Statistics does not have to be overwhelming.
If you are a doctoral student preparing for dissertation research, you do not need more equations; you need clarity. You need to understand how research questions connect to statistical decisions, how to evaluate assumptions, and how to confidently explain your methods during your defense.
Doctoral Statistics Demystified is a practical, applied guide written specifically for doctoral students in education, leadership, business, psychology, and the social sciences. Rather than overwhelming readers with dense theory or abstract mathematical proofs, this book focuses on what doctoral researchers actually need:
- How to align research questions with the correct statistical test
- How to evaluate assumptions before interpreting results
- How to determine appropriate sample size using power analysis
- How to conduct analyses step-by-step using JASP
- How to report findings clearly in APA format
- How to defend statistical decisions with confidence
From t-tests and ANOVA to regression, logistic regression, reliability analysis, factor analysis, and structural equation modeling, each chapter includes:
- Clear explanations in plain language
- Guided JASP walkthroughs
- Assumption-testing frameworks
- APA reporting examples
- Common doctoral mistakes to avoid
- Defense preparation questions
This book moves beyond "running statistics" and teaches you how to think statistically. It bridges the gap between statistical procedures and dissertation application, helping you move from anxiety to authority.
If you have ever said, "I'm just not good at statistics," this book was written for you.
Part I Foundations: Thinking Like a Doctoral Researcher
Chapter 1: Why You Are Not “Bad at Statistics”
Chapter 2: Research Questions Drive Statistics
Chapter 3: Descriptive Statistics and Data Screening
Chapter 4: Hypothesis Testing Fundamentals: From Question to Statistical Decision
Chapter 5: Confidence Intervals and Effect Sizes: Moving Beyond “Significant or Not”
Chapter 6: Power Analysis and Sample Size: Using G*Power to Plan Strong Studies
Chapter 7: Assumptions for Group Comparisons: Protecting t-Tests and ANOVA
Part II Core Statistical Tests—Comparing Differences in Means
Chapter 8: Comparing Two Groups: Independent and Paired Samples t-Tests
Chapter 9: Comparing Three or More Groups: ANOVA, Factorial ANOVA, ANCOVA, and Repeated Measures ANOVA
Part III Relationships and Prediction
Chapter 10: Correlation and Regression: From Association to Prediction
Chapter 11: Nonparametric Alternatives: When Parametric Assumptions Are Not Met
Chapter 12: Logistic Regression: Predicting Categorical Outcomes
Chapter 13: Chi-Square Testing: Analyzing Relationships Between Categorical Variables