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Behavioral Research Data Analysis with R

:	Yuelin Li, Jonathan Baron
:	Behavioral Research Data Analysis with R
:	Springer-Verlag
:	9781461412380
:	1
:	CHF 71.20
:

:	Methoden der empirischen und qualitativen Sozialforschung
:	English

:	245
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

	Behavioral Research Data Analysis with R	3
	Preface	5
	Contents	9
	Chapter 1 Introduction	13
	1.1 An Example R Session	13
	1.2 A Few Useful Concepts and Commands	15
	1.2.1 Concepts	15
	1.2.2 Commands	16
	1.2.2.1 Working Directory	16
	1.2.2.2 Getting Help	17
	1.2.2.3 Installing Packages	18
	1.2.2.4 Assignment, Logic, and Arithmetic	18
	1.2.2.5 Loading and Saving	20
	1.2.2.6 Dealing with Objects	21
	1.3 Data Objects and Data Types	21
	1.3.1 Vectors of Character Strings	22
	1.3.2 Matrices, Lists, and Data Frames	24
	1.3.2.1 Summaries and Calculations by Row, Column, or Group	26
	1.4 Functions and Debugging	27
	Chapter 2 Reading and Transforming Data Format	30
	2.1 Reading and Transforming Data	30
	2.1.1 Data Layout	30
	2.1.2 A Simple Questionnaire Example	30
	2.1.2.1 Extracting Subsets of Data	31
	2.1.2.2 Finding Means (or Other Things) of Sets of Variables	32
	2.1.2.3 One Row Per Observation	32
	2.1.3 Other Ways to Read in Data	36
	2.1.4 Other Ways to Transform Variables	37
	2.1.4.1 Contrasts	37
	2.1.4.2 Averaging Items in a Within-Subject Design	38
	2.1.4.3 Selecting Cases or Variables	39
	2.1.4.4 Recoding and Replacing Data	39
	2.1.4.5 Replacing Characters with Numbers	41
	2.1.5 Using R to Compute Course Grades	41
	2.2 Reshape and Merge Data Frames	42
	2.3 Data Management with a SQL Database	44
	2.4 SQL Database Considerations	46
	Chapter 3 Statistics for Comparing Means and Proportions	49
	3.1 Comparing Means of Continuous Variables	49
	3.2 More on Manual Checking of Data	52
	3.3 Comparing Sample Proportions	53
	3.4 Moderating Effect in loglin()	55
	3.5 Assessing Change of Correlated Proportions	59
	3.5.1 McNemar Test Across Two Samples	60
	Chapter 4 R Graphics and Trellis Plots	65
	4.1 Default Behavior of Basic Commands	65
	4.2 Other Graphics	66
	4.3 Saving Graphics	66
	4.4 Multiple Figures on One Screen	67
	4.5 Other Graphics Tricks	67
	4.6 Examples of Simple Graphs in Publications	68
	4.6.1 http://journal.sjdm.org/8827/jdm8827.pdf	70
	4.6.2 http://journal.sjdm.org/8814/jdm8814.pdf	73
	4.6.3 http://journal.sjdm.org/8801/jdm8801.pdf	74
	4.6.4 http://journal.sjdm.org/8319/jdm8319.pdf	75
	4.6.5 http://journal.sjdm.org/8221/jdm8221.pdf	76
	4.6.6 http://journal.sjdm.org/8210/jdm8210.pdf	78
	4.7 Shaded Areas Under a Curve	79
	4.7.1 Vectors in polygon()	81
	4.8 Lattice Graphics	82
	4.8.0.1 Mathematics Achievement and Socioeconomic Status	82
	Chapter 5 Analysis of Variance: Repeated-Measures	88
	5.1 Example 1: Two Within-Subject Factors	88
	5.1.1 Unbalanced Designs	92
	5.2 Example 2: Maxwell and Delaney	94
	5.3 Example 3: More Than Two Within-Subject Factors	97
	5.4 Example 4: A Simpler Design with Only One Within-Subject Variable	98
	5.5 Example 5: One Between, Two Within	98
	5.6 Other Useful Functions for ANOVA	100
	5.7 Graphics with Error Bars	102
	5.8 Another Way to do Error Bars Using plotCI()	104
	5.8.1 Use Error() for Repeated-Measure ANOVA	105
	5.8.1.1 Basic ANOVA Table with aov()	106
	5.8.1.2 Using Error() Within aov()	107
	5.8.1.3 The Appropriate Error Terms	107
	5.8.1.4 Sources of the Appropriate Error Terms	108
	5.8.1.5 Verify the Calculations Manually	110
	5.8.2 Sphericity	111
	5.8.2.1 Why Is Sphericity Important?	111
	5.9 How to Estimate the Greenhouse–Geisser Epsilon?	112
	5.9.1 Huynh–Feldt Correction	1
	Chapter 6 Linear and Logistic Regression	117
	6.1 Linear Regression	117
	6.2 An Application of Linear Regression on Diamond Pricing	118
	6.2.1 Plotting Data Before Model Fitting	119
	6.2.2 Checking Model Distributional Assumptions	122
	6.2.3 Assessing Model Fit	123
	6.3 Logistic Regression	126
	6.4 Log–Linear Models	127
	6.5 Regression in Vector–Matrix Notation	128
	6.6 Caution on Model Overfit and Classification Errors	130
	Chapter 7 Statistical Power and Sample Size Considerations	136
	7.1 A Simple Example	136
	7.2 Basic Concepts on Statistical Power Estimation	137
	7.3 t-Test with Unequal Sample Sizes	138
	7.4 Binomial Proportions	139
	7.5 Power to Declare a Study Feasible	140
	7.6 Repeated-Measures ANOVA	140
	7.7 Cluster-Randomized Study Design	142
	Chapter 8 Item Response Theory	145
	8.1 Overview	145
	8.2 Rasch Model for Dichotomous Item Responses	145