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Data sources: https://statewidedatabase.org/d20/g22.html Shapefiles: https://statewidedatabase.org/d20/g22_geo_conv.html CD boundaries: https://sfelections.sfgov.org/sites/default/files/Documents/Maps/J22_NewCongressionalDistrictMap.pdf
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I’ve made the replication code for this blog post publically available, here as a GitHub repository.
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In survey research, the composition of a sample may differ notably from the population being modeled across important characteristics (e.g. race, age, education, party identification). These sampling errors often reflect systematic bias which can pose a threat to accuracy and the researcher’s ability to make inferences using the data, especially if the error is correlated with the variables of interest.
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A recent working paper by Anthony Fowler and William Howell finds that partisans update their policy beliefs in response to both in- and out-party elite cues. I found the paper especially notable for a few reasons: its findings are well-supported yet somewhat incongrous with both previous research and the oft-cited1 expression that “partisanship is a hell of a drug”.2 Given the paper’s novelty and relevance to my own research interests, I ran an extension and replication in May 2021. Do partisans update their policy beliefs in response to elite cues from both in-party and out-party leaders?
See, for example, its frequent use by political scientists on Twitter. ↩
One which seems to have been created and popularized by Brendan Nyhan and Stephen Miller. ↩
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Recently, CES Researcher Pia Deshpande wrote an excellent tutorial detailing how to plot trends over time using CES data — I highly encourage anyone who hasn’t yet read the piece to promptly do so! This style of plot pairs particularly well with the cumulative CES Common Content dataset created by Shiro Kuriwaki. Together, these resources can help us to understand an issue that has received increased attention in recent months: educational polarization.
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With the recent release of the final 2020 Cooperative Election Study (CES) dataset, which includes vote validation, I returned to an earlier project examining youth voter turnout. In looking at voter turnout among adults under the age of 30, I noticed a pronounced gap between estimates from the Center for Information & Research on Civic Learning and Engagement (CIRCLE) and the CES.
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July 1 marked 50 years since the ratification of the 26th Amendment to the United States Constitution, which legally extended the right to vote to those over the age of 18. Young voters have played a vital – at times, even decisive – role in elections since. But decades after the franchise was extended to 18-, 19-, and 20- year-olds, how does their voting rates compare to American adults overall?
{survey} Package in R Published:
Survey research commonly relies on weights to reduce bias and produce a representative sample for a given population of interest. Weighted survey data produces a value assigned to each observation in the data that increases or decreases that observation’s influence (or weight) when performing statistical operations using the data.
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The blossoming use of survey research in political science heightens the need for rigorous investigation into data quality. To obtain samples, academics often rely on survey vendors such as SurveyMonkey, Amazon’s Mechanical Turk (MTurk), and Lucid Theorem. Use of data from Lucid has become increasingly prevalent, and the focus of growing data quality concerns.