Quantitative Political Science Education in the Past and Future

Abstract

AbstractThere has been a massive shift in teaching quantitative political research since the Journal of Political Science Education was launched in 2004. Smartphones were an anomaly, and it was uncommon to have laptops in the classroom. Statistical calculations were sometimes done by “statisticians”, i.e., professional staff who did calculations for faculty members. Today, it is rare to see students without electronics. Through that transition we experienced ubiquitous Wi-Fi and smartphones, statistical computing on personal computers, the end of the academic staff statistician, an explosion in open-source statistical software and tutorials, and an unexpected mass transition to online learning during COVID. We experienced a similar revolution in teaching statistics. Increases in computational power and data availability make quantitative and qualitative research different than 20 years ago. Computation is rarely a limiting factor, and we find ourselves spending more time on statistical assumptions, correct methods, data integrity, and replicability. We are now entering an era of assistive technology and will need to transition to teaching students how to use artificial intelligence tools to assist them with quantitative work. In this article, we consider these changes and what they mean for teaching political science in the next 20 years.Keywords: Methods pedagogypolitical science educationquantitative political analysis Disclosure statementThe authors report there are no competing interests to declare.Notes1 NVivo is a qualitative analysis software that allows for document collection, organization, coding, and analysis (https://lumivero.com/products/nvivo/).2 A website where users post coding problems that are answered by other users or package developers (https://stats.stackexchange.com/). See also Stackoverflow (https://stackoverflow.com/).3 Applications like Nearpod, Mentimeter, and Echo360 offer students and instructors features to help integrate traditional presentation slides with interactive activities. They expand substantially upon older iClicker student response systems that allowed for on-the-spot multiple-choice and true-false questions during lectures (Baumann, Marchetti, and Soltoff Citation2015). For example, Nearpod has posterboards that allow students to post notes in response to an instructor’s prompts.4 https://blogs.sas.com/content/sgf/2014/10/08/configuring-sas-what-to-know-before-you-install/.5 R is an open-source statistical computing software (https://cran.r-project.org/).6 Python is a programming language. In addition to other programming, it can be used to conduct statistics (https://docs.python.org/release/2.0/).7 https://www.tiobe.com/tiobe-index/8 An aside that becomes extremely important later, in 2007, Apple released the iPhone and “iOS” and Google followed shortly after with Android. This had almost no impact on the classroom at the time, but fast forward to 2023, and students constantly attempt to use these devices for coursework with great frustration.9 See https://doesitarm.com/app/rstudio, https://learn.microsoft.com/en-us/surface/surface-arm-faq10 https://community.canvaslms.com/docs/DOC-10720-67952720329.11 RStudio is an integrated development environment where users can develop and compile R and Python code (https://posit.co/download/rstudio-desktop/). Quarto is an open-source platform for scientific writing using R, Python, Julia, and Observable (https://quarto.org/).12 Jupyter Notebooks is a cloud-based computing flatform (https://jupyter.org/).13 Google Colaboratory is also a cloud-based notebook (https://colab.research.google.com/).14 Posit Cloud is a web-based platform for collaborative use of RStudio (https://posit.co/download/rstudio-server/).15 https://git-scm.com/16 Github is a cloud-based platform where developers share computer code (https://github.com/).17 https://openai.com/blog/chatgpt18 https://us-rse.org/Additional informationNotes on contributorsEric BestEric Best is an Assistant Professor of Emergency Management and Homeland Security and a faculty affiliate of the Institute of Artificial Intelligence at the University at Albany. His research interests include data collection and analysis from mobile sensors to allow for rapid decision-making in the built environment. Eric teaches quantitative research methods and research software design courses.Daniel J. MallinsonDaniel J. Mallinson is an Associate Professor of Public Policy and Administration at Penn State Harrisburg. His research interests include policy process theory (particularly policy diffusion and punctuated equilibrium theory), cannabis policy, energy policy, and the science of teaching and learning.