We'll start by getting to know one another, identifying a theme for our coursework, and completing the CITI research ethics certification.

Survey sampling and implementation are two pillars of scientific design that can make or break your data. We'll review what sampling is, why we do it, and how. We'll also consider the trade-offs of differing delivery modes.

Creating a user-friendly questionnaire is important for minimizing survey error. We'll review key considerations for document layout and question characteristics, and review three common variable types.

One of the first steps in transforming your data from mess to manageable is examining the shape of the response distribution on each variable. We'll review methods for cleaning your data and preparing it for analysis, as well as describing the center and shape of each variable's distribution.

Often we want to be able to compare similarities and differences between two groups of survey respondents. We'll review how to examine relationships between two variables in your dataset and their statistical significance. 

If a picture is worth a thousand words, then a data visualization is worth a thousand numbers. Data visualizations, however, can be hard to interpret, inaccessible, or - at worst - totally misleading. We'll practice making visualizations, identifying the appropriate visualizations for different data, and reviewing principles of data visualization that ensure all readers can correctly interpret your visualization. 

At last, it's time to tell your data's story. Organize and focus your findings by creating an outline, then drafting and revising to create an ADA-compliant research brief or report.