12,431 responses from 1,085 respondents to 13 items.
One of 13 tables from the same source; this one measures Climate policy support. The others are listed under Related tables.
| Description | Support for 13 climate policies (market mechanisms, industry/personal taxes, public education, Kyoto, efficiency mandates, renewables, agriculture, coasts, fuel-efficient autos, gas price), 1 strongly oppose - 4 strongly support; q93_v1/q93_v2 are a split-ballot pair each answered by half the sample; 2004 US national telephone survey, N = 1,093 adults; cov_gender, cov_age, cov_education, cov_ideology, cov_income. |
|---|---|
| Reference | Kellstedt, P. M., Ramirez, M. D., Vedlitz, A., & Zahran, S. (2019). Does political sophistication minimize value conflict? Evidence from a heteroskedastic graded IRT model of opinions toward climate change. British Journal of Political Science, 49(4), 1309-1332. Replication data: https://doi.org/10.7910/DVN/SRC9HN |
| DOI | 10.1017/S0007123416000600 |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/SRC9HN |
| Responses | 12,431 |
|---|---|
| Respondents | 1,085 |
| Items | 13 |
| Response categories | 4 |
| Responses per respondent | 11.457 |
| Responses per item | 956.231 |
| Density | 0.881 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | General/non-specific |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Climate policy support |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, kellstedt_2017_climate_survey.py. 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). | resp_num |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | q117 |
cov_education | Covariate | q116 |
cov_gender | Covariate | gender |
cov_ideology | Covariate | q118 |
cov_income | Covariate | q122 |
What item codes and covariate values stand for is in the codebook released with 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("kellstedt_2017_policy_support")
# Python
pip install irw
import irw
df = irw.fetch("kellstedt_2017_policy_support")
@article{V_ssing_2017, title={Information Behavior and Political Preferences}, volume={49}, ISSN={1469-2112}, url={http://dx.doi.org/10.1017/S0007123416000600}, DOI={10.1017/s0007123416000600}, number={2}, journal={British Journal of Political Science}, publisher={Cambridge University Press (CUP)}, author={Vössing, Konstantin and Weber, Till}, year={2017}, month=Mar, pages={533–556} }
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_7 v1.1 |
| Redivis dataset DOI | 10.57761/7pd3-sh31 |
| Manifest pin for this IRW version | v1.0 |
| Metadata source | irw_meta v44.0 |