Written by Connor Zahler:
Intro to Quantitative Methods in the Social Sciences is the first course in the QMSS curriculum. Although this four-credit course is intended for students wanting to enroll in the minor, the class is open to anyone who wants to learn about the program or about how big data applies to fields like economics, psychology, and sociology. This spring, we enrolled in the first half-semester offering of QMSS 201. We would like to tell you about our experiences with this 7-week, online version of a course that is usually offered in a standard 16-week, in-person format.
First, full disclosure: we were both involved in the minor program prior to taking the class. Suzie is the editor and Connor is a writer for the student-run QMSS blog. We also both knew Dr. Beth Ann Whitaker previously, who taught the course this past spring. Regardless, we have strived to be completely transparent about our feelings about the course and not soften any criticisms. With that said, let’s talk about the course format and our thoughts about QMSS!
Because of the pandemic, the course was offered completely remotely, abiding by the guidelines set by the university. We met synchronously twice a week on Zoom for lecture, and lab material was covered asynchronously, usually with an additional content-based video in addition with a video explaining the homework. Assignments were usually some sort of data cleaning, formatting, or analyzing activity done in one of the platforms we were learning (R, Excel, Tableau) or a write-up on the use of data in a relevant article or video. There were also a few quizzes. The two biggest assignments were a midterm test and a final data presentation project, which together accounted for half of our grade. There was a lot of content to get through in a short time, but both of our instructors were responsive enough to make it work.
Both Dr. Whitaker and Susan Parker, our G.S.I., were accessible and understanding throughout the term. There was never any issue with scheduling an office hours appointment and getting help with an assignment. At one point, Connor did the wrong assignment (one due Monday vs. one due Friday) and had to submit the other late. Susan understood completely and still gave him full credit. Throughout the entire semester, it was clear that help was easily available to those who sought after it.
The assignments were interesting and taught the skills discussed in lecture well. Students gained experience in using popular programs like Excel and R to clean and analyze data. Quizzes weren’t incredibly difficult, but they picked out important information from the lectures and outside content that was helpful. The final project, a presentation about how attitudes about climate change vary across different states, was a great way to use all the skills acquired throughout the semester. It gave students a lot of choice in how to run and present descriptive data: pie charts, bar graphs, geographic distribution maps, and even more. Our only criticism is that the asynchronous videos explaining the homework could make the actual assignments a bit too easy. It’s one thing to be able to follow along, but allowing students to think more for themselves could have made the skills remain for longer. For example, in one of Suzie’s statistics courses, the supplementary content explained the skills we would need to complete the assignment, using a different data set. Regardless, the assignments taught useful skills and paired well with the rest of the content.
The content of the course was accessible to beginners and a nice review for those with experience. Dr. Whitaker covered introductory elements of statistics, like the theoretical underpinnings of the different tests and how they apply to real life, hitting on essential concepts like the Central Limit Theorem, hypothesis tests, and elements of probability. The class also covered practical concepts like data cleaning and visualization, as well as advanced statistical concepts like factor analysis and regression software. While eight weeks is a relatively short time, the course still covered an incredible amount of information without making it feel rushed. For students with experience in statistics or computer science, we believe this course will not be too challenging. For those with no understanding, this course will give you a strong foundation for harder courses.
The online format was inferior to an in-person format, but this is to be expected with most classes. It would have been great to do the lab activities with a teacher or GSI in the room, but the videos made up for this somewhat. It was hard to feel a connection with other students with the shorter time frame and all-online instruction, but again, this is typical of other spring- term and online courses. QMSS 201 doesn’t suffer uniquely from being online. Luckily, future iterations of the class will be in-person, so these problems won’t be a consideration.
Taking QMSS 201 in the spring was a positive experience for both of us. It taught us relevant concepts for additional classes in statistics, gave us useful skills for any data-based job, and made us excited to take more QMSS-related classes. We think the shortened semester makes this class best for people with some, but not a lot, of statistics and data experience. While students with none can still do well, the pace may be challenging. We would recommend this class for any students who have some free time during the spring or summer and want to learn more about what QMSS can offer.