How online discussion boards informed our study of what ‘news’ means to Americans
We share the “why” and “how” behind our use of an online discussion board as a qualitative research method.
A behind-the-scenes blog about research methods at Pew Research Center.
For our latest findings, visit pewresearch.org.
Latest from Decoded
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A short intro to linear regression analysis using survey data
Regression is a statistical method that allows us to look at the relationship between two variables, while holding other factors equal.
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How Pew Research Center uses small multiple charts
Researchers and designers at Pew Research Center often create faceted information graphics, commonly known as “small multiples,” to…
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Comparing MRP to raking for online opt-in polls
In a recent study about online opt-in polling, Pew Research Center compared a variety of weighting approaches and found that complex…
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What voter files can tell us about trends in party registration
Commercial voter files hold great promise for studying certain aspects of American politics. These files, compiled by private vendors from…
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Learning about locations with Google APIs
Geocoding is the process of mapping an address or a location name to a point on Earth’s surface using latitude and longitude coordinates…
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Sharing the code we used to study the public’s interest in the Flint water crisis
In 2017, Pew Research Center published an analysis of Google search trends related to the water crisis in Flint, Michigan. The analysis…
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Q&A: What works and what doesn’t when studying social media data
Ever since Friendster and Myspace gained popularity in the early 2000s, social scientists have been interested in studying the impact of…
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Using supervised machine learning to quantify political rhetoric
Several posts on this blog have examined unsupervised methods of natural language processing. These algorithms and models can help…
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Validating 2016 voters in Pew Research Center’s survey data
(Related post: Validating 2020 voters in Pew Research Center’s survey data)
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Making sense of topic models
Topic models can produce clusters of words that characterize written documents. But how do we figure out what those clusters mean, exactly?
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