Special Series on Methods Pedagogy
During the Spring semester of 2023, MCAP developed a Special Series on Methods Pedagogy. The series is supported by a Teaching Leadership Award from the Purdue Teaching Academy along with the Center for Instructional Excellence (CIE). The goal of the series is to open a conversation across campus for instructors and mentors to share wins and failures, and gain new strategies for effectively teaching methods both in courses and in one-on-one mentoring or consulting arrangements (see the flyer of all series events).
Below are some of the products of this special series that serve as resources for improving methods instruction at Purdue.
Workshop: How do you teach a short workshop on methods? Tips, tricks, and other advice from experience
Dr. Trent Mize, Associate Professor of Sociology and Co-Director of MCAP (February 2023, slides)
Survey results: A graduate student perspective on methods instruction: Feedback from a survey of graduate students
Dr. Kritine Marceau, Associate Professor of HDFS and Co-Director of MCAP (March 2023)
See the key results of the graduate student survey
How common is opinion change? Evidence from a 17-wave panel dataset
Dr. Stephen Vaisey, Professor of Sociology and Political Science, Duke University; Director, Worldview Lab; Director, Code Horizons; Instructor, Statistical Horizons (April 2023)
Bio:
Dr. Vaisey is a Professor of Sociology and Political Science and Director of the Worldview Lab at Duke University. The main goal of Dr. Vaisey's research is to understand moral and political beliefs: what they are, where they come from, and what they do. Dr. Vaisey pursues these questions in the broader interdisciplinary context of cultural evolution. Dr. Vaisey also does a lot with applied statistics and has been working on how to use simple patterns in repeated cross-section and panel data to help adjudicate between competing theories of cultural change and socialization. Dr. Vaisey teaches several short courses on applied statistics through Statistical Horizons and directs their Code Horizons initiative.
Abstract:
In recent years, cultural sociologists have developed methods for modeling individual change using panel data. These methods are able to distinguish persistent change from temporary change. Analyses using these methods are generally consistent with the position that most cultural change comes through cohort replacement rather than people changing their minds. One limitation of these analyses, however, is that they rely on "short panels" -- repeated measures of only 3 or 4 waves. In this paper, I use a longitudinal dataset with 17 waves collected two weeks apart in the United States in 2020 to model the extent of individual-level changes in beliefs and opinions. The results show that although some measures are changing at the individual level (e.g., views on gay adoption) most core political beliefs (e.g., party identification, left-right identity, abortion views) are stable within persons. I discuss the implications of these findings for models of cultural change at the macro level.
Teaching research design and methods: Emphasizing critical thinking over data
Dr. Anthony Folwer, Professor in the Harris School of Public Policy, University of Chicago (March 2023)
Bio:
Dr. Anthony Fowler is a Professor in the Harris School of Public Policy at the University of Chicago. His research applies econometric methods for causal inference to questions in political science, with particular emphasis on elections and political representation. Specific interests include unequal political participation, electoral selection and incentives, political polarization, and the credibility of empirical research. He is an editor-in-chief of the Quarterly Journal of Political Science, an author of Thinking Clearly with Data, and a host of Not Another Politics Podcast.
Abstract:
Quantitative methods are often taught in a way that prevents students from seeing the forest for the trees. In this talk, I will discuss an alternative approach that emphasizes clear thinking over technicality. I will discuss my co-authored book, Thinking Clearly with Data, and my experiences teaching undergraduates, graduate students, and working professionals. After providing an overview of the book, I will discuss Chapter 7 of the book, which covers the important but often underappreciated problem of publication bias.