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Behavioral Research Data Analysis with R
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Behavioral Research Data Analysis with R
von: Yuelin Li, Jonathan Baron
Springer-Verlag, 2011
ISBN: 9781461412380
245 Seiten, Download: 2431 KB
 
Format:  PDF
geeignet für: Apple iPad, Android Tablet PC's Online-Lesen PC, MAC, Laptop

Typ: B (paralleler Zugriff)

 

 
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Inhaltsverzeichnis

  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  
           8.2.1 Fitting a rasch() Model 146  
           8.2.2 Graphing Item Characteristics and Item Information 149  
           8.2.3 Scoring New Item Response Data 151  
           8.2.4 Person Fit and Item Fit Statistics 151  
        8.3 Generalized Partial Credit Model for Polytomous ItemResponses 152  
           8.3.1 Neuroticism Data 153  
           8.3.2 Category Response Curves and Item InformationCurves 153  
        8.4 Bayesian Methods for Fitting IRT Models 155  
           8.4.1 GPCM 155  
           8.4.2 Explanatory IRT 158  
     Chapter 9 Imputation of Missing Data 166  
        9.1 Missing Data in Smoking Cessation Study 166  
        9.2 Multiple Imputation with aregImpute() 168  
           9.2.1 Imputed Data 170  
           9.2.2 Pooling Results Over Imputed Datasets 171  
        9.3 Multiple Imputation with the mi Package 173  
        9.4 Multiple Imputation with the Amelia and Zelig Packages 176  
        9.5 Further Reading 178  
     Chapter 10 Linear Mixed-Effects Models in Analyzing Repeated-Measures Data 181  
        10.1 The "Language-as-Fixed-Effect Fallacy' 181  
        10.2 Recall Scores Example: One Between and One Within Factor 184  
           10.2.1 Data Preparations 184  
           10.2.2 Data Visualizations 185  
           10.2.3 Initial Modeling 186  
           10.2.4 Model Interpretation 186  
              10.2.4.1 Fixed Effects 186  
              10.2.4.2 Random Effects 189  
           10.2.5 Alternative Models 190  
           10.2.6 Checking Model Fit Visually 193  
           10.2.7 Modeling Dependence 194  
        10.3 Generalized Least Squares Using gls() 199  
        10.4 Example on Random and Nested Effects 202  
           10.4.1 Treatment by Therapist Interaction 204  
     Chapter 11 Linear Mixed-Effects Models in Cluster-Randomized Studies 209  
        11.1 The Television, School, and Family Smoking Prevention and Cessation Project 209  
        11.2 Data Import and Preparations 210  
           11.2.1 Exploratory Analyses 211  
        11.3 Testing Intervention Efficacy with Linear Mixed-Effects Models 214  
        11.4 Model Equation 217  
        11.5 Multiple-Level Model Equations 219  
        11.6 Model Equation in Matrix Notations 220  
        11.7 Intraclass Correlation Coefficients 224  
        11.8 ICCs from a Mixed-Effects Model 225  
        11.9 Statistical Power Considerationsfor a Group-Randomized Design 227  
           11.9.1 Calculate Statistical Power by Simulation 227  
     Appendix A Data Management with a Database 232  
        A.1 Create Database and Database Tables 232  
        A.2 Enter Data 233  
        A.3 Using RODBC to Import Data from an ACCESS Database 235  
           A.3.1 Step 1: Adding an ODBC Data Source Name 236  
           A.3.2 Step 2: ODBC Data Source Name Points to the ACCESS File 236  
           A.3.3 Step 3: Run RODBC to Import Data 237  
     References 239  
     Index 244  


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