Introduction

Quantitative data analysis is a critical skill to develop as an experience researcher. As more data is readily available (or easier to collect), you need to be able to clean, structure, visualize, summarize, and interpret quantitative data.

This Handbook has two main topics: extracting value from your quantitative data using descriptive statistics and visualizations and exposing you to more complex techniques so you can learn about and use them in the future.

Sadly, you can’t get good at this kind of analysis without investing real time and practice. There are resources listed throughout this Handbook). Don’t expect to directly copy/paste what you read here or online, apply to your data, and expect to get meaningful quantitative results. You might use the same tests or approaches across quantitative studies, but every time, you’ll have to contextualize your approach and interpret your data accordingly. Remember, numbers without context are meaningless (jump to this Topic for more).

The focus on this Handbook is on exploring, describing, and displaying your quantitative data. When you have short study timelines or are new to quantitative research, descriptions and displays might be all you’re able to do. But you’ll be surprised how much value you can extract from your quantitative data just by describing and displaying your data.

And briefly, the topic of statistical software. Tools like R, Python, or Matlab make it easy to work with and manipulate your quantitative data. However, statistical programming is more complex than covered here, but helpful resources are listed at the end of the chapter.

This Handbook focuses on quantitative analysis that can mostly be done with spreadsheeting tools (like Microsoft Excel or Google Sheets) and other free, public online tools. The purpose is to make the ideas accessible, not hide them behind complex or expensive software.

With that out of the way, let’s start by discussing the first part of the quantitative data analysis process: exploring and describing your quantitative data.