Synopses & Reviews
Need to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts.
You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis.
Organized into four distinct sections, Statistics in a Nutshell offers you:
- Different ways to think about statistics
- Basic concepts of measurement and probability theory
- Data management for statistical analysis
- Research design and experimental design
- How to critique statistics presented by others
Basic inferential statistics:
- Basic concepts of inferential statistics
- The concept of correlation, when it is and is not an appropriate measure of association
- Dichotomous and categorical data
- The distinction between parametric and nonparametric statistics
Advanced inferential techniques:
- The General Linear Model
- Analysis of Variance (ANOVA) and MANOVA
- Multiple linear regression
- Business and quality improvement statistics
- Medical and public health statistics
- Educational and psychological statistics
Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests.
With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.
A clear and concise introduction and reference, this work gives readers a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts.
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).
Paul A. Watters PhD CITP, is Associate Professor in the School of Information and Mathematical Sciences and Centre for Informatics and Applied Optimization (CIAO) at the University of Ballarat. Until recently, he was Head of Data Services at the Medical Research Council's National Survey of Health and Development, which is the oldest of the British birth cohort studies, and an honorary senior research fellow at University College London. He uses multivariate statistics to develop orthogonal and non-orthogonal methods for feature extraction in pattern recognition, especially in biometric applications.
Table of Contents
Preface; 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's Theorem; 2.5 Enough Exposition, Let's Do Some Statistics!; 2.6 Exercises; Chapter 3: Data Management; 3.1 An Approach, Not a Set of Recipes; 3.2 The Chain of Command; 3.3 Codebooks; 3.4 The Rectangular Data File; 3.5 Spreadsheets and Relational Databases; 3.6 Inspecting a New Data File; 3.7 String and Numeric Data; 3.8 Missing Data; Chapter 4: Descriptive Statistics and Graphics; 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: Research Design; 5.1 Observational Studies; 5.2 Experimental Studies; 5.3 Gathering Experimental Data; 5.4 Inference and Threats to Validity; 5.5 Eliminating Bias; 5.6 Example Experimental Design; Chapter 6: Critiquing Statistics Presented by Others; 6.1 The Misuse of Statistics; 6.2 Common Problems; 6.3 Quick Checklist; 6.4 Research Design; 6.5 Descriptive Statistics; 6.6 Inferential Statistics; Chapter 7: Inferential Statistics; 7.1 Probability Distributions; 7.2 Independent and Dependent Variables; 7.3 Populations and Samples; 7.4 The Central Limit Theorem; 7.5 Hypothesis Testing; 7.6 Confidence Intervals; 7.7 p-values; 7.8 Data Transformations; 7.9 Exercises; Chapter 8: Thet-Test; 8.1 The t Distribution; 8.2 t-Tests; 8.3 One-Sample t-Test; 8.4 Two-Sample t-Test; 8.5 Repeated Measures t-Test; 8.6 Unequal Variance t-Test; 8.7 Effect Size and Power; 8.8 Exercises; Chapter 9: The Correlation Coefficient; 9.1 Measuring Association; 9.2 Graphing Associations Through Scatterplots; 9.3 Pearson's Product-Moment Correlation Coefficient; 9.4 Coefficient of Determination; 9.5 Spearman Rank-Order Coefficient; 9.6 Advanced Techniques; Chapter 10: Categorical Data; 10.1 The R×C Table; 10.2 The Chi-Square Distribution; 10.3 The Chi-Square Test; 10.4 Fisher's Exact Test; 10.5 McNemar's Test for Matched Pairs; 10.6 Correlation Statistics for Categorical Data; 10.7 The Likert and Semantic Differential Scales; 10.8 Exercises; Chapter 11: Nonparametric Statistics; 11.1 Nonnormal Data; 11.2 Between Subjects Designs; 11.3 Within-Subjects Designs; 11.4 Exercises; Chapter 12: Introduction to the General Linear Model; 12.1 The General Linear Model; 12.2 Linear Regression; 12.3 Analysis of Variance (ANOVA); 12.4 Exercises; Chapter 13: Extensions of Analysis of Variance; 13.1 Factorial ANOVA; 13.2 MANOVA; 13.3 ANCOVA; 13.4 Repeated Measures ANOVA; 13.5 Mixed Designs; Chapter 14: Multiple Linear Regression; 14.1 Multiple Regression Models; 14.2 Common Problems with Multiple Regression; 14.3 Exercises; Chapter 15: Other Types of Regression; 15.1 Logistic Regression; 15.2 Logarithmic Transformations; 15.3 Polynomial Regression; 15.4 Overfitting; Chapter 16: Other Statistical Techniques; 16.1 Factor Analysis; 16.2 Cluster Analysis; 16.3 Discriminant Function Analysis; 16.4 Multidimensional Scaling; Chapter 17: Business and Quality Improvement Statistics; 17.1 Index Numbers; 17.2 Time Series; 17.3 Decision Analysis; 17.4 Quality Improvement; 17.5 Exercises; Chapter 18: Medical and Epidemiological Statistics; 18.1 Measures of Disease Frequency; 18.2 Ratio, Proportion, and Rate; 18.3 Prevalence and Incidence; 18.4 Crude, Category-Specific, and Standardized Rates; 18.5 The Risk Ratio; 18.6 The Odds Ratio; 18.7 Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio; 18.8 Power Analysis; 18.9 Sample Size Calculations; 18.10 Exercises; Chapter 19: Educational and Psychological Statistics; 19.1 Percentiles; 19.2 Standardized Scores; 19.3 Test Construction; 19.4 Classical Test Theory: The True Score Model; 19.5 Reliability of a Composite Test; 19.6 Measures of Internal Consistency; 19.7 Item Analysis; 19.8 Item Response Theory; 19.9 Exercises; Review of Basic Mathematics; Introduction to Statistical Packages; References; Colophon;