Survey Data Collection and Analytics
Description
This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data.
Faculty for this specialisation comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection firm Westat, founded by the National Science Foundation and the Interagency Consortium of Statistical Policy in the U.S. to educate the next generation of survey researchers, survey statisticians, and survey methodologists. In addition to this specialization we offer short courses, a summer school, certificates, master degrees as well as PhD programs.
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Subjects
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Language
English
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Duration
20 weeks
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Status
Available
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U-M Credit Eligible
No
Instructors
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Frederick Conrad
Research Professor, Survey Methodology
Institute for Social Research
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Frauke Kreuter
Professor, Joint Program in Survey Methodology
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James Lepkowski
Research Professor
Courses (4)
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Learn moreData Collection: Online, Telephone and Face-to-face
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4 weeks
Explore survey design methods, data collection modes, and their effects on data quality, bias, and error tradeoffs. -
Learn moreQuestionnaire Design for Social Surveys
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6 weeks
Learn to design effective questionnaires, evaluate question formats, and adapt survey design to behavioral and contextual challenges. -
Learn moreSampling People, Networks and Records
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6 weeks
Explore probability-based sampling methods for people, records, and networks to ensure accurate, cost-effective data collection. -
Learn moreSurvey Data Collection and Analytics Project (Capstone)
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4 weeks
Apply your skills in a capstone project by developing a research question, analyzing multiple data sources, and evaluating data quality.
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