Synopses & Reviews
Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts.
Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book.
- Learn basic concepts of measurement and probability theory, data management, and research design
- Discover basic statistical procedures, including correlation, the t-test, the chi-square and Fishers exact tests, and techniques for analyzing nonparametric data
- Learn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regression
- Use and interpret statistics for business and quality improvement, medical and public health, and education and psychology
- Communicate with statistics and critique statistical information presented by others
About the Author
Sarah Boslaugh holds a PhD in Research and Evaluation from the City University of New York and have been working as a statistical analyst for 15 years, in a variety of professional settings, including the New York City Board of Education, the Institutional Research Office of the City University of New York, Montefiore Medical Center, the Virginia Department of Social Services, Magellan Health Services, Washington University School of Medicine, and BJC HealthCare. She has taught statistics in several different contexts and currently teaches Intermediate Statistics at Washington University Medical School. She has published two previous books: An Intermediate Guide to SPSS Programming: Using Syntax for Data Management (SAGE Publications, 2004) and Secondary Data Sources for Public Health (forthcoming from Cambridge U. Press, 2007) and am currently editing the Encyclopedia of Epidemiology for SAGE Publications (forthcoming, 2007).
Table of Contents
Preface; OK, Just What Is Statistics?; The Focus of This Book; Statistics in the Age of Information; Organization of This Book; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Chapter 1: Basic Concepts of Measurement; 1.1 Measurement; 1.2 Levels of Measurement; 1.3 True and Error Scores; 1.4 Reliability and Validity; 1.5 Measurement Bias; 1.6 Exercises; Chapter 2: Probability; 2.1 About Formulas; 2.2 Basic Definitions; 2.3 Defining Probability; 2.4 Bayes' Theorem; 2.5 Enough Exposition, Let's Do Some Statistics!; 2.6 Exercises; Chapter 3: Inferential Statistics; 3.1 Probability Distributions; 3.2 Independent and Dependent Variables; 3.3 Populations and Samples; 3.4 The Central Limit Theorem; 3.5 Hypothesis Testing; 3.6 Confidence Intervals; 3.7 p-values; 3.8 The Z-Statistic; 3.9 Data Transformations; 3.10 Exercises; Chapter 4: Descriptive Statistics and Graphic Displays; 4.1 Populations and Samples; 4.2 Measures of Central Tendency; 4.3 Measures of Dispersion; 4.4 Outliers; 4.5 Graphic Methods; 4.6 Bar Charts; 4.7 Bivariate Charts; 4.8 Exercises; Chapter 5: Categorical Data; 5.1 The R×C Table; 5.2 The Chi-Square Distribution; 5.3 The Chi-Square Test; 5.4 Fisher's Exact Test; 5.5 McNemar's Test for Matched Pairs; 5.6 Proportions: The Large Sample Case; 5.7 Correlation Statistics for Categorical Data; 5.8 The Likert and Semantic Differential Scales; 5.9 Exercises; Chapter 6: The t-Test; 6.1 The t Distribution; 6.2 The One-Sample t-Test; 6.3 The Independent Samples t-Test; 6.4 Repeated Measures t-Test; 6.5 Unequal Variance t-Test; 6.6 Exercises; Chapter 7: The Pearson Correlation Coefficient; 7.1 Association; 7.2 Scatterplots; 7.3 The Pearson Correlation Coefficient; 7.4 The Coefficient of Determination; 7.5 Exercises; Chapter 8: Introduction to Regression and ANOVA; 8.1 The General Linear Model; 8.2 Linear Regression; 8.3 Analysis of Variance (ANOVA); 8.4 Calculating Simple Regression by Hand; 8.5 Exercises; Chapter 9: Factorial ANOVA and ANCOVA; 9.1 Factorial ANOVA; 9.2 ANCOVA; 9.3 Exercises; Chapter 10: Multiple Linear Regression; 10.1 Multiple Regression Models; 10.2 Exercises; Chapter 11: Logistic, Multinomial, and Polynomial Regression; 11.1 Logistic Regression; 11.2 Multinomial Logistic Regression; 11.3 Polynomial Regression; 11.4 Overfitting; 11.5 Exercises; Chapter 12: Factor Analysis, Cluster Analysis, and Discriminant Function Analysis; 12.1 Factor Analysis; 12.2 Cluster Analysis; 12.3 Discriminant Function Analysis; 12.4 Exercises; Chapter 13: Nonparametric Statistics; 13.1 Between-Subjects Designs; 13.2 Within-Subjects Designs; 13.3 Exercises; Chapter 14: Business and Quality Improvement Statistics; 14.1 Index Numbers; 14.2 Time Series; 14.3 Decision Analysis; 14.4 Quality Improvement; 14.5 Exercises; Chapter 15: Medical and Epidemiological Statistics; 15.1 Measures of Disease Frequency; 15.2 Ratio, Proportion, and Rate; 15.3 Prevalence and Incidence; 15.4 Crude, Category-Specific, and Standardized Rates; 15.5 The Risk Ratio; 15.6 The Odds Ratio; 15.7 Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio; 15.8 Power Analysis; 15.9 Sample Size Calculations; 15.10 Exercises; Chapter 16: Educational and Psychological Statistics; 16.1 Percentiles; 16.2 Standardized Scores; 16.3 Test Construction; 16.4 Classical Test Theory: The True Score Model; 16.5 Reliability of a Composite Test; 16.6 Measures of Internal Consistency; 16.7 Item Analysis; 16.8 Item Response Theory; 16.9 Exercises; Chapter 17: Data Management; 17.1 An Approach, Not a Set of Recipes; 17.2 The Chain of Command; 17.3 Codebooks; 17.4 The Rectangular Data File; 17.5 Spreadsheets and Relational Databases; 17.6 Inspecting a New Data File; 17.7 String and Numeric Data; 17.8 Missing Data; Chapter 18: Research Design; 18.1 Basic Vocabulary; 18.2 Observational Studies; 18.3 Quasi-Experimental Studies; 18.4 Experimental Studies; 18.5 Gathering Experimental Data; 18.6 Example Experimental Design; Chapter 19: Communicating with Statistics; 19.1 General Notes; Chapter 20: Critiquing Statistics Presented by Others; 20.1 Evaluating the Whole Article; 20.2 The Misuse of Statistics; 20.3 Common Problems; 20.4 Quick Checklist; 20.5 Issues in Research Design; 20.6 Descriptive Statistics; 20.7 Inferential Statistics; Review of Basic Mathematics; Laws of Arithmetic; Properties of Real Numbers; Exponents and Roots; Solving Equations; Systems of Equations; Graphing Equations; Linear Inequalities; Fractions; Factorials, Permutations, and Combinations; Exercises; Answers; Introduction to Statistical Packages; Minitab; SPSS; SAS; R; Microsoft Excel; References; Preface and General Sources; Chapter 1; Chapter 2; Chapter 3; Chapter 4; Chapter 5; Chapter 6; Chapter 7; Chapter 8; Chapter 9; Chapter 10; Chapter 11; Chapter 12; Chapter 13; Chapter 14; Chapter 15; Chapter 16; Chapter 17; Chapter 18; Chapter 19; Chapter 20; Probability Tables for Common Distributions; The Standard Normal Distribution; The t-Distribution; The Binomial Distribution; The Chi-Square Distribution; Online Resources; General Resources; Glossaries; Probability Tables; Online Calculators; Online Textbooks; Glossary of Statistical Terms; Colophon;