6,008 responses from 1,502 respondents to 4 items.
One of 3 tables from the same source; this one measures COVID-19 policy attitudes. The others are listed under Related tables.
| Description | COVID-19 policy attitudes, 4 items; 4 items, 0-10 slider (strongly disagree-strongly agree); 1502 US online respondents (March 2020) randomised to five information-cue arms; covariates: cov_arm and demographics (gender, age band, education, party, news following, state, employment, essential worker, income). |
|---|---|
| Reference | Favero, N., & Pedersen, M. J. (2020). How to encourage 'Togetherness by Keeping Apart' amid COVID-19? The ineffectiveness of prosocial and empathy appeals. Journal of Behavioral Public Administration, 3(2). https://doi.org/10.30636/jbpa.32.167 |
| DOI | 10.30636/jbpa.32.167 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/1LIIDV |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Responses | 6,008 |
|---|---|
| Respondents | 1,502 |
| Items | 4 |
| Response categories | 11 |
| Responses per respondent | 4 |
| Responses per item | 1,502 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Internet-based |
|---|---|
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Slider/continuous |
| primary language(s) | eng |
| construct name | COVID-19 policy attitudes |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | COVID-19 policy attitudes (Favero & Pedersen 2020) |
|---|---|
| Mean words per item | 16.250 |
| Mean characters per item | 95.750 |
| Mean characters per response | 4.455 |
| Flesch-Kincaid grade level | 9.609 |
This codebook is reconstructed by the IRW from the IRW data standard. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | not traced |
item | IRW standard Item identifier. | not traced |
resp | IRW standard Response value. | not traced |
| Respondent covariates | ||
cov_age | Covariate | not traced |
cov_arm | Covariate | not traced |
cov_education | Covariate | not traced |
cov_employment | Covariate | not traced |
cov_employment_change | Covariate | not traced |
cov_essential_worker | Covariate | not traced |
cov_follow_news | Covariate | not traced |
cov_gender | Covariate | not traced |
cov_income | Covariate | not traced |
cov_party | Covariate | not traced |
cov_state | Covariate | not traced |
What item codes and covariate values stand for is in the source’s codebook, 3829236.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("favero_2020_covid_attitudes")
# Python
pip install irw
import irw
df = irw.fetch("favero_2020_covid_attitudes")
@article{Favero_2020, title={How to encourage “Togetherness by Keeping Apart” amid COVID-19? The ineffectiveness of prosocial and empathy appeals}, volume={3}, ISSN={2576-6465}, url={http://dx.doi.org/10.30636/jbpa.32.167}, DOI={10.30636/jbpa.32.167}, number={2}, journal={Journal of Behavioral Public Administration}, publisher={Center for Experimental and Behavioral Public Administration}, author={Favero, Nathan and Pedersen, Mogens Jin}, year={2020}, month=May }
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |