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Some contents of the Book

• Using R
• Statistics Textbooks and Other Resources
• Statistics for Educational Program Evaluation
• Why Statistics?
• Evaluation Tools and Surveys
• Variables, Descriptive Statistics, and Plots
• Types of Variables
• Descriptive Statistics
• Confidence Intervals
• Basic Plots
• Understanding Statistics and Hypothesis Testing
• Hypothesis Testing and p-values
• Reporting Results of Data and Analyses
• Choosing a Statistical Test
• Independent and Paired Values
• Likert Data
• Introduction to Likert Data
• Descriptive Statistics for Likert Item Data
• Plots for Likert Item Data
• Confidence Intervals for Medians
• Converting Numeric Data to Categories
• Traditional Nonparametric Tests
• Introduction to Traditional Nonparametric Tests
• Wilcoxon Signed-rank Test for One-sample Data
• Sign Test and Trinomial Test for One-sample Data
• Mann–Whitney U Test and Wilcoxon Rank-sum Test for Two-Sample Data
• Mood’s Median Test for Two-sample Data
• Wilcoxon Signed-rank Test for Two-sample Paired Data
• Sign Test and Trinomial Test for Two-sample Paired Data
• Kruskal–Wallis Test
• Mood’s Median Test
• Friedman Test
• Quade Test
• Scheirer–Ray–Hare Test
• Aligned Ranks Transformation ANOVA
• Kolmogorov–Smirnov, Lilliefors, Wald–Wolfowitz, and runs tests
• Nonparametric Regression and Local Regression
• Nonparametric Regression for Time Series
• Permutation Tests
• Introduction to Permutation Tests
• One-way Permutation Test for Ordinal Data
• One-way Permutation Test for Paired Ordinal Data
• Permutation Tests for Medians and Percentiles
• Ordinal Data in Tables
• Ordinal Chi-square and Association Tests for Ordinal Tables
• Effect Size Statistics and Measures of Association for Ordinal Tables
• Concepts for Linear Models
• Introduction to Linear Models
• Using Random Effects in Models
• What are Estimated Marginal Means?
• Estimated Marginal Means for Multiple Comparisons
• Post-hoc Contrasts and Polynomial Contrasts
• Factorial ANOVA: Main Effects, Interaction Effects, and Interaction Plots
• p-values and R-square Values for Models
• Accuracy and Errors for Models

Book Description

The goal of this book is to introduce to students interested in extension education, outreach, and public education to the quantitative methods used to assess the evaluation of these activities. 

Extension education includes a diverse collection of subject matter, including environmental science, home horticulture, agriculture, youth development, nutrition, and financial literacy.

Tools for evaluating educational programs may include in-class surveys that measure the knowledge gain of students in a course or follow-up surveys to determine the behaviors adopted by course participants.  Evaluation may also include any number of measured variables, perhaps the age of youth participants, the number of calories eaten daily by students in a nutrition program, or the organic matter content of farm fields managed by participating farmers.

The examples and methods here are chosen specifically to be applicable to the evaluation of extension education programs.  That being said, these methods are some of the most common used in the analysis of experiments, techniques used from diverse disciplines from manufacturing to environmental science to psychology, though each of these disciplines has additional methods used in specific situations.

Why Read This Book

If you’ve ever wondered how organizations figure out if their programs are workingthis book gives you the inside scoop. It’s not just about crunching numbers; it’s about telling a story with data and making better decisions. The author’s passion for clear communication shines through every page. You’ll learn how to avoid common pitfalls and get tips that only come from real experience (and maybe some trial and error). Plus, if you’re fascinated by extracting insights from complex databases, check out interactive visual analysis of NoSQL data for ideas on expanding your analytical toolkit.

Who This Book Is For

This book speaks to a wide crowd: extension agents, non-profit professionals, evaluators, grad studentsbasically anyone who wants to measure impact without getting lost in technical weeds. Don’t worry if you’re new to R or haven’t touched program evaluation before; the author starts from square one but still keeps things engaging for advanced readers. If you geek out over making sense of messy data or just want results that matter, this is your jam.

What You Will Learn

• How to design effective extension program evaluations using practical frameworks
• Step-by-step guidance on conducting statistical analyses in R
• Ways to interpret results and communicate findings clearly
• Troubleshooting tips for common challenges with data sets
• Real-world case studies that make concepts stick
• Best practices for collecting reliable evaluation data
• How to visualize outcomes so they actually make sense (and impress your boss!)
• Integrating feedback loops into your evaluation process

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