Statistics Made Simple for School Leaders 3rd Edition By Susan Rovezzi Carroll, David Carroll
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- 137
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- Education
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About this ebook
The Statistics Made Simple for School Leaders: A New Approach for Using Student, Staff, and Community Data (3rd Edition) by Susan Rovezzi Carroll and David J. Carroll is designed as a practical handbook to help educational administrators make data-driven decisions. Rather than focusing on heavy mathematical formulas, it emphasizes the conceptual logic, execution, and clear interpretation of data. [1, 2, 3, 4]
The book is organized into 13 core chapters covering the following topics and statistical procedures: [1, 2]
Foundational Data Literacy
- Chapter 1: Statistics: A Powerful Tool for School Leaders – Introduction to data-based decision-making and how statistics can be a leader's most potent management tool.
- Chapter 2: Measurement: The Foundation of Data Literacy – Explains variables and the four foundational scales of measurement.
- Chapter 3: Data: Steps in Management – Guidance on how to clean, structure, and manage datasets, including frequency distributions. [1, 2]
Presenting and Describing Data
- Chapter 4: Graphs: A Story with Data – Using visual charts and graphing techniques to translate numbers into a clear, compelling story. [1]
- Chapter 5: Descriptive Statistics: Great Communication Tools – Utilizing averages, percentages, and basic summaries to effectively communicate school performance results to staff and the community. [1, 2]
- Chapter 6: Variability: Partner of the Mean – Understanding variance, standard deviations, and why the spread or range in your school's data matters. [1, 2]
Sampling and Testing Hypotheses
- Chapter 7: Sampling: An Overlooked Management Tool – Explains random sampling and other strategies to pull reliable subsets of student or community data. [1]
- Chapter 8: Hypotheses: Testing Assumptions – Formulating research assumptions and understanding the basic principles of statistical testing. [1]
- Chapter 9: The t-test: Comparing Two Groups – Evaluating whether a statistically significant difference exists when comparing two distinct groups (e.g., test scores of two different classes). [1]
Advanced and Relational Analyses
- Chapter 10: ANOVA: Comparing Three or More Groups – Running a One-Way Analysis of Variance to find meaningful differences across multiple programs or groups simultaneously.
- Chapter 11: Chi-Square Analyses: Comparing Categories – Non-parametric statistics used to compare categorical variables (e.g., enrollment categories or survey choices).
- Chapter 12: Correlations: Relationships with Two Variables – Discovering whether two factors move together (e.g., student attendance and overall GPA).
- Chapter 13: Regression: Predicting and Explaining Relationships – Using tracking metrics to forecast trends and model potential outcomes based on historical school or community data. [1, 2]
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