Statistics in a Nutshell

by Boslaugh, Sarah

• ISBN13: 9781449316822
• ISBN10: 1449316824
• Edition: 2nd
• Format: Paperback
• Copyright: 2012-11-22
• Publisher: Oreilly & Associates Inc

Summary

Need to learn statistics as part of your job, or want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone who's new to the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. Each chapter in this thoroughly revised and expanded edition presents easy-to-follow descriptions illustrated by graphics, formulas, and lots 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. Statistics in a Nutshell includes: Introductory material-Learn basic concepts of measurement and probability theory, data management for statistical analysis, research design and experimental design, how to write up your own results, and how to critique statistics presented by others Basic inferential statistics-Discover the concepts of hypothesis testing, simple correlation, the distinction between parametric and nonparametric statistics, and learn simple methods of analysis appropriate to dichotomous, categorical, and continuous variables Advanced inferential techniques-Learn the General Linear Model, including analysis of Variance (ANOVA), multiple linear regression, and logistic and multinomial regression Specialized techniques-Use and interpret business and quality improvement statistics, medical and public health statistics, and educational and psychological statistics If you need to know how to perform most common statistical analyses, and how to use a wide range of statistical techniques without getting in over your head, this is the book for you.

Author Biography

Sarah Boslaugh, PhD, MPH, a grant and proposal writer at Kennesaw State University, has worked as a statistician and computer programmer for more than 20 years. She's the author of An Intermediate Guide to SPSS Programming (Sage), Secondary Data Sources for Public Health (Cambridge University Press), Statistics in a Nutshell (first edition; O'Reilly), and editor of The Encyclopedia of Epidemiology (Sage).

Table of Contents

Preface	p. ix
Basic Concepts of Measurement	p. 1
Measurement	p. 2
Levels of Measurement	p. 2
True and Error Scores	p. 8
Reliability and Validity	p. 10
Measurement Bias	p. 14
Exercises	p. 17
Probability	p. 21
About Formulas	p. 22
Basic Definitions	p. 23
Defining Probability	p. 30
Bayes' Theorem	p. 34
Enough Exposition, Let's Do Some Statistics!	p. 36
Exercises	p. 38
Inferential Statistics	p. 45
Probability Distributions	p. 46
Independent and Dependent Variables	p. 53
Populations and Samples	p. 54
The Central Limit Theorem	p. 59
Hypothesis Testing	p. 64
Confidence Intervals	p. 67
p-values	p. 68
The Z-Statistic	p. 70
Data Transformations	p. 72
Exercises	p. 75
Descriptive Statistics and Graphic Displays	p. 83
Populations and Samples	p. 83
Measures of Central Tendency	p. 84
Measures of Dispersion	p. 90
Outliers	p. 96
Graphic Methods	p. 97
Bar Charts	p. 100
Bivariate Charts	p. 111
Exercises	p. 117
Categorical Data	p. 121
The R×C Table	p. 122
The Chi-Square Distribution	p. 125
The Chi-Square Test	p. 127
Fisher's Exact Test	p. 132
McNemar's Test for Matched Pairs	p. 134
Proportions: The Large Sample Case	p. 136
Correlation Statistics for Categorical Data	p. 138
The Likert and Semantic Differential Scales	p. 145
Exercises	p. 147
The t-Test	p. 155
The t Distribution	p. 155
The One-Sample t-Test	p. 157
The Independent Samples t-Test	p. 160
Repeated Measures t-Test	p. 164
Unequal Variance t-Test	p. 167
Exercises	p. 168
The Pearson Correlation Coefficient	p. 173
Association	p. 174
Scatterplots	p. 175
The Pearson Correlation Coefficient	p. 182
The Coefficient of Determination	p. 187
Exercises	p. 188
Introduction to Regression and ANOVA	p. 193
The General Linear Model	p. 193
Linear Regression	p. 195
Analysis of Variance (ANOVA)	p. 206
Calculating Simple Regression by Hand	p. 212
Exercises	p. 214
Factorial ANOVA and ANCOVA	p. 223
Factorial ANOVA	p. 223
ANCOVA	p. 233
Exercises	p. 238
Multiple Linear Regression	p. 243
Multiple Regression Models	p. 243
Exercises	p. 267
Logistic, Multinomial, and Polynomial Regression	p. 273
Logistic Regression	p. 273
Multinomial Logistic Regression	p. 279
Polynomial Regression	p. 282
Overfitting	p. 285
Exercises	p. 287
Factor Analysis, Cluster Analysis, and Discriminant Function Analysis	p. 291
Factor Analysis	p. 291
Cluster Analysis	p. 299
Discriminant Function Analysis	p. 302
Exercises	p. 305
Nonparametric Statistics	p. 307
Between-Subjects Designs	p. 308
Within-Subjects Designs	p. 317
Exercises	p. 321
Business and Quality Improvement Statistics	p. 325
Index Numbers	p. 325
Time Series	p. 331
Decision Analysis	p. 334
Quality Improvement	p. 339
Exercises	p. 347
Medical and Epidemiological Statistic	p. 351
Measures of Disease Frequency	p. 351
Ratio, Proportion, and Rate	p. 352
Prevalence and Incidence	p. 354
Crude, Category-Specific, and Standardized Rates	p. 357
The Risk Ratio	p. 362
The Odds Ratio	p. 367
Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio	p. 370
Power Analysis	p. 375
Sample Size Calculations	p. 377
Exercises	p. 380
Educational and Psychological Statistics	p. 385
Percentiles	p. 386
Standardized Scores	p. 388
Test Construction	p. 390
Classical Test Theory: The True Score Model	p. 393
Reliability of a Composite Test	p. 394
Measures of Internal Consistency	p. 395
Item Analysis	p. 400
Item Response Theory	p. 403
Exercises	p. 408
Data Management	p. 411
An Approach, Not a Set of Recipes	p. 412
The Chain of Command	p. 413
Codebooks	p. 413
The Rectangular Data File	p. 415
Spreadsheets and Relational Databases	p. 418
Inspecting a New Data File	p. 418
String and Numeric Data	p. 422
Missing Data	p. 423
Research Design	p. 425
Basic Vocabulary	p. 426
Observational Studies	p. 428
Quasi-Experimental Studies	p. 431
Experimental Studies	p. 436
Gathering Experimental Data	p. 437
Example Experimental Design	p. 447
Communicating with Statistics	p. 449
General Notes	p. 449
Critiquing Statistics Presented by Others	p. 457
Evaluating the Whole Article	p. 457
The Misuse of Statistics	p. 458
Common Problems	p. 459
Quick Checklist	p. 461
Issues in Research Design	p. 463
Descriptive Statistics	p. 466
Inferential Statistics	p. 470
Review of Basic Mathematics	p. 473
Introduction to Statistical Packages	p. 499
References	p. 513
Probability Tables for Common Distributions	p. 527
Online Resources	p. 539
Glossary of Statistical Terms	p. 543
Index	p. 553
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