Advanced Modeling and Reproducibility for Educational Scientists

Front Matter

The content in this “book”, as the title suggests, is related to advanced statistical modeling and reproducibility. More specifically, the content focuses on expanding the General Linear Model (GLM) to provide statistical evidence that can help answer substantive questions in the educational and social sciences. It also includes computing content for leveling-up educational scientists’ reproducibility of analyses.

While it is a book intended for applied practitioners in the educational or social sciences, and the statistical content is hopefully presented in a manner that these domain scientists will find useful, it does require a good foundation in regression analysis using the GLM. Moreover, since the content is somewhat mathematical in nature, the reader will need a solid understanding of algebra for maximum benefit. The burden of calculation that typically accompanied statistical work in previous generations is now primarily carried out in a scientific computing environment. As Thisted & Velleman (1992) point out, “computational advances have changed the face of statistical practice by transforming what we do and by challenging how we think about scientific problems.” To support and help facilitate the use of scientific computing, examples using the R computer language will be used throughout this work.

The organization of content is consistent with the sequence this content is taught in EPsy 8252, the second of two applied statistics courses that form the foundational sequence for many graduate students in the educational and social sciences at the University of Minnesota. This course require that students have taken a previous statistics course at the graduate level focusing on the GLM.

Resources

This book refers to and uses several data sets throughout the text. Each of these data sets and their codebooks are available online at the book’s github repository, https://github.com/zief0002/adv-modeling/.

Acknowledgments

Many thanks to all the students in my courses who have been through previous iterations of this material. Your feedback has been invaluable, and you are the world’s greatest copyeditors. In particular, I would like to thank the following students who have gone above and beyond in the feedback they have provided: Jonathan Brown, Pablo Vivas Corrales, Amaniel Mrutu, Corissa Rohloff, and Mireya Smith.

Colophon

The book is typeset using Atkinson Hyperlegible. The color palette was generated using coolors.co.

Icon and note ideas and prototypes by Desirée De Leon.

Artwork

• Hedgehog banner by Alison Hill and Allison Horst, for RStudio.
• Data science and statistics artwork by @allison_horst

License

Advanced Modeling and Reproducibility for Educational Scientists by Andrew Zieffler is licensed under CC BY-NC-SA 4.0

References

Thisted, R. A., & Velleman, P. F. (1992). Computers and modern statistics. In D. C. Hoaglin & D. S. Moore (Eds.), Perspectives on contemporary statistics, MAA notes no. 21 (pp. 41–53). Mathematical Association of America.