on One Screen;67
1.6.5;4.5 Other Graphics Tricks;67
1.6.6;4.6 Examples of Simple Graphs in Publications;68
1.6.6.1;4.6.1 http://journal.sjdm.org/8827/jdm8827.pdf;70
1.6.6.2;4.6.2 http://journal.sjdm.org/8814/jdm8814.pdf;73
1.6.6.3;4.6.3 http://journal.sjdm.org/8801/jdm8801.pdf;74
1.6.6.4;4.6.4 http://journal.sjdm.org/8319/jdm8319.pdf;75
1.6.6.5;4.6.5 http://journal.sjdm.org/8221/jdm8221.pdf;76
1.6.6.6;4.6.6 http://journal.sjdm.org/8210/jdm8210.pdf;78
1.6.7;4.7 Shaded Areas Under a Curve;79
1.6.7.1;4.7.1 Vectors in polygon();81
1.6.8;4.8 Lattice Graphics;82
1.6.8.1;4.8.0.1 Mathematics Achievement and Socioeconomic Status;82
1.7;Chapter 5 Analysis of Variance: Repeated-Measures;88
1.7.1;5.1 Example 1: Two Within-Subject Factors ;88
1.7.1.1;5.1.1 Unbalanced Designs;92
1.7.2;5.2 Example 2: Maxwell and Delaney;94
1.7.3;5.3 Example 3: More Than Two Within-Subject Factors;97
1.7.4;5.4 Example 4: A Simpler Design with Only One Within-Subject Variable;98
1.7.5;5.5 Example 5: One Between, Two Within;98
1.7.6;5.6 Other Useful Functions for ANOVA;100
1.7.7;5.7 Graphics with Error Bars;102
1.7.8;5.8 Another Way to do Error Bars Using plotCI();104
1.7.8.1;5.8.1 Use Error() for Repeated-Measure ANOVA;105
1.7.8.1.1;5.8.1.1 Basic ANOVA Table with aov();106
1.7.8.1.2;5.8.1.2 Using Error() Within aov();107
1.7.8.1.3;5.8.1.3 The Appropriate Error Terms;107
1.7.8.1.4;5.8.1.4 Sources of the Appropriate Error Terms;108
1.7.8.1.5;5.8.1.5 Verify the Calculations Manually;110
1.7.8.2;5.8.2 Sphericity;111
1.7.8.2.1;5.8.2.1 Why Is Sphericity Important?;111
1.7.9;5.9 How to Estimate the Greenhouse–Geisser Epsilon?;112
1.7.9.1;5.9.1 Huynh–Feldt Correction;1
1.8;Chapter 6 Linear and Logistic Regression;117
1.8.1;6.1 Linear Regression;117
1.8.2;6.2 An Application of Linear Regression on Diamond Pricing;118
1.8.2.1;6.2.1 Plotting Data Before Model Fitting;119
1.8.2.2;6.2.2 Checking Model Distributional Assumptions;122
1.8.2.3;6.2.3 Assessing Model Fit;123
1.8.3;6.3 Logistic Regression;126
1.8.4;6.4 Log–Linear Models;127
1.8.5;6.5 Regression in Vector–Matrix Notation;128
1.8.6;6.6 Caution on Model Overfit and Classification Errors;130
1.9;Chapter 7 Statistical Power and Sample Size Considerations;136
1.9.1;7.1 A Simple Example;136
1.9.2;7.2 Basic Concepts on Statistical Power Estimation;137
1.9.3;7.3 t-Test with Unequal Sample Sizes;138
1.9.4;7.4 Binomial Proportions;139
1.9.5;7.5 Power to Declare a Study Feasible;140
1.9.6;7.6 Repeated-Measures ANOVA;140
1.9.7;7.7 Cluster-Randomized Study Design;142
1.10;Chapter 8 Item Response Theory ;145
1.10.1;8.1 Overview;145
1.10.2;8.2 Rasch Model for Dichotomous Item Responses;145
1.10.2.1;8.2.1 Fitting a rasch() Model;146
1.10.2.2;8.2.2 Graphing Item Characteristics and Item Information;149
1.10.2.3;8.2.3 Scoring New Item Response Data;151
1.10.2.4;8.2.4 Person Fit and Item Fit Statistics;151
1.10.3;8.3 Generalized Partial Credit Model for Polytomous ItemResponses;152
1.10.3.1;8.3.1 Neuroticism Data;153
1.10.3.2;8.3.2 Category Response Curves and Item InformationCurves;153
1.10.4;8.4 Bayesian Methods for Fitting IRT Models;155
1.10.4.1;8.4.1 GPCM;155
1.10.4.2;8.4.2 Explanatory IRT;158
1.11;Chapter 9 Imputation of Missing Data;166
1.11.1;9.1 Missing Data in Smoking Cessation Study;166
1.11.2;9.2 Multiple Imputation with aregImpute();168
1.11.2.1;9.2.1 Imputed Data;170
1.11.2.2;9.2.2 Pooling Results Over Imputed Datasets;171
1.11.3;9.3 Multiple Imputation with the mi Package;173
1.11.4;9.4 Multiple Imputation with the Amelia and Zelig Packages;176
1.11.5;9.5 Further Reading;178
1.12;Chapter 10 Linear Mixed-Effects Models in Analyzing Repeated-Measures Data ;181
1.12.1;10.1 The "Language-as-Fixed-Effect Fallacy';181
1.12.2;10.2 Recall Scores Example: One Between and One Within Factor ;184
1.12.2.1;10.2.1 Data Preparations;184
1.12.2.2;10.2.2 Data Visualizations;185
1.12.2.3;10.2.3 Initial Modeling;186
1.12.2.4;10.2.4 Model Interpretation;186
1.12.2.4.1;10.2.4.1 Fixed Effects;186
1.12.2.4.2;10.2.4.2 Random Effects;189
1.12.2.5;10.2.5 Alternative Models;190
1.12.2.6;10.2.6 Checking Model Fit Visually;193
1.12.2.7;10.2.7 Modeling Dependence;194
1.12.3;10.3 Generalized Least Squares Using gls();199
1.12.4;10.4 Example on Random and Nested Effects;202
1.12.4.1;10.4.1 Treatment by Therapist Interaction;204
1.13;Chapter 11 Linear Mixed-Effects Models in Cluster-Randomized Studies ;209
1.13.1;11.1 The Television, School, and Family Smoking Prevention and Cessation Project;209
1.13.2;11.2 Data Import and Preparations;210
1.13.2.1;11.2.1 Exploratory Analyses;211
1.13.3;11.3 Testing Intervention Efficacy with Linear Mixed-Effects Models ;214
1.13.4;11.4 Model Equation;217
1.13.5;11.5 Multiple-Level Model Equations;219
1.13.6;11.6 Model Equation in Matrix Notations;220
1.13.7;11.7 Intraclass Correlation Coefficients;224
1.13.8;11.8 ICCs from a Mixed-Effects Model;225
1.13.9;11.9 Statistical Power Considerationsfor a Group-Randomized Design;227
1.13.9.1;11.9.1 Calculate Statistical Power by Simulation;227
1.14;Appendix A Data Management with a Database;232
1.14.1;A.1 Create Database and Database Tables;232
1.14.2;A.2 Enter Data;233
1.14.3;A.3 Using RODBC to Import Data from an ACCESS Database;235
1.14.3.1;A.3.1 Step 1: Adding an ODBC Data Source Name;236
1.14.3.2;A.3.2 Step 2: ODBC Data Source Name Points to the ACCESS File;236
1.14.3.3;A.3.3 Step 3: Run RODBC to Import Data;237
1.15;References;239
1.16;Index;244