2,928 responses from 732 respondents to 4 items.
One of 16 tables from the same source; this one is the ae table. The others are listed under Related tables.
| Description | 4 items on a 1-7 agreement scale, Chinese fitness coaches; the source labels the block only as AE and ships no codebook, so the codes are kept verbatim rather than named. 2,928 responses from 732 respondents on 4 items. |
|---|---|
| Reference | Chen, B. (2024). Exploring the Impact of Curiosity and Sport Commitment on Creativity among Fitness Coaches: The Mediating Role of Knowledge-Sharing and Flow Experience [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13855427 |
| Licence | CC BY 4.0 |
| Source data | https://zenodo.org/records/13855427 |
| Responses | 2,928 |
|---|---|
| Respondents | 732 |
| Items | 4 |
| Response categories | 7 |
| Responses per respondent | 4 |
| Responses per item | 732 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, chen_2024_fitness_coach_creativity.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). | ord |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age_band | Covariate | Age |
cov_gender | Covariate | Gender |
cov_monthly_income | Covariate | Monthly |
cov_seniority | Covariate | Seniority |
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("chen_2024_ae")
# Python
pip install irw
import irw
df = irw.fetch("chen_2024_ae")
@misc{https://doi.org/10.5281/zenodo.13855427,
doi = {10.5281/ZENODO.13855427},
url = {https://zenodo.org/doi/10.5281/zenodo.13855427},
author = {Chen, Bin},
title = {Exploring the Impact of Curiosity and Sport Commitment on Creativity among Fitness Coaches: The Mediating Role of Knowledge-Sharing and Flow Experience},
publisher = {Zenodo},
year = {2024},
copyright = {Creative Commons Attribution 4.0 International}
}
| IRW version | v566 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.4 |
| Redivis dataset DOI | 10.57761/0gqp-zn75 |
| Manifest pin for this IRW version | v8.4 |
| Metadata source | irw_meta v43.0 |