Big sample, big impact...right? It's not so simple...Large samples don't automatically produce more valid, useful outcomes. Survey design, sample representativeness, participant incentive structures, and analysis plan all impact the results. What can mixed-method, qual-leaning researchers learn from this fact?On this episode, we're joined by Dr. Peter K. Enns, a professor of Government and Public Policy at Cornell University (where he also serves as Director for the Center for Public Opinion and the Center for Social Sciences). Dr. Enns spends a lot of his time thinking about the impact of his conclusions, because of their political, material, and policy implications. In addition to his work at Cornell, he is a cofounder of Verasight, a consumer insights firm.He outlines the ways we can collect more representative data that's also less likely to produce spurious conclusions. Experience pros will leave a sharper sense of data hygiene and ways to foster a relationship with the users who make their practices possible. Show Notes:Dr. Enns' work, including his books Hijacking the Agenda and Representation NationDr. Katherine Cramer discusses listening in her political science research