Survey methodology combines knowledge from several different fields of study. These fields include sociology, psychology, statistics, and data science. All students are required to take courses from each of these areas. Students will also specialize in either Survey Methods, Survey Statistics, or Data Science. Students will supplement this work with two courses in a cognate field, a Rackham Graduate School requirement. The specific courses a student will take are determined through discussion with the student's faculty adviser. Flexibility in cognate election is allowed, although each Masters degree candidate must complete specific required courses. In addition, students will participate in practical training which includes coursework and a summer internship.
All students must have satisfactory background, in statistical methods. For students not taking the Statistics track, there is a required three-course sequence taught by MPSM faculty and tailored to the skills required for a career in survey methodology. In addition, all students will gain familiarity with principles of Data Science from a required course in computing and data display that focuses on machine learning.
Practical training is an important component of the Masters degree. During the first year, students will complete Fundamentals of Data Collection I and II, which includes several practical, data-generating activities that will be used by the student in the following year. In the summer after the first year, students will complete an internship. Students must also complete the Survey Design Seminar (SurvMeth 670) during the second year.
The largest share of the Master's student program will consist of survey methodology courses. All students are expected to take courses in fundamentals of data collection, inference, statistical modeling, computing and data display, and total data quality. Students emphasizing social science aspects will need to choose five electives on topics such as questionnaire design, cognitive and social foundations of survey measurement, economic measurement, and survey management. Students emphasizing statistical aspects typically elect three courses on topics such as sampling, small area estimation, and Bayesian analysis. Students specializing in data science will need to take five electives on topics such as machine learning, database management, and record linkage.
Finally, all Master's degree students must elect four credits of coursework in a cognate area. For those with interests in employment in the private sector, courses in market research are strongly recommended. Those with interests in academic or government employment may elect courses in psychology (particularly cognitive psychology and memory), sociology (including courses in qualitative methods of measurement, social psychology, and demography), political science, economics, anthropology, computer science, epidemiology, health management and policy, public policy, or information sciences. Students should consult with their academic advisers about other cognate areas of interest to individual students. The three areas of academic concentration are:
The statistical science area of concentration is designed for students who wish to specialize in areas such as sample design, estimation in complex samples, variance estimation, statistical measurement error models, and statistical adjustments for missing data.
The social science area of concentration is designed for students who wish to specialize in areas such as questionnaire design, design of interviewing systems, computer assistance in data collection, effects of mode of data collection, cognitive psychological insights into survey measurement, and efforts to reduce various nonsampling errors in data collection.
The data science area of concentration is designed for students who wish to specialize in the more computational aspects of survey methodology and research involving "big data," including data visualization, management and analysis of large and messy data sets, human-computer interaction in survey research, and machine learning algorithms.
Application deadline for the Master's program is January 1st for study beginning the following fall term.