In DATA ANALYSIS BY RESAMPLING, Clifford Lunneborg argues that modern computing power has rendered the model-driven and assumption-plagued data analyses of the past unnecessary, obsolete, and inappropriate. This book introduces readers to modern, design-driven analyses that depend only on the observed data, on knowledge of how the data were collected, and on questions the data were intended to answer. Overall, Lunneborg provides a modern and timely approach to statistical inference.
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C.E. Lunneborg is Professor Emeritus of Psychology and Statistics at the University of Washington. During a career spanning 40 years he has published over 100 technical articles and three university-level texts. His current research interests are in resampling, experimental design, and web-based instruction.Review:
PREFACE: DATA ANALYSIS BY RESAMPLING PART I: RESAMPLING CONCEPTS INTRODUCTION CONCEPTS 1: TERMS AND NOTATION Case, Attributes, Scores, and Treatments / Experimental and Observational Studies / Data Sets, Samples, and Populations / Parameters, Statistics, and Distributions / Distribution Functions APPLICATIONS 1: CASES, ATTRIBUTES, AND DISTRIBUTIONS Attributes, Scores, Groups, and Treatments / Distributions of Scores and Statistics / Exercises CONCEPTS 2: POPULATIONS AND RANDOM SAMPLES Varieties of Populations / Random Samples APPLICATIONS 2: RANDOM SAMPLING Simple Random Samples / Exercises CONCEPTS 3: STATISTICS AND SAMPLING DISTRIBUTIONS Statistics and Estimators / Accuracy of Estimation / The Sampling Distribution / Bias of an Estimator / Standard Error of a Statistic / RMS Error of an Estimator / Confidence Interval APPLICATIONS 3: SAMPLING DISTRIBUTION COMPUTATIONS Exercises CONCEPTS 4: TESTING POPULATION HYPOTHESES Population Statistical Hypotheses / Population Hypothesis Testing APPLICATIONS 4: NULL SAMPLING DISTRIBUTION P-VALUES The p-value of a Directional Test / The p-value of a Nondirectional Test / Exercises CONCEPTS 5: PARAMETRICS, PIVOTALS, AND ASYMPTOTICS The Unrealizable Sampling Distribution / Sampling Distribution of a Sample Mean / Parametric Population Distributions / Pivotal Form Statistics / Asymptotic Sampling Distributions / Limitations of the Mathematical Approach APPLICATIONS 5: CIs FOR NORMAL POPULATION MEAN AND VARIANCE CI for a Normal Population Mean / CI for a Normal Population Variance / Nonparametric CI Estimation / Exercises CONCEPTS 6: LIMITATIONS OF PARAMETRIC INFERENCE Range and Precision of Scores / Size of Population / Size of Sample / Roughness of Population Distribution / Parameters and Statistics of Interests / Scarcity of Random Samples / Resampling Inference APPLICATIONS 6: RESAMPLING APPROACHES TO INFERENCE Exercises CONCEPTS 7: THE REAL AND BOOTSTRAP WORLDS The Real World of Population Inference / The Bootstrap World of Population Inference / Real World Population Distribution Estimates / Nonparametric Population Estimates / Sample Size and Distribution Estimates APPLICATIONS 7: BOOTSTRAP POPULATION DISTRIBUTIONS Nonparametric Population Estimates / Exercises CONCEPTS 8: THE BOOTSTRAP SAMPLING DISTRIBUTION The Bootstrap Conjecture / Complete Bootstrap Sampling Distributions / Monte Carlo Bootstrap Estimate of Standard Error / The Bootstrap Estimate of Bias / Simple Bootstrap CI Estimates APPLICATIONS 8: BOOTSTRAP SE, BIAS, AND CI ESTIMATES Example / Exercises CONCEPTS 9: BETTER BOOTSTRAP CIs: THE BOOTSTRAP-T Pivotal Form Statistics / The Bootstrap-t Pivotal Transformation / Forming Bootstrap-t CIs / Estimating the Standard Error of an Estimate / Range of Applications of the Bootstrap-t / Iterated Bootstrap CIs APPLICATIONS 9: SE AND CIs FOR TRIMMED MEANS Definition of the Trimmed Mean / Importance of the Trimmed Mean / A Note on Outliers / Determining the Trimming Fraction / Sampling Distribution of the Trimmed Mean / Applications / Exercises CONCEPTS 10: BETTER BOOTSTRAP CIs: BCA INTERVALS Bias Corrected and Accelerated CI Estimates / Applications of BCA CI / Better Confidence Interval Estimates APPLICATIONS 10: USING CI CORRECTION FACTORS Requirements for a BCA CI / Implementations of the BCA Algorithm / Exercise CONCEPTS 11: BOOTSTRAP HYPOTHESIS TESTING CIs, Null Hypothesis Tests, and p-values / Bootstrap-t Hypothesis Testing / Bootstrap Hypothesis Testing Alternatives / CI Hypothesis Testing / Confidence Intervals or p-values? APPLICATIONS 11: BOOTSTRAP P-VALUES Computing a Bootstrap-t p-value / Fixed-alpha CIs and Hypothesis Testing / Computing a BCI CI p-Value / Exercise CONCEPTS 12: RANDOMIZED TREATMENT ASSIGNMENT Two Functions of Randomization / Randomization of Sampled Cases / Randomization of Two Available Cases / Statistical Basis for Local Casual Inference / Population Hypothesis Revisited APPLICATIONS 12: MONTE CARLO REFERENCE DISTRIBUTIONS Serum Albumen in Diabetic Mice / Resampling Stats Analysis / SC Analysis / S-Plus Analysis / Exercises CONCEPTS 13: STRATEGIES FOR RANDOMIZING CASES Independent Randomization of Cases / Completely Randomized Designs / Randomized Blocks Designs / Restricted Randomization / Constraints on Rerandomization APPLICATIONS 13: IMPLEMENTING CASE RERANDOMIZATION Completely Randomized Designs / Randomized Blocks Designs / Independent Randomization of Cases / Restricted Randomization / Exercises CONCEPTS 14: RANDOM TREATMENT SEQUENCES Between- and Within-Cases Designs / Randomizing the Sequence of Treatments / Casual Inference for Within-Cases Designs / Sequence of Randomization Strategies APPLICATIONS 14: RERANDOMIZING TREATMENT SEQUENCES Analysis of the AB-BA Design / Sequences of k > 2 Treatments / Exercises CONCEPTS 15: BETWEEN- AND WITHIN-CASE DECISIONS Between/Within Designs / Between/Within Resampling Strategies / Doubly Randomized Available Cases APPLICATIONS 15: INTERACTIONS AND SIMPLE EFFECTS Simple and Main Effects / Exercises CONCEPTS 16: SUBSAMPLES: STABILITY OF DESCRIPTION Nonrandom Studies and Data Sets / Local Descriptive Inference / Descriptive Stability and Case Homogeneity / Subsample Descriptions / Employing Subsample Descriptions / Subsamples and Randomized Studies APPLICATIONS 16: STRUCTURED & UNSTRUCTURED DATA Half-Samples of Unstructured Data / Subsamples of Source-Structured Cases / Exercises PART II: RESAMPLING APPLICATIONS INTRODUCTION APPLICATIONS 17: A SINGLE GROUP OF CASES Random Sample or Set of Available Cases / Typical Size of Score Distribution / Variability of Attribute Scores / Association Between Two Attributes / Exercises APPLICATIONS 18: TWO INDEPENDENT GROUPS OF CASES Constitution of Independent Groups / Location Comparisons for Samples / Magnitude Differences, CR and RB Designs / Magnitude Differences, Nonrandom Designs / Study Size / Exercises APPLICATIONS 19: MULTIPLE INDEPENDENT GROUPS Multiple Group Parametric Comparisons / Nonparametric K-group Comparison / Comparisons among Randomized Groups / Comparisons among Nonrandom Groups / Adjustment for Multiple Comparisons / Exercises APPLICATIONS 20: MULTIPLE FACTORS AND COVARIATES Two Treatment Factors / Treatment and Blocking Factors / Covariate Adjustment of Treatment Scores / Exercises APPLICATIONS 21: WITHIN-CASES TREATMENT COMPARISONS Normal Models, Univariate and Multivariate / Bootstrap Treatment Comparisons / Randomized Sequence of Treatments / Nonrandom Repeated Measures / Exercises APPLICATIONS 22: LINEAR MODELS: MEASURED RESPONSE The Parametric Linear Model / Nonparametric Linear Models / Prediction Accuracy / Linear Models for Randomized Cases / Linear Models for Nonrandom Studies / Exercises APPLICATIONS 23: CATEGORICAL RESPONSE ATTRIBUTES Cross-Classification of Cases / The 2 x 2 Table / Logistic Regression / Exercises POSTSCRIPT: GENERALITY, CAUSALITY & STABILITY Study Design and Resampling / Resampling Tools / REFERENCES / INDEX
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