4,380 responses from 876 respondents to 5 items.
One of 3 tables from the same source; this one measures Teacher efficacy (5 items). The others are listed under Related tables.
| Description | Teacher efficacy, 5 items (1-5); N = 876 Indonesian teachers; no covariates. |
|---|---|
| Reference | Habibi, A. (2020). Dataset for Factors Affecting Teachers' Burnout: A SEM Analysis in an Asian Context. Mendeley Data. https://doi.org/10.17632/6jmv43nffk |
| Licence | CC BY 4.0 |
| Source data | https://data.mendeley.com/datasets/6jmv43nffk |
| Responses | 4,380 |
|---|---|
| Respondents | 876 |
| Items | 5 |
| Response categories | 5 |
| Responses per respondent | 5 |
| Responses per item | 876 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Workplace |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ind |
| construct name | Teacher efficacy (5 items) |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, habibi_2020_teacher_burnout.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). | made in the build script |
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("habibi_2020_te")
# Python
pip install irw
import irw
df = irw.fetch("habibi_2020_te")
@misc{https://doi.org/10.17632/6jmv43nffk,
doi = {10.17632/6JMV43NFFK},
url = {https://data.mendeley.com/datasets/6jmv43nffk},
author = {Habibi, Akhmad},
keywords = {Education, Health},
title = {Dataset for Factors Affecting Teachers’ Burnout: A SEM Analysis in an Asian Context},
publisher = {Mendeley},
year = {2020}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_7 v1.1 |
| Redivis dataset DOI | 10.57761/7pd3-sh31 |
| Manifest pin for this IRW version | v1.0 |
| Metadata source | irw_meta v44.0 |