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Replication Data for: Increasing Uptake of Social Distancing during COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions
Oct 26, 2021
Charles, Grace, 2021, "Replication Data for: Increasing Uptake of Social Distancing during COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions", https://doi.org/10.7910/DVN/RBZONT, Harvard Dataverse, V1, UNF:6:i9gVTs4DWtjUHZ0Z2BQfag== [fileUNF]
Data and code for replication of the Harvard Data Science Review paper " Increasing Uptake of Social Distancing during COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions"
Codebook 4_3.tab
Oct 26, 2021 - Replication Data for: Increasing Uptake of Social Distancing during COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions
Tabular Data - 293.4 KB - 10 Variables, 5658 Observations - UNF:6:mMCrZk1GVCR8njjjNf1RBQ==
Survey codebook
HDSR_social_distancing.R
Oct 26, 2021 - Replication Data for: Increasing Uptake of Social Distancing during COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions
R Syntax - 13.6 KB - MD5: 964db83924ad871b16d92002c1ee3d4c
R code to replicate analysis
social_distancing_munged.tab
Oct 26, 2021 - Replication Data for: Increasing Uptake of Social Distancing during COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions
Tabular Data - 710.8 KB - 59 Variables, 2500 Observations - UNF:6:nKsADuFDvCqkviw2hH/cvA==
Surgo COVID Social Distancing Survey Instrument FINALv3.docx
Oct 26, 2021 - Replication Data for: Increasing Uptake of Social Distancing during COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions
MS Word - 46.5 KB - MD5: c2a8b2c0e7376a97cf7ba2b3b0fdfc06
Survey instrument from analysis
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