Introduction to Data Analysis and R: Start Here

Broadly speaking, there are two main types of data analysis: statistical and graphical (aka data visualization). They are closely intertwined, and many researchers use both types when presenting the results of their research. It's important to have an understanding of the foundations of data analysis so that you can gain a more thorough understanding of your (and others') research.

This module will provide an introduction to statistical data analysis from both a theoretical and practical angle, as well as an introduction to conducting several common statistical analyses in R. Data visualization is outside the scope of this module. 

In days past, researchers who needed to statistically analyze their data had to perform all the calculations by hand. Obviously, computers have made that task easier on us. There is a vast array of programs available that can help students and researchers analyze their data in virtually any way they need. However, without a basic grasp of the theory underlying many of the most prevalent analyses, it will be difficult to accurately interpret and present the results of your work. In that vein, you will also need a good foundational understanding of research design.

This module will cover:

• Hypothesis testing
• Variable types
• Choosing an appropriate analysis
• Common statistical analyses
• Working with data in RStudio

Based on feedback from previous students, this module is likely to take 4-6 hours to complete.