About the workshop

This workshop will introduce R and R Studio in a non-technical way. We will go through some useful techniques for working in R; the code provided can later be used on the participants' own data. By the end of this session, participants will be able to use R to create graphics and obtain descriptive statistics from their data.

The aim of this session is twofold: (i) to introduce and discuss the importance of descriptive statistics and visualization for getting to know your data, and (ii) to help participants further familiarize themselves with R.

This session will provide an introduction to creating graphs in R. Participants will become familiarized with base R graphs and the R package ggplot2 and learn how to use these tools for creating elegant customized graphics.

Participants will learn about the relationship between sound sampling practices and the generalizability of quantitative findings. We will introduce how to define a target population, develop an appropriate sampling frame, determine appropriate sample size, and incorporate sample design principles such as randomization, stratification, and proportionality.

We will introduce a range of research design considerations, including observational versus experimental designs, hypothesis testing, observational units, variable types and scales, and aligning research questions with appropriate statistical techniques.

We will provide a practical guide for researchers interested in moving beyond the ritual of simply rejecting (or failing to reject) the null hypothesis using p-values.

Taking both a conceptual and hands-on approach, we will focus on calculating and interpreting effect sizes and confidence intervals, both of which are critical tools for understanding effects/relationships in research.