Overview

Higher education researchers have a wide variety of quantitative tools at their disposal. Yet as the number and sophistication of these tools grows, so too do expectations about the quality of final analyses. Furthermore, increasing scrutiny of non-replicable results demands that researchers follow a proper workflow to mitigate errors. In this course, students will learn the fundamentals of a quantitative research workflow and apply these lessons using common open-source tools. We will cover project organization, data cleaning, and exploratory analyses as well as how to run basic econometric models and recover estimates for publication. Time and interest permitting, students will also cover some special coding and/or data gathering techniques. Throughout, students will use coding best-practices so that their workflow may be shared and easily reproduced.

In this course, students will learn:

1. To use a number of tools that are useful for conducting applied quantitative research, including:
   • R w/ RStudio
   • git w/ GitHub
   • R Markdown
2. To properly organize a project for maximum clarity and reproducibility
3. Best practices for cleaning/tidying/wrangling raw data into analysis-ready data