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.
Specific learning goals
One goal of this book is to give readers the skills and abilities to be able to understand the graphs and statistics that you might encounter in a publication such as the Journal of Extension or other academic reports of program results. As examples, students will be able to answer the questions: What can I conclude from this bar plot? How do I interpret this p-value? What is an r-squared value?
A second goal is for readers to be able to design and analyze their own program evaluation experiments in order to document the impacts of their extension teaching or research. What analysis would I use to assess knowledge gain with before-and-after surveys? What statistics should I report to convey the results of this analysis? Can I explain the results with a graph?
This book is written for students at the undergraduate level with no prior knowledge of the analysis of experiments, and with no prior knowledge of computer programming. This being said, students with no background in these areas will need to apply care and dedication in order to understand the material and the computer code used in examples. These students may also need to explore the optional readings to obtain a better foundation in statistical thinking and theory.
This book is written with the intention of providing users simple and complete examples of analyses common in the evaluation of extension education programs using the R Project for Statistical Computing. I have chosen the computer code to both be as clear as possible for a beginner to follow, but also to demonstrate some of the options available in conducting analyses or producing plots of data. In some cases, the code is more difficult to follow than in others.
Likewise, discussion of the assumptions and theoretical considerations for the statistical analyses included here is limited. Readers are encouraged to understand these considerations in more depth when using these analyses.
What this book will not cover
Designing and conducting surveys
There are many skills and considerations that go into conducting competent assessments of education programs. This book will not cover these many of these topics in any depth. For example, good survey design and effective survey questions will be touched on only very briefly. Conducting surveys well, for example by avoiding sampling bias, will also not be covered in any significant way.
Advanced statistical analyses
There are variety of relatively advanced statistical analyses that are used in even relatively simple studies. This book focuses on only the most basic analyses for common designs used in extension evaluation. A solid understanding of these analyses will give the reader the foundation for exploring more complicated analyses as the student wishes or the situation calls for.
R is a flexible and powerful programming language. Readers of this book will benefit from learning the basics of programming in R; however, descriptions of R programming will be kept to a minimum here. There are books and online resources available to learn R programming. A few places to start are included in the Statistics Textbooks and Other Resources chapter.