dong_2025_teacher_leadership

6,400 responses from 400 respondents to 16 items.

About this table

Description	Teacher leadership self-assessment, 16 items, 1-5 Likert (text-coded compliance scale), Xinjiang China teachers
Reference	Dong J, Chen X, Chen C, Chen C. (2025) Development and validation of a deep learning-based assessment tool for teacher leadership: A case study from Xinjiang, China. PLoS ONE.
DOI	10.1371/journal.pone.0331560
Licence	CC BY 4.0
Source data	https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0331560.s001

Size and shape

Responses	6,400
Respondents	400
Items	16
Response categories	5
Responses per respondent	16
Responses per item	400
Density	1
Longitudinal	FALSE

Classification

sample	Workplace
measurement tool	Survey/questionnaire
item format	Likert Scale/selected response
primary language(s)	chi

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

Instrument	Survey Questionnaire on the Current Situation of Leadership among Primary and Secondary School Teachers in Xinjiang Uygur Autonomous Region
Mean words per item	13.688
Mean characters per item	94.938
Mean characters per response	19.800
Flesch-Kincaid grade level	12.407

Columns

cov_agecov_educationcov_educational_stagecov_ethnicitycov_gendercov_majorcov_normal_university_gradcov_professional_titlecov_regioncov_teaching_experienceiditemresp

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("dong_2025_teacher_leadership")

# Python
pip install irw

import irw
df = irw.fetch("dong_2025_teacher_leadership")

Version and provenance

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