i. the project-based model

This Passion-Driven Statistics e-book includes a step-by-step curriculum for answering questions with data. It is designed for everyone including complete beginners!

You will drawn on existing data to pose research questions, and use a statistical software platform to answer those questions by turning raw data into useful information.

Passion-Driven Statistics “flips” the traditional classroom format. Rather than attending class sessions in order to listen to lectures, you will engage with online lessons as homework and use class time to work on a research project of your own design.

Chapter Description
Ch 1 provides an introduction to this project-based e-book.
Ch 2 describes how code books document the ways that data are arranged within data sets.
Ch 3 introduces the literature review process.
Ch 4 explains how to examine frequency distributions.
Ch 5 describes the process of data management.
Ch 6 demonstrates how and why to create univariate graphs.
Ch 7 demonstrates how and why to create bivariate graphs.
Ch 8 provides an overview of hypothesis testing.
Ch 9 covers analysis of variance.
Ch 10 covers the chi-square tests of independence.
Ch 11 covers the pearson correlation coefficient.
Ch 12 provides a concrete overview of of moderation, also known as statistical interaction.
Ch 13 covers the important concept of causation and when it can and cannot be observed.
Ch 14 presents multiple and logistic regression methods.
Ch 15 gives advice on creating effective poster presentations.

ii. supporting research

Passion-Driven Statistics has been implemented as a statistics course, a research methods course, a data science course, a capstone experience, and a summer research boot camp with students from a wide variety of academic settings. Liberal arts colleges, large state universities, regional colleges/universities, medical schools, community colleges, and high schools have all successfully implemented the model. Research evaluating the model has been exciting to see unfold. The curriculum attracts higher rates of under-represented (UR) students compared to a traditional statistics course and students enrolled in Passion-Driven Statistics are more likely to report increased confidence in working with data and increased interest in pursuing advanced statistics coursework.The project-based curriculum has also been found to promote further training in statistics. Using causal inference techniques to achieve matched comparisons across three different statistics courses, students originally enrolled in Passion-Driven Statistics were significantly more likely to take at least one additional undergraduate course focused on statistical concepts, applied data analysis, and/or use of statistical software compared to students taking either a psychology statistics course or math statistics course.

Nazzaro, V., Rose, J., Dierker, L. (2020) A comparison of future course enrollment among students completing one of four different introductory statistics courses. Statistics Education Research Journal. 19(3), 6–17,

Dierker, L., Flaming, K., Cooper, J., Singer-Freeman, K., Germano, K., & Rose, J. (2018). Evaluation impact: A comparison of learning experiences and outcomes of students completing a traditional versus multidisciplinary, project-based introductory statistics course. International Journal of Education, Training and Learning, 2(1), 16-28. DOI:10.33094/6.2017.2018.21.16.28

Dierker, L., Robertson Evia, J., Singer-Freeman, K., Woods, K., Zupkus, J., Arnholt, A., Moliski, E.G., Delia Deckard, N. Gallagher, K., Rose, J., (2018). Project-based learning in introductory statistics: Comparing course experiences and predicting positive outcomes for students from diverse educational settings. International Journal of Educational Technology and Learning, 3(2), 52-64. DOI:10.20448/2003.32.52.64

Dierker, L., Cooper, J., Selya, A., Alexander, J., Rose, J. (2015) Evaluating access: A comparison of demographic and disciplinary characteristics of students enrolled in a traditional introductory statistics course vs. a multidisciplinary, project-based course, Journal of Interdisciplinary Studies of Education. 4(1), 22-37.

Many student reactions have supported the positive impact of the course. In anonymous post-course evaluations, one student wrote, “I have never felt so excited and motivated to be part of an academic environment as I have in this class. I am so proud of my work.” Another wrote, “Allowing students to pick from a study and data set to answer their own research question was effective because we became attached to our own projects, understood exactly why we were learning what we were learning, and wanted to know more.” Finally, “Though the structure of the class is unorthodox, the resulting education is priceless. Aside from teaching me the valuable process and application of statistical inquiry, this course taught me how to take initiative and start a scientific project that I can call my own.”

iii. using the e-book

The e-book is broken into 15 chapters. Each provides a brief overview of what you have learned, a summary of the upcoming lesson, links to the video lessons, and a corresponding project assignment.

For each chapter:
1. Read the overview and lesson summary.
2. Use the appropriate link to watch the video lesson.
3. Use the model code/instructions to complete the assignment.

Model code is provided as part of several of the chapters. You can copy/paste relevant code into your own software program and make needed changes based on the variables you have selected and the formatting rules for your software platform.

If you are using SPSS, lessons include both a video and a print screen tutorial. Python video lessons are segmented. After watching each segment, select the next segment on the right side of the YouTube screen (e.g., 1/4, 2/4, 3/4, 4/4).

We recommend keeping a journal or lab notebook to track the progress of your project. You will find this to be a valuable research tool that saves time and unnecessary work.