Using fixed and random effects models for panel data in Python
Identifying causal relationships from observational data is not easy. Still, researchers are often interested in examining the effects of policy changes.
A behind-the-scenes blog about research methods at Pew Research Center.
For our latest findings, visit pewresearch.org.
Identifying causal relationships from observational data is not easy. Still, researchers are often interested in examining the effects of policy changes.
From weather events to snap elections, outside developments can sometimes disrupt surveys while they are in the field.
Overall, our survey found that 13% of U.S. adult Twitter users keep their feeds private.
We’ve been asking Americans about their online news habits since the mid-1990s. Since then, the ways people get news online have changed a lot — and so have the ways we ask about it.
I wrote an introductory blog post about how to access and analyze Pew Research Center survey data with R, a free, open-source software for statistical analysis. The post showed how to perform tasks using the survey package.
From time to time, data collected through surveys doesn’t match sources that are widely acknowledged as accurate.
Understanding how the numeric labels on scales might influence survey responses is an area of ongoing investigation for researchers.
Regression is a statistical method that allows us to look at the relationship between two variables, while holding other factors equal.
In a recent study about online opt-in polling, Pew Research Center compared a variety of weighting approaches and found that complex…
(Related post: Validating 2020 voters in Pew Research Center’s survey data)