Principles & methods of statistical analysis
Statistics
SAGE Publications, Inc.
2018
EISBN 1071878905
COVER.
TITLE PAGE.
COPYRIGHT PAGE.
BRIEF CONTENTS.
DETAILED CONTENTS.
PREFACE.
ABOUT THE AUTHORS.
PROLOGUE.
PART I: GETTING STARTED.
CHAPTER 1 - THE BIG PICTURE.
CHAPTER 2 - EXAMINING OUR DATA: AN INTRODUCTION TO SOME OF THE TECHNIQUES OF EXPLORATORY DATA ANALYSIS.
PART II: THE BEHAVIOR OF DATA.
CHAPTER 3 - PROPERTIES OF DISTRIBUTIONS: THE BUILDING BLOCKS OF STATISTICAL INFERENCE.
PART III: THE BASICS OF STATISTICAL INFERENCE.
CHAPTER 4 - ESTIMATING PARAMETERS OF POPULATIONS FROM SAMPLE DATA.
CHAPTER 5 - RESISTANT ESTIMATORS OF PARAMETERS.
CHAPTER 6 - GENERAL PRINCIPLES OF HYPOTHESIS TESTING.
PART IV: SPECIFIC TECHNIQUES TO ANSWER SPECIFIC QUESTIONS.
CHAPTER 7 - THE INDEPENDENT GROUPS t-TESTS FOR TESTING FOR DIFFERENCES BETWEEN POPULATION MEANS.
CHAPTER 8 - TESTING HYPOTHESES WHEN THE DEPENDENT VARIABLE CONSISTS OF FREQUENCIES OF SCORES IN VARIOUS CATEGORIES.
CHAPTER 9 - THE RANDOMIZATION/PERMUTATION MODEL: AN ALTERNATIVE TO THE CLASSICAL STATISTICAL MODEL FOR TESTING HYPOTHESES ABOUT TREATMENT EFFECTS.
CHAPTER 10 - EXPLORING THE RELATIONSHIP BETWEEN TWO VARIABLES: CORRELATION.
CHAPTER 11 - EXPLORING THE RELATIONSHIP BETWEEN TWO VARIABLES: THE LINEAR REGRESSION MODEL.
CHAPTER 12 - A CLOSER LOOK AT LINEAR REGRESSION.
CHAPTER 13 - ANOTHER WAY TO SCALE THE SIZE OF TREATMENT EFFECTS.
CHAPTER 14 - ANALYSIS OF VARIANCE FOR TESTING FOR DIFFERENCES BETWEEN POPULATION MEANS.
CHAPTER 15 - MULTIPLE REGRESSION AND BEYOND.
EPILOGUE.
APPENDIX A: SOME USEFUL RULES OF ALGEBRA.
APPENDIX B: RULES OF SUMMATION.
APPENDIX C: LOGARITHMS.
APPENDIX D: THE INVERSE OF THE CUMULATIVE NORMAL DISTRIBUTION.
APPENDIX E: THE UNIT NORMAL DISTRIBUTION.
APPENDIX F: THE t-DISTRIBUTION.
APPENDIX G: THE FISHER r TO zr TRANSFORMATION.
APPENDIX H: CRITICAL VALUES FOR F WITH ALPHA = .05.
APPENDIX I: THE CHI SQUARE DISTRIBUTION.
REFERENCES.
INDEX.
Key Features: Coverage of traditional concepts in statistics includes expected value operators, likelihood functions, maximum likelihood estimation, and least squares estimation, preparing students for concepts they will continue to encounter in more advanced material. Real research on specific antisocial behaviors provides consistent context for answering research questions in an interesting and intuitive way. Discussion of statistical inference in an easy-to-understand manner ensures that students have the foundation they need to avoid misusing hypothesis tests. A detailed presentation of resampling methods and randomization tests for experiments and correlation provides a better way to analyze data when the assumptions of the classical tests are not met. A number of current techniques for data analysis not included in other textbooks are introduced, including quantile plots, quantile-quantile plots, normal quantile plots, analysis of residuals in scatter plots, bootstrap methods, robust estimators, robust regression, and the use of randomization (permutation) tests for experiments and correlation.
TITLE PAGE.
COPYRIGHT PAGE.
BRIEF CONTENTS.
DETAILED CONTENTS.
PREFACE.
ABOUT THE AUTHORS.
PROLOGUE.
PART I: GETTING STARTED.
CHAPTER 1 - THE BIG PICTURE.
CHAPTER 2 - EXAMINING OUR DATA: AN INTRODUCTION TO SOME OF THE TECHNIQUES OF EXPLORATORY DATA ANALYSIS.
PART II: THE BEHAVIOR OF DATA.
CHAPTER 3 - PROPERTIES OF DISTRIBUTIONS: THE BUILDING BLOCKS OF STATISTICAL INFERENCE.
PART III: THE BASICS OF STATISTICAL INFERENCE.
CHAPTER 4 - ESTIMATING PARAMETERS OF POPULATIONS FROM SAMPLE DATA.
CHAPTER 5 - RESISTANT ESTIMATORS OF PARAMETERS.
CHAPTER 6 - GENERAL PRINCIPLES OF HYPOTHESIS TESTING.
PART IV: SPECIFIC TECHNIQUES TO ANSWER SPECIFIC QUESTIONS.
CHAPTER 7 - THE INDEPENDENT GROUPS t-TESTS FOR TESTING FOR DIFFERENCES BETWEEN POPULATION MEANS.
CHAPTER 8 - TESTING HYPOTHESES WHEN THE DEPENDENT VARIABLE CONSISTS OF FREQUENCIES OF SCORES IN VARIOUS CATEGORIES.
CHAPTER 9 - THE RANDOMIZATION/PERMUTATION MODEL: AN ALTERNATIVE TO THE CLASSICAL STATISTICAL MODEL FOR TESTING HYPOTHESES ABOUT TREATMENT EFFECTS.
CHAPTER 10 - EXPLORING THE RELATIONSHIP BETWEEN TWO VARIABLES: CORRELATION.
CHAPTER 11 - EXPLORING THE RELATIONSHIP BETWEEN TWO VARIABLES: THE LINEAR REGRESSION MODEL.
CHAPTER 12 - A CLOSER LOOK AT LINEAR REGRESSION.
CHAPTER 13 - ANOTHER WAY TO SCALE THE SIZE OF TREATMENT EFFECTS.
CHAPTER 14 - ANALYSIS OF VARIANCE FOR TESTING FOR DIFFERENCES BETWEEN POPULATION MEANS.
CHAPTER 15 - MULTIPLE REGRESSION AND BEYOND.
EPILOGUE.
APPENDIX A: SOME USEFUL RULES OF ALGEBRA.
APPENDIX B: RULES OF SUMMATION.
APPENDIX C: LOGARITHMS.
APPENDIX D: THE INVERSE OF THE CUMULATIVE NORMAL DISTRIBUTION.
APPENDIX E: THE UNIT NORMAL DISTRIBUTION.
APPENDIX F: THE t-DISTRIBUTION.
APPENDIX G: THE FISHER r TO zr TRANSFORMATION.
APPENDIX H: CRITICAL VALUES FOR F WITH ALPHA = .05.
APPENDIX I: THE CHI SQUARE DISTRIBUTION.
REFERENCES.
INDEX.
Key Features: Coverage of traditional concepts in statistics includes expected value operators, likelihood functions, maximum likelihood estimation, and least squares estimation, preparing students for concepts they will continue to encounter in more advanced material. Real research on specific antisocial behaviors provides consistent context for answering research questions in an interesting and intuitive way. Discussion of statistical inference in an easy-to-understand manner ensures that students have the foundation they need to avoid misusing hypothesis tests. A detailed presentation of resampling methods and randomization tests for experiments and correlation provides a better way to analyze data when the assumptions of the classical tests are not met. A number of current techniques for data analysis not included in other textbooks are introduced, including quantile plots, quantile-quantile plots, normal quantile plots, analysis of residuals in scatter plots, bootstrap methods, robust estimators, robust regression, and the use of randomization (permutation) tests for experiments and correlation.
