3,660 responses from 732 respondents to 5 items.
One of 16 tables from the same source; this one is the spe table. The others are listed under Related tables.
| Description | 5 items on a 1-7 agreement scale, Chinese fitness coaches; the source labels the block only as SPE and ships no codebook, so the codes are kept verbatim rather than named. 3,660 responses from 732 respondents on 5 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 | 3,660 |
|---|---|
| Respondents | 732 |
| Items | 5 |
| Response categories | 7 |
| Responses per respondent | 5 |
| Responses per item | 732 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
These are classifications the IRW records, not the study’s own words. Some were annotated by hand and some written by an automated tagger whose output is published only where it clears a measured accuracy bar; which values came from which is not recorded per table. Coverage and accuracy per column, and what is held back, are on Tag quality.
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.
Respondent uniqueness. No respondent in this table is unique on the 2 covariates above: every one of them shares their combination with someone else. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.
Or load it directly in R (no account needed with irw 1.4.0 or later) or Python (needs a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("irw")
library(irw)
df <- irw_fetch("chen_2024_spe")
# Python
pip install irw
import irw
df = irw.fetch("chen_2024_spe")
Or in Stata, which reads the same no-account CSV as the button above:
* Stata
net install irw, from("https://raw.githubusercontent.com/itemresponsewarehouse/Stata-pkg/main/")
irw_fetch chen_2024_spe, clear
@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 | v674 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v11.0 |
| Redivis dataset DOI | 10.57761/vz89-gt69 |
| Manifest pin for this IRW version | v11.0 |
| Metadata source | irw_meta v59.0 |