LSA Course Guide Search Results: UG, Fall 2020, Subject = QMSS

Courses in Quantitative Methods in the Social Sciences

Because humans are complex, the questions that are asked and the data that are gathered are also complex. For decades social scientists have gathered data by using surveys, interviews, observations, and experiments. The resulting data reflected choices within the control of the researcher. Now, data are increasingly being generated without any researcher intervention. Our world is full of data coming from multiple types of devices, interaction with applications on these devices, digitized documents, and much more. The Quantitative Methods in the Social Sciences (QMSS) program seeks to unite Michigan’s excellence in social science with the current revolution in data science.

The QMSS minor will train the next generation of social scientists in the methods needed to harness all types of quantitative data in order to generate new insights and solutions to the problems of today and tomorrow. QMSS courses will deepen the knowledge and skills for a broadly diverse group of students to work in the rapidly changing environment of the analysis of human data. Existing programs focus on computational methods, big data, data management etc. While QMSS is acutely aware of the need for skills in those domains, the foundational courses focus on a) social science applications (understanding human behavior and beliefs through data analysis, solving societal problems) and b) the particular strengths and needs of students in the social sciences, who often select a topic area (inequality) or discipline (psychology) rather than a methodology or a particular mathematical skillset when they select their major.

Further, students interested in social science research are often asked to take long lists of prerequisites with content they won't need before they can take the "useful" courses that exist in computer science and related disciplines. To teach these methods for social science students, QMSS has flipped the curriculum so that students are learning material relevant to them and this material is grounded in / connected to their interests. This leads to increased engagement with the curriculum and a strong understanding of its application to the real world - and their future job prospects. Likewise, the QMSS minor will benefit students with advanced technical and mathematical skills who are interested in majors in the social sciences and need courses like these to help them reason through the application of their tools to social questions.

The QMSS program is investing heavily in resources to help students achieve success. Program faculty are dedicated to providing individualized assistance to declared minors and students enrolled in the program's core courses with both course concepts and student research projects - in the capstone course as well as self-directed research endeavors.

Advising: Advising Appointments will be available online through the LSA Advising Appointment System beginning in Fall 2020. Information about minor requirements and fall course offerings are available on the QMSS website ( The core courses for the QMSS minor do not require any prerequisite courses. The foundational courses will introduce students to quantitative methods in data analysis and are intentionally designed so that they are accessible to students who have not yet taken college-level statistics courses, and/or have not completed the requirements for their own social science major/minor. This minor is designed to complement, not replace, statistical and methodological training in each of the social science disciplines. The capstone course currently requires senior status, as well as declaration of both a QMSS minor and a major in a social science department (the social science major requirement is currently under review by the program). If you are interested in QMSS courses or in the program as a minor, please email and include a copy of your informal transcript to be connected with advising assistance.

  Page 1 of 1, Results 1 - 2 of 2  
Class Instruction Mode
  Page 1 of 1, Results 1 - 2 of 2