In our recurring 10 Questions series, the Neag School catches up with students, alumni, faculty, and others throughout the year to offer a glimpse into their Neag School experience and their current career, research, or community activities.

Jacqueline Caemmerer, assistant professor of school psychology, recently co-authored a book with Timothy Z. Keith and Matthew Reynolds, “Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling,” which presents multiple regression (MR) and structural equation modeling (SEM) in an accessible, concept-focused way that supports effective research.

Since joining UConn in 2020, Caemmerer, a licensed and nationally certified school psychologist, has taught courses in cognitive, academic, and social-emotional behavioral assessment, tests, and measures, as well as advanced practicums. Her research focuses on what commonly used tests measure; how cognitive abilities predict children’s math, reading, and writing skills; and the developmental and cultural factors that affect testing. Her previous research was selected as the Editor’s Choice Paper in Psychological Assessment.

Along with her bachelor’s, master’s, and doctoral degrees in psychology and school psychology from Binghamton University, Columbia University, and the University of Texas at Austin, respectively, Caemmerer also earned her master’s in quantitative methods and a certificate in applied statistical modeling from UT, equipping her to support evidence-based research and practice.

Q: Where did the inspiration for this book come from, and how long was it in the works for?

A: We worked on this book for two years before publication. This is the fourth edition of a statistics textbook, so we updated earlier editions with new methods, advice, and guidance. Since the first edition was published in 2005, we also updated recommendations about which statistical software can be used.

Q: How did you get involved with the other co-authors?

A: The first author, Tim Keith, was my advisor during my doctoral training. He taught me how to apply the statistical techniques discussed in the textbook. He later invited me and another former student, Matt Reynolds, to help revise the book.

Q: What is the focus of the book and how do you convey that through its content?

A: It’s a textbook on performing statistical analyses, demonstrating how to do so in different software programs and how to interpret results. We include many real-world examples so students and researchers new to statistics can see how to analyze data to answer research questions. We selected examples from real journal articles across the field of education.

Q: How do you feel now that the book is complete?

A: It’s exciting after working on something for two years. Having it published is a relief, but it’s not entirely done — the textbook includes a website with all the code for running analyses, so students and researchers can teach themselves. We also provide additional code and answers for instructors using the textbook in class, and we’re still finalizing that piece.

Q: What have you learned through the writing process that surprised you? And how might this compare with other projects you’ve been involved with?

A: This is the first book I have worked on. The size and scope of a book project, compared to writing research articles, which is what I’m most familiar with, is quite different, especially given how long it takes. The many chapters to review and rewrite, plus the detailed steps of putting a book together, were all very different from writing research articles. If anything, what surprised me was just how long everything took.

This textbook helps researchers build confidence in using statistical techniques to answer questions and apply those skills to improve decision-making, policies, and practices in schools. Strong statistical methods support high-quality research. — Jacqueline Caemmemer

Q: In what ways has this broader research and writing process influenced your work at the Neag School?

A: In the school psychology program, I teach field-specific and applied clinical skills, not statistics. But I do advise doctoral students conducting research with me and later independently. The techniques in this book are ones I use in my own research, and it’s a resource I often return to and recommend to my students. I also learned more by helping to update it.

Q: Can you briefly explain what multiple regression (MR) and structural equation modeling (SEM) are?

A: These are two methods researchers use to study how different factors relate to outcomes. Multiple regression examines how several predictors affect one result — for example, how a student’s relationship with a teacher influences math performance. Structural equation modeling builds on that by exploring how many factors affect multiple outcomes at once, such as performance in math, English, and science. It also accounts for measurement error, giving researchers a clearer picture of the “true” ideas being studied. This textbook combines both techniques to help students move from understanding multiple regression to understanding structural equation modeling.

Q: What is the relevance of this research, given the current academic climate?

A: Our book aims to present these complex statistical concepts in a clear, conceptual way. Many students are often intimidated by having to take statistical classes that may be a singular requirement within their undergraduate or graduate program.

We try to present those concepts in a more approachable way and tie them more to real-world examples rather than how you would calculate things by hand, so I think that’s always relevant, as students are always feeling intimidated by taking statistics.

In this newest edition, we’ve incorporated the free statistical software R for the first time. I think that’s been a big push in the field for people to be able to use freely available statistical software. More and more training programs are moving to having students use R, and once they graduate and are out in their future careers, that may be software they are able to use.

Q: What are the possible applications of these findings in schools?

A: This textbook helps researchers build confidence in using statistical techniques to answer questions and apply those skills to improve decision-making, policies, and practices in schools. Strong statistical methods support high-quality research.

Q: What do you hope the larger audience takes away from this book?

A: Because we’re trying to make learning, applying, and interpreting statistics more accessible, I hope readers will realize statistics can be approachable and achievable.

The cover of the textbook is a beach scene, as in past editions — chosen intentionally to evoke calm and emphasize that learning statistics can be relaxing, not intimidating.

To learn more about Jacqueline Caemmerer’s work, email her at jacqueline.caemmerer@uconn.edu.