The Quantitative Methods in the Social Science (QMSS) program in the College of Literature, Science, and the Arts at the University of Michigan aims to train undergraduate students in the theories and methods needed to be successful data literate social scientists. Todays job market is saturated with opportunities that either desire or require skills in data literacy; whether that means being able to find data, analyze data, or know how and when to use data. This is true even for jobs outside of the data science or analyst fields specifically.
QMSS was designed to teach students how data can be used to generate solutions for social problems of today and tomorrow and give students opportunities to apply and practice their skills to hit the ground running in their internships and careers in the future. QMSS is unique relative to programs in statistics or data science in that we teach data-based skills from a social science perspective.
The Quantitative Methods in the Social Sciences (QMSS) program seeks applicants for a part-time Lecturer I position with an anticipated start date of August 26, 2024. This is a non-tenure track position with an appointment period for the Fall 2024 term (i.e., August 26, 2024 to December 31, 2024).
QMSS is seeking part-time lecturers for either 33% or 67% appointment efforts to serve as section instructors of the laboratory sections for QMSS 201: Introduction to Quantitative Methods in the Social Sciences. A 33% appointment requires teaching 2 laboratory sections per week, and a 67% appointment requires teaching 4 laboratory sections per week, each under the guidance of a faculty lecturer who serves as the instructor of record for the course.
QMSS 201 includes training in descriptive statistics, data collection, data management, and data cleaning. It provides an overview of research design and hands-on experience with using data to ask and answer research questions, and it educates students about ethical issues around data, data analysis, and reporting. Students will be taught and asked to use Excel, Tableau, and R in this course.