Computing has become an essential part of the day-to-daypractice of statistical work, broadening the types of questionsthat can now be addressed by research scientists applying newlyderived data analytic techniques. Comparing Groups:Randomization and Bootstrap Methods Using R emphasizes thedirect link between scientific research questions and dataanalysis. Rather than relying on mathematical calculations, thisbook focus on conceptual explanations and the use of statisticalcomputing in an effort to guide readers through the integration ofdesign, statistical methodology, and computation to answer specificresearch questions regarding group differences.
Utilizing the widely-used, freely accessible R software, theauthors introduce a modern approach to promote methods that providea more complete understanding of statistical concepts. Following anintroduction to R, each chapter is driven by a research question,and empirical data analysis is used to provide answers to thatquestion. These examples are data-driven inquiries that promoteinteraction between statistical methods and ideas and computerapplication. Computer code and output are interwoven in the book toillustrate exactly how each analysis is carried out and how outputis interpreted. Additional topical coverage includes:
- Data exploration of one variable and multivariate data
- Comparing two groups and many groups
- Permutation tests, randomization tests, and the independentsamples t-Test
- Bootstrap tests and bootstrap intervals
- Interval estimates and effect sizes
Throughout the book, the authors incorporate data fromreal-world research studies as well aschapter problems that providea platform to perform data analyses. A related Web site features acomplete collection of the book's datasets along with theaccompanying codebooks and the R script files and commands,allowing readers to reproduce the presented output and plots.
Comparing Groups: Randomization and Bootstrap Methods UsingR is an excellent book for upper-undergraduate and graduatelevel courses on statistical methods, particularlyin theeducational and behavioral sciences. The book also serves as avaluable resource for researchers who need a practical guide tomodern data analytic and computational methods.
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About the Author
Jeffrey R. Harring, PhD, is Assistant Professor in theDepartment of Measurement, Statistics, and Evaluation at theUniversity of Maryland. Dr. Harring currently focuses his researchon statistical models for repeated measures data and nonlinearstructural equation models.
Jeffrey D. Long, PhD, is Professor of Psychiatry in theCarver College of Medicine at The University of Iowa and HeadStatistician for Neurobiological Predictors of Huntington's Disease(PREDICT-HD), a longitudinal NIH-funded study of early detection ofHuntington's disease. His interests include the analysis oflongitudinal and time-to-event data and ordinal data.
Table of ContentsList of Figures.
List of Tables.
1. An Introduction to R.
1.1 Getting Started.
1.2 Arithmetic: R as a Calculator.
1.3 Computations in R: Functions.
1.4 Connecting Computations.
1.5 Data Structures: Vectors.
1.6 Getting Help.
1.7 Alternative Ways to Run R.
1.8 Extension: Matrices and Matrix Operations.
1.9 Further Reading.
2. Data Representation and Preparation.
2.1 Tabular Data.
2.2 Data Entry.
2.3 Reading Delimited Data into R.
2.4 Data Structure: Data Frames.
2.5 Recording Syntax using Script Files.
2.6 Simple Graphing in R.
2.7 Extension: Logical Expressions and Graphs for CategoricalVariables.
2.8 Further Reading.
3. Data Exploration: One Variable.
3.1 Reading in the Data.
3.2 Non-Parametric Density Estimation.
3.3 Summarizing the Findings.
3.4 Extension: Variability Bands for Kernel Densities.
3.5 Further Reading.
4. Exploration of Multivariate Data: Comparing TwoGroups.
4.1 Graphically Summarizing the Marginal Distribution.
4.2 Graphically Summarizing Conditional Distributions.
4.4 Numerical Summaries of Data: Estimates of the PopulationParameters.
4.4 Summarizing the Findings.
4.5 Extension: Robust Estimation.
4.6 Further Reading.
5. Exploration of Multivariate Data: Comparing ManyGroups.
5.1 Graphing Many Conditional Distributions.
5.2 Numerically Summarizing the Data.
5.3 Summarizing the Findings.
5.4 Examining Distributions Conditional on MultipleVariables.
5.5 Extension: Conditioning on Continuous Variables.
5.6 Further Reading.
6. Randomization & Permutation Tests.
6.1 Randomized Experimental Research.
6.2 An Introduction to the Randomization Test.
6.3 Randomization Tests with Large Samples: Monte CarloSimulation.
6.4 Validity of the Inferences and Conclusions Drawn from aRandomization Test.
6.5 Generalization from the Randomization Results.
6.6 Summarizing the Results for Publication.
6.7 Extension: Test of the Variance.
7. Bootstrap Tests.
7.1 Educational Achievement of Latino Immigrants.
7.2 Probability Models: An Interlude.
7.3 Theoretical Probability Models in R.
7.4 Parametric Bootstrap Tests.
7.5 The Parametric Bootstrap.
7.6 Implementing the Parametric Bootstrap in R.
7.7 Summarizing the Results of the Parametric BootstrapTest.
7.8 Nonparametric Bootstrap Tests.
7.9 Summarizing the Results for the Nonparametric BootstrapTest.
7.10 Bootstrapping Using a Pivot Statistic.
7.11 Independence Assumption for the Bootstrap Methods.
7.12 Extension: Testing Functions.
7.13 Further Reading.
8. Philosophical Considerations.
8.1 The Randomization Test vs. the Bootstrap Test.
8.2 Philosophical Frameworks of Classical Inference.
9. Bootstrap Intervals and Effect Sizes.
9.1 Educational Achievement Among Latino Immigrants: ExampleRevisited.
9.2 Plausible Models to Reproduce the Observed Result.
9.3 Bootstrapping Using an Alternative Model.
9.4 Interpretation of the Interval Estimate.
9.5 Adjusted Bootstrap Intervals.
9.6 Standardized Effect Size: Quantifying the Group Differencesin a Common Metric.
9.7 Summarizing the Results.
9.8 Extension: Bootstrapping the Confidence Envelope for a Q-QPlot.
9.9 Confidence Envelopes.
9.10 Further Reading.
10. Dependent Samples.
10.1 Matching: Reducing the Likelihood of Non-EquivalentGroups.
10.2 Mathematics Achievement Study Design.
10.3 Randomization/Permutation Test for Dependent Samples.
10.4 Effect Size.
10.5 Summarizing the Results of a Dependent Samples Test forPublication.
10.6 To Match or Not to Match: That is the Question.
10.7 Extension: Block Bootstrap.
10.8 Further Reading.
11. Planned Contrasts.
11.1 Planned Comparisons.
11.2 Examination of Weight Loss Conditioned on Diet.
11.3 From Research Questions to Hypotheses.
11.4 Statistical Contrasts.
11.5 Computing the Estimated Contrasts Using the ObservedData.
11.6 Testing Contrasts: Randomization Test.
11.7 Strength of Association: A Measure of Effect.
11.8 Contrast Sum of Squares.
11.9 Eta-Squared for Contrasts.
11.10 Bootstrap Interval for Eta-Squared.
11.11 Summarizing the Results of a Planned Contrast TestAnalysis.
11.12 Extension: Orthogonal Contrasts.
11.13 Further Reading.
12. Unplanned Contrasts.
12.1 Unplanned Comparisons.
12.2 Examination of Weight Loss Conditioned on Diet.
12.3 Omnibus Test.
12.4 Group Comparisons After the Omnibus Test.
12.5 Ensemble-Adjusted p-values.
12.6 Strengths and Limitations of the Four Approaches.
12.7 Summarizing the Results of Unplanned Contrast Tests forPublication.
12.8 Extension: Plots of the Unplanned Contrasts.
12.9 Further Reading.
What People are Saying About This
“The three authors of this book have a deep understanding of research methods and statistics and provide great value in this book for students of this subject and readers interested in it.” (Biz India, 8 May 2012)