Does the thought of quantitative data analysis bring back the horrors of math classes? We get it.<\/p>\n
But conducting quantitative data analysis doesn’t have to be hard with the right tools. Want to learn how to turn raw numbers into actionable insights on how to improve your product?<\/p>\n
In this article, we explore what quantitative data analysis is, the difference between quantitative and qualitative data analysis<\/a>, and statistical methods you can apply to your data. We also walk you through the steps you can follow to analyze quantitative information, and how Userpilot<\/a> can help you streamline the product analytics<\/a> process. Let’s get started.<\/p>\n Quantitative data<\/a> analysis is about applying statistical analysis methods to define, summarize, and contextualize numerical data. In short, it’s about turning raw numbers and data into actionable insights.<\/p>\n The analysis will vary depending on the research questions<\/a> and the collected data (more on this below).<\/p>\n The main difference between these forms of analysis lies in the collected data. Quantitative data is numerical or easily quantifiable. For example, the answers to a customer satisfaction score<\/a> (CSAT) survey are quantitative since you can count the number of people who answered “very satisfied”.<\/p>\n Qualitative feedback<\/a>, on the other hand, analyzes information that requires interpretation. For instance, evaluating graphics, videos, text-based answers, or impressions.<\/p>\n Another difference between quantitative and qualitative analysis is the questions each seeks to answer. For instance, quantitative data analysis primarily answers what <\/em>happened, when<\/em> it happened, and where<\/em> it happened. However, qualitative data analysis answers why <\/em>and how<\/em> an event occurred.<\/p>\n Quantitative data analysis also looks into identifying patterns<\/a>, drivers, and metrics for different groups. However, qualitative analysis digs deeper into the sample dataset to understand underlying motivations and thinking processes.<\/p>\n <\/p>\nWhat is quantitative data analysis?<\/h2>\n
Quantitative vs qualitative data analysis<\/h2>\n