At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.
In a digital world full of ever-expanding datasets like these, it’s not always possible for humans to analyze such vast troves of information themselves. That’s why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.
Our latest video explainer – part of our Methods 101 series – explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about how we’ve used machine learning and other computational methods in our research, including the analysis mentioned in this video, you can explore recent reports from our Data Labs team.
[callout]Watch our other Methods 101 videos:
How can a survey of 1,000 people tell you what the whole U.S. thinks?
How do you write survey questions that accurately measure public opinion?
What are nonprobability surveys?
Phone vs. online surveys: Why do respondents’ answers sometimes differ by mode?
How is polling done around the world?
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