18,660 responses from 3,110 respondents to 6 items.
One of 2 tables from the same source; this one measures Discrimination. The others are listed under Related tables.
| Description | Likert-scale responses to the presence of discrimination, inequality, and justice in society currently |
|---|---|
| Reference | Friedman, Jeffrey, 2018, "Replication Data for: Priorities for Preventive Action: Explaining Americans' Divergent Reactions to 100 Public Risks", https://doi.org/10.7910/DVN/ZSJA25 |
| Licence | Custom |
| Source data | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZSJA25 |
| Responses | 18,660 |
|---|---|
| Respondents | 3,110 |
| Items | 6 |
| Response categories | 6 |
| Responses per respondent | 6 |
| Responses per item | 3,110 |
| Density | 1 |
| Longitudinal | TRUE |
| age range | Mixed |
|---|---|
| sample | General/non-specific |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Discrimination |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Cultural worldview items: grid (hierarchy-egalitarianism) index |
|---|---|
| Mean words per item | 14.833 |
| Mean characters per item | 84.833 |
| Mean characters per response | 16.167 |
| Flesch-Kincaid grade level | 9.155 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, friedman_2018_risks.do. 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). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | `var' |
| Respondent covariates | ||
cov_age | Covariate | age |
cov_democrat | Covariate | democrat |
cov_education | Covariate | education |
cov_enddate | Covariate | enddate |
cov_gender | Covariate | gender |
cov_hispanic | Covariate | hispanic |
cov_income | Covariate | income |
cov_independent | Covariate | independent |
cov_party | Covariate | party |
cov_race | Covariate | race |
cov_republican | Covariate | republican |
cov_sduration | Covariate | durationinseconds |
cov_startdate | Covariate | startdate |
cov_state | Covariate | state |
cov_zip | Covariate | zip |
What item codes and covariate values stand for is in the source’s codebook, Codebook.pdf.
Custom licence — check the terms before reuse at the source data.
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("friedman_2018_risks_discrimination")
# Python
pip install irw
import irw
df = irw.fetch("friedman_2018_risks_discrimination")
@misc{https://doi.org/10.7910/dvn/zsja25,
doi = {10.7910/DVN/ZSJA25},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/ZSJA25},
author = {Friedman, Jeffrey},
keywords = {Social Sciences, Risk, Government spending, Political psychology, Terrorism, Misperception, Public understanding of science},
title = {Replication Data for: Priorities for Preventive Action: Explaining Americans' Divergent Reactions to 100 Public Risks},
publisher = {Harvard Dataverse},
year = {2018}
}
| IRW version | v549 |
|---|---|
| Redivis dataset | item_response_warehouse_2 v33.0 |
| Redivis dataset DOI | 10.57761/6m04-m698 |
| Manifest pin for this IRW version | v33.0 |
| Metadata source | irw_meta v39.0 |