Total Survey Error: A Framework for High Quality Survey Design

a Workshop

Brady T. West
Paul Schulz (U-M, Population Studies Center)

Tuesday, 10/23/2018, 1:00pm to 5:00pm.   ARCHIVED EVENT

Location: 426 Thompson St, Ann Arbor, MI 48104

Instructors Brady T. West and Paul Schulz are kicking off the new PDHP workshop series with an overview of the Total Survey Error framework and its implications for survey research.  This half-day workshop is geared toward survey researchers of all types and experience levels, and will cover the design, implementation, and monitoring of survey data collections using the TSE paradigm as a guiding set of principles.  The workshop will use a mix of conceptual discussions and team exercises to explore both the underlying theory and real world applications of the TSE paradigm in survey research.

Topics include:

• Sources of survey error
• Quantifying and evaluating TSE in a data collection
• Implications of TSE for study design
• TSE reduction strategies
• Linking TSE and Responsive / Adaptive Survey Design



Dr. West's current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. He is the lead author of the book Linear Mixed Models: A Practical Guide Using Statistical Software and co-author of the book Applied Survey Data Analysis.

Paul Schulz is a consulting statistician and data scientist who joined the PDHP in April 2018. Paul began his career at PSC as a research associate in 1999 and later worked as a statistician for SRO's Statistics & Methods unit, where he gained a wealth of experience in sample design and survey weighting, high performance computing, and data visualization and dashboarding. Paul's role is to provide PDHP faculty affiliates with high-level technical support and consultation on statistical methods and techniques, data (acquisition, management, and analysis), and computing (statistical software, machine learning techniques, and high performance computing environments and algorithms).

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