Balancing Theory with Practice: How to Develop
Successful Industry Research Practitioners
While industry researchers
need practical skills, survey training often involves a balance of theory and
practice. Training that focuses too heavily on practical applications may skip
over foundational concepts, such as sampling theory, error reduction, or bias
minimization. Without these foundations, researchers might design surveys that
overlook important methodological considerations, potentially compromising data
quality in ways that negatively affect insights and decision-making. At the
same time, strong training programs often teach sophisticated survey methods
(e.g., stratified sampling, regression analysis, psychometric scaling) that are
used in academic or governmental research, but are impractical or overly
complex in most industry contexts without adaptation to the time and cost
constraints often present. A middle ground of training is often missing--one
that trains researchers on how to adapt or simplify these more complex
methodologies for practical use in the real world and how to make them
accessible without sacrificing quality. The presenter will discuss the holes
often seen in hiring trained survey researchers and the complimentary development
that is necessary to bring them up to speed to be successful industry
practitioners.
Curtiss Cobb
is a Vice President of Research at Meta where he leads the Demography and
Survey Science Team, a quantitative focused research team that works across
Meta to identify and share best practices and methodological innovations in
demographic and survey research. His
team oversees the collection of millions of survey responses a day from around
the world using mobile, web, face-to-face and other methods. Prior to Meta, Curtiss was a senior director
of survey methodology at GfK and has independently served on advisory panels or
consulted for the U.S. State Department, CDC, Associated Press, World Health
Organization, OECD and various academic studies. He holds a PhD in Sociology from Stanford
University