Statistics in Plain English

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Statistics in Plain English is a straightforward, conversational introduction to statistics that delivers exactly what its title promises. Each chapter begins with a brief overview of a statistic (or set of statistics) that describes what the statistic does and when to use it, followed by a detailed step-by-step explanation of how the statistic works and exactly what information it provides. Chapters also include an example of the statistic (or statistics) used in real-world research, "Worked Examples," "Writing It Up" sections that demonstrate how to write about each statistic, "Wrapping Up and Looking Forward" sections, and practice work problems.

Thoroughly updated throughout, this edition features several key additions and changes. First, a new chapter on person-centered analyses, including cluster analysis and latent class analysis (LCA) has been added, providing an important alternative to the more commonly used variable-centered analyses (e.g., t tests, ANOVA, regression). Next, the chapter on non-parametric statistics has been enhanced with in-depth descriptions of Mann-Whitney U, Kruskal-Wallis, and Wilcoxon Signed-Rank analyses, in addition to the detailed discussion of the Chi-square statistic found in the previous edition. These nonparametric statistics are widely used when dealing with nonnormally distributed data. This edition also includes more information about the assumptions of various statistics, including a detailed explanation of the assumptions and consequences of violating the assumptions of regression, as well as more coverage of the normal distribution in statistics. Finally, the book features a multitude of real-world examples throughout to aid student understanding and provides them with a solid understanding of how several statistics techniques commonly used by researchers in the social sciences work.

Statistics in Plain English is suitable for a wide range of readers, including students taking their first statistics course, professionals who want to refresh their statistical memory, and undergraduate or graduate students who need a concise companion to a more complicated text used in their class. The text works as a standalone or as a supplement and covers a range of statistical concepts from descriptive statistics to factor analysis and person-centered analyses.

Author(s): Timothy C. Urdan
Edition: 5
Publisher: Routledge
Year: 2022

Language: English
Pages: 304
City: London

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Acknowledgments
About the Author
Quick Guide to Statistics, Formulas, and Degrees of Freedom
Chapter 1: Introduction to Social Science Research Principles and Terminology
The Importance of Statistics and Research in Our Lives
Terminology Used in Statistics and Research Methods
Making Sense of Distributions and Graphs
Wrapping Up and Looking Forward
Chapter 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
Chapter 3: Measures of Variability
Range
Variance
Standard Deviation
Measures of Variability in Depth
Examples: Examining the Range, Variance, and Standard Deviation
Worked Examples
Wrapping Up and Looking Forward
Chapter 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
Chapter 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
Chapter 6: Standard Errors
What Is a Standard Error?
Standard Errors in Depth
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 of the Mean
Worked Examples
Wrapping Up and Looking Forward
Chapter 7: Statistical Significance, Effect Size, and Confidence Intervals
Statistical Significance in Depth
Assumptions of Parametric Tests
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
Chapter 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
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
Wrapping Up and Looking Forward
Chapter 9: One-Way Analysis of Variance
ANOVA vs. Independent t Tests
One-Way ANOVA in Depth
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
Chapter 10: Factorial Analysis of Variance
When to Use Factorial ANOVA
Some Cautions
Factorial ANOVA in Depth
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
Chapter 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
Chapter 12: Correlation
When to Use Correlation and What It Tells Us
Pearson Correlation Coefficients in Depth
Comparing Correlation Coefficients and Calculating Confidence Intervals: The Fisher’s Z Transformation
A Brief Word on Other Types of Correlation Coefficients
Example: The Correlation Between Grades and Test Scores
Worked Example: Statistical Significance of a Sample Correlation Coefficient
Writing It Up
Wrapping Up and Looking Forward
Chapter 13: Regression
Simple vs. Multiple Regression
Regression in Depth
Multiple Regression
Example: Predicting the Use of Self-Handicapping Strategies
Writing It Up
Worked Examples
Wrapping Up and Looking Forward
Chapter 14: Nonparametric Statistics
Mann-Whitney U
Mann-Whitney U Test in Depth
Wilcoxon Signed-Rank Test
Wilcoxon Signed-Rank Test in Depth
Kruskal-Wallis Test
Chi-square (χ2) 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
Chapter 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
Wrapping Up and Looking Forward
Chapter 16: Person-Centered Analysis
Cluster Analysis
Cluster Analysis in Depth
Latent Class Analysis
Latent Class Analysis in Depth
Writing It Up
Wrapping Up and Looking Forward
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: Table of the Fischer’s Z Transformations for Correlation Coefficients
Appendix F: Critical Values of the Mann-Whitney U Statistic
Appendix G: Critical Values for Wilcoxon Signed-Rank Test
Appendix H: Critical Values of the X2 Distributions
Bibliography
Glossary of Symbols
Glossary of Terms
Index