55,658 responses from 1,637 respondents to 34 items.
One of 2 tables from the same source; this one is the offensive_topics table. The others are listed under Related tables.
| Description | 34-item binary (0/1) checklist of topics students found offensive, from a survey on gender differences in perceptions of instructor humor |
|---|---|
| Reference | Cooper KM, Hendrix T, Stephens MD, et al. (2018) To be funny or not to be funny: Gender differences in student perceptions of instructor humor in college science courses. PLoS ONE. |
| DOI | 10.1371/journal.pone.0201258 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0201258.s005 |
| Responses | 55,658 |
|---|---|
| Respondents | 1,637 |
| Items | 34 |
| Response categories | 2 |
| Responses per respondent | 34 |
| Responses per item | 1,637 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Humor survey: offensive joke subjects checklist |
|---|---|
| Mean words per item | 3.412 |
| Mean characters per item | 21.529 |
| Mean characters per response | 10 |
| Flesch-Kincaid grade level | 95.540 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, cooper_2018_humor.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). | anonymous.ID |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
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("cooper_2018_offensive_topics")
# Python
pip install irw
import irw
df = irw.fetch("cooper_2018_offensive_topics")
@article{Cooper_2018, title={To be funny or not to be funny: Gender differences in student perceptions of instructor humor in college science courses}, volume={13}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0201258}, DOI={10.1371/journal.pone.0201258}, number={8}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Cooper, Katelyn M. and Hendrix, Taija and Stephens, Michelle D. and Cala, Jacqueline M. and Mahrer, Kali and Krieg, Anna and Agloro, Ashley C. M. and Badini, Giovani V. and Barnes, M. Elizabeth and Eledge, Bradley and Jones, Roxann and Lemon, Edmond C. and Massimo, Nicholas C. and Martin, Annette and Ruberto, Thomas and Simonson, Kailey and Webb, Emily A. and Weaver, Joseph and Zheng, Yi and Brownell, Sara E.}, editor={Conejero, J. Alberto}, year={2018}, month=Aug, pages={e0201258} }
| IRW version | v569 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v14.0 |
| Redivis dataset DOI | 10.57761/rkkt-p907 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v43.0 |