Overview of Survey and Data Science

The Profession

Since the 19th century, survey research has been used as a tool to describe and understand society. Surveys generally ask individuals questions about themselves and what they think, using their answers as the data on which these descriptions are based. More recently other sources of data have become available that may help researchers understand society, in particular data that are generated in massive quantities, such as sensor data, online transactions, search strings and social media posts. Survey methodologists use and develop techniques to collect and interpret these different kinds of data.

Throughout the world today, specialists in survey methods are found in academia, government, and commerce. Survey researchers in the academic sector investigate discipline-based questions in fields such as sociology, psychology, political science, public health, communication studies, criminology, economics, and gerontology. U.S. government agencies on health, education, justice, transportation, and labor statistics, as well as the Bureau of the Census, collect and disseminate government survey information. The private sector includes research firms devoted to the measurement of media audiences, user experience, political and other opinion research, market and product research, and customer satisfaction.

The Discipline

Survey methodology is the study of sources of error in surveys--the bias and variability that affect the quality of survey data. As a field of knowledge, a profession, and a science, survey methodology seeks to link the principles of design, collection, processing, and analysis of surveys to an understanding of error.

Achieving high quality survey results within the scientific aspects of surveys requires applying principles from academic disciplines such as statistics, the social sciences, and data science. For example, statistics provides a quantitative foundation to examine sources of error and to summarize their effects. Social and cognitive psychology provides the framework for understanding how human behavior affects accuracy in survey responses. Sociology and anthropology offer theories of social stratification and cultural diversity. Computer science provides principles of database design and human-computer interaction, as well as computational techniques such as machine learning. Because these disciplines all contribute to the foundation of survey and data science, it is an inherently multidisciplinary, dynamic field of study.

The Challenge

Every survey involves a number of decisions about its design and implementation, and each decision has the potential to affect the quality and validity of the results. How will the sample be chosen? What mode will be used to pose questions and collect answers from respondents? All surveys involve compromises, and the challenge for the researcher is to determine how best to use the available resources to produce, on balance, the best results. The Michigan Program in Survey and Data Science prepares students to meet this challenge.