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Statistics in Plain English

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This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as "t" tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. Using the author s own data and examples from published research and the popular media, the book is a straightforward and accessible guide to statistics.

New features in the fourth edition include:



sets of work problems in each chapter with detailed solutions and additional problems online to help students test their understanding of the material,

new ""Work Examples"" to walk students through how to calculate and interpret the statistics featured in each chapter,

new examples from the author s own data and from published research and the popular media to help students see how statistics are applied and written about in professional publications,

many more examples, tables, and charts to help students visualize key concepts, clarify concepts, and demonstrate how the statistics are used in the real world.

a more logical flow, with correlation directly preceding regression, and a combined glossary appearing at the end of the book,

a Quick Guide to Statistics, Formulas, and Degrees of Freedom at the start of the book, plainly outlining each statistic and when students should use them,

greater emphasis on (and description of) effect size and confidence interval reporting, reflecting their growing importance in research across the social science disciplines

an expanded website at www.routledge.com/cw/urdan with PowerPoint presentations, chapter summaries, a new test bank, interactive problems and detailed solutions to the text s work problems, chapter summaries, SPSS datasets for practice, links to useful tools and resources, and videos showing how to calculate statistics, how to calculate and interpret the appendices, and how to understand some of the more confusing tables of output produced by SPSS.

"

Statistics in Plain English, Fourth Edition" is an ideal guide for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences. "

Author(s): Timothy C. Urdan
Edition: 4
Publisher: Routledge
Year: 2017

Language: English
Pages: xx+266

Cover
Half Title
Series page
Title Page
Copyright Page
Brief Contents
Table of Contents
Preface
Acknowledgments
About the Author
Quick Guide to Statistics, Formulas, and Degrees of Freedom
1 Introduction to Social Science Research Principles and Terminology
Populations, Samples, Parameters, and Statistics
Inferential and Descriptive Statistics
Sampling Issues
Types of Variables and Scales of Measurement
Research Designs
Making Sense of Distributions and Graphs
Wrapping Up and Looking Forward
Work Problems
Note
2 Measures of Central Tendency
Measures of Central Tendency in Depth
Example: The Mean, Median, and Mode of Skewed Distributions
Writing it Up
Wrapping Up and Looking Forward
Work Problems
Note
3 Measures of Variability
Range
Variance
Standard Deviation
Measures of Variability in Depth
Calculating the Variance and Standard Deviation
Why Have Variance?
Examples: Examining the Range, Variance, and Standard Deviation
Worked Examples
Wrapping Up and Looking Forward
Work Problems
Notes
4 The Normal Distribution
Characteristics of the Normal Distribution
Why Is the Normal Distribution So Important?
The Normal Distribution in Depth
The Relationship Between the Sampling Method and the Normal Distribution
Skew and Kurtosis
Example 1: Applying Normal Distribution Probabilities to a Normal Distribution
Example 2: Applying Normal Distribution Probabilities to a Nonnormal Distribution
Wrapping Up and Looking Forward
Work Problems
5 Standardization and z Scores
Standardization and z Scores in Depth
Interpreting z Scores
Examples: Comparing Raw Scores and z Scores
Worked Examples
Wrapping Up and Looking Forward
Work Problems
6 Standard Errors
What Is a Standard Error?
Standard Errors in Depth
The Conceptual Description of the Standard Error of the Mean
How to Calculate the Standard Error of the Mean
The Central Limit Theorem
The Normal Distribution and t Distributions: Comparing z Scores and t Values
The Use of Standard Errors in Inferential Statistics
Example: Sample Size and Standard Deviation Effects on the Standard Error
Worked Examples
Wrapping Up and Looking Forward
Work Problems
7 Statistical Significance, Effect Size, and Confidence Intervals
Statistical Significance in Depth
Samples and Populations
Probability
Hypothesis Testing and Type I Errors
Limitations of Statistical Significance Testing
Effect Size in Depth
Confidence Intervals in Depth
Example: Statistical Significance, Confidence Interval, and Effect Size for a One-Sample t Test of Motivation
Wrapping up and Looking Forward
Work Problems
Notes
8 t Tests
What Is a t Test?
t Distributions
The One-Sample t Test
The Independent Samples t Test
Dependent (Paired) Samples t Test
Independent Samples t Tests in Depth
Conceptual Issues with the Independent Samples t Test
The Standard Error of the Difference between Independent Sample Means
Determining the Significance of the t Value for an Independent Samples t Test
Paired or Dependent Samples t Tests in Depth
Example 1: Comparing Boys’ and Girls’ Grade Point Averages
Example 2: Comparing Fifth- and Sixth-Grade GPAs
Writing it Up
Worked Examples
One-Sample t Test
Independent Samples t Test
Dependent/Paired t Test
Wrapping Up and Looking Forward
Work Problems
Note
9 One-Way Analysis of Variance
ANOVA vs. Independent t Tests
One-Way ANOVA in Depth
Deciding if the Group Means Are Significantly Different
Post-Hoc Tests
Effect Size
Example: Comparing the Sleep of 5-, 8-, and 12-Year-Olds
Writing it Up
Worked Example
Wrapping Up and Looking Forward
Work Problems
Notes
10 Factorial Analysis of Variance
When to Use Factorial ANOVA
Some Cautions
Factorial ANOVA in Depth
Main Effects and Controlled or Partial Effects
Interactions
Interpreting Main Effects in the Presence of an Interaction Effect
Testing Simple Effects
Analysis of Covariance
Illustration of Factorial ANOVA, ANCOVA, and Effect Size with Real Data
Example: Performance, Choice, and Public vs. Private Evaluation
Writing it Up
Wrapping Up and Looking Forward
Work Problems
11 Repeated-Measures Analysis of Variance
When to Use Each Type of Repeated-Measures Technique
Repeated-Measures ANOVA in Depth
Repeated-Measures Analysis of Covariance (ANCOVA)
Adding an Independent Group Variable
Example: Changing Attitudes about Standardized Tests
Writing it Up
Wrapping Up and Looking Forward
Work Problems
12 Correlation
When to Use Correlation and What it Tells Us
Pearson Correlation Coefficients in Depth
What the Correlation Coefficient Does, and Does Not, Tell Us
The Coefficient of Determination
Statistically Significant Correlations
A Brief Word on Other Types of Correlation Coefficients
Point-Biserial Correlation
Phi
Spearman Rho
Example: The Correlation Between Grades and Test Scores
Worked Example: Statistical Significance and Confidence Interval
Writing it Up
Wrapping Up and Looking Forward
Work Problems
Notes
13 Regression
Simple vs. Multiple Regression
Variables Used in Regression
Regression in Depth
Multiple Regression
An Example Using SPSS
Example: Predicting the Use of Self-Handicapping Strategies
Writing it Up
Worked Examples
Wrapping Up and Looking Forward
Work Problems
Notes
14 The Chi-Square Test of Independence
Chi-Square Test of Independence in Depth
Example: Generational Status and Grade Level
Writing it Up
Worked Example
Wrapping Up and Looking Forward
Work Problems
15 Factor Analysis and Reliability Analysis: Data Reduction Techniques
Factor Analysis in Depth
A More Concrete Example of Exploratory Factor Analysis
Confirmatory Factor Analysis: A Brief Introduction
Reliability Analysis in Depth
Writing it Up
Work Problems
Wrapping Up
Notes
Appendices
Appendix A: Area Under the Normal Curve Beyond z
Appendix B: Critical Values of the t Distributions
Appendix C: Critical Values of the F Distributions
Appendix D: Critical Values of the Studentized Range Statistic (For Tukey HSD Tests)
Appendix E: Critical Values Of The χ2 Distributions
Bibliography
Glossary of Terms
Glossary of Symbols
Index