Introduction

*This image was created using napkin.ai; however, the concept, design direction, and creative vision were conceived by Dr. Knight and Chris Cardenas

Once data has been collected, researchers must organize, summarize, and interpret it in meaningful ways. This chapter introduces the essential steps of quantitative data analysis, helping you understand how to prepare data for analysis, describe patterns, and explore relationships between variables.

We begin by reviewing the types of variables used in quantitative research, focusing on the four levels of measurement: nominal, ordinal, interval, and ratio. Understanding whether a variable is categorical or continuous is crucial for selecting the appropriate type of analysis later in the research process.

Next, the chapter walks through data preparation, including how researchers use a codebook, code and clean their data, and prepare it for analysis. You’ll learn about basic descriptive statistics and how these help identify patterns within your dataset.

The chapter also introduces data visualization tools that allow researchers to present findings clearly and effectively. Graphs can reveal trends that might be difficult to spot in raw data alone.

Finally, we’ll explore how to examine correlations between quantitative variables. You’ll learn how to read scatterplots and understand positive and negative correlations, setting the stage for more advanced statistical techniques you might explore in further classes or research experiences.

* AI was used to help organize my thoughts and suggest clarifying sentences, but all ideas and final writing are entirely my own.