19,074 responses from 867 respondents to 22 items.
| Description | 22-item Negative Acts Questionnaire-Revised, 1-5; the item-level data behind an IRT/DIF study of workplace bullying. 867 respondents x 22 items = 19074 responses. |
|---|---|
| Reference | Daderman, A. M., Basinska, B. A., & Kajonius, P. (2023). Employing Item Response Theory to Identify Workplace Bullying Risk Groups, Gender-Based Response Disparities, and Shorten the Negative Acts Questionnaire-Revised [Data set]. Mendeley Data. https://doi.org/10.17632/mgwtzjww7g |
| Licence | CC BY 4.0 |
| Source data | https://data.mendeley.com/datasets/mgwtzjww7g |
| Responses | 19,074 |
|---|---|
| Respondents | 867 |
| Items | 22 |
| Response categories | 5 |
| Responses per respondent | 22 |
| Responses per item | 867 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Workplace |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | swe |
cov_agecov_gendercov_self_labelled_bulliediditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("daderman_2023_naqr")
# Python
pip install irw
import irw
df = irw.fetch("daderman_2023_naqr")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v7.0 |
| Redivis dataset DOI | 10.57761/cpj5-vm97 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |