Introduction to Analysis of Public Survey Data

Lecture to introduce complex sampling

Fundamentally, a complex sample survey aims to save money on the transportation costs of its interviewers by sampling geographies first and then people (or businesses or structures) within the geographies. So instead of sampling individuals nationwide, a survey administrator samples twenty towns and cities across the country, and then within those geographic areas, again samples multiple individuals. Nationwide, everyone still has the same probability of being sampled, but once the first stage of sampling occurs - when geographies are sampled - then suddenly the residents of those sampled geographies have a much higher probability of inclusion and everyone else's inclusion probability goes to zero. But now, instead of sending an in-person survey team to ten thousand different interviews across the country, they'll only need to travel to twenty. Suddenly, the survey interviewer transportation budget looks much nicer.

Discussion of experience with datasets

Course participants will discuss any publication history or experience using any publicly available dataset, and what research questions they have answered (or would like to answer) with any publicly available dataset.

Review of one http://asdfree.com/ entry

Each dataset presented on http://asdfree.com/ includes three major components: 1. Download automation or data acquisition; 2. Helpfully-noted analysis examples; 3. Replication of published estimates to prove correct methodology. We will walk through each of these segments for one dataset, with participants testing out the same R code on their local laptops. Given the high similarity of the structure of each dataset, participants will ideally quickly understand that once they are able to get started using any of these entries, it's straightforward to apply the same knowledge to *all* of these entries.