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Replication Data for: Field Evidence of the Effects of Pro-sociality and Transparency on COVID-19 App Attractiveness

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Field Evidence of the Effects of Pro-sociality and Transparency on COVID-19 App Attractiveness
 
Identifier https://doi.org/10.7910/DVN/OT36PX
 
Creator Dooley, Samuel
Turjeman, Dana
Dickerson, John P
Redmiles, Elissa
 
Publisher Harvard Dataverse
 
Description These data and associated R analysis file are associated with the paper: "Field Evidence of the Effects of Pro-sociality and Transparency on COVID-19 App Attractiveness"




We ran 14 separate Google display ad campaigns from February 1 to 26. These were the only Google Display ads run for CovidDefense. Each campaign was targeted at people who reside in Louisiana via IP address. All campaigns used the same settings, ad destination, and ad image from the state of Louisiana's CovidDefense marketing materials. The 14 ads varied only in their text data in alignment with the 14 conditions summarized in this file (ads.csv).




There are two primary datasets: one (data_demo.csv) which has all 7,010,271 impressions and demographic data, and another (data_geo.csv) with just the impressions that have associated geographic information. The former includes columns for Google-estimated demographics like Age and Gender, with many impressions having values of ``Unknown''.




These two data tables for demographic and geographic impressions were represented by a row for each impression with columns for whether that impression resulted in a click; the age and gender or geography of the impression; as well as indicator variables for the presence or absence of ad information (appeals, privacy transparency -- broad privacy reassurance, non-technical control, and technical control -- and data transparency).




An associated R file is included which includes functions to reproduce each model and associated statistics.
 
Subject Computer and Information Science
Social Sciences
 
Contributor Dooley, Samuel