1,212 responses from 404 respondents to 3 items.
One of 6 tables from the same source; this one is the task_tech_fit table. The others are listed under Related tables.
| Description | 3-item Task Technology Fit scale (7-point Likert, 1-7), from a survey of N=404 students on online learning during COVID-19. |
|---|---|
| Reference | Butt S, Mahmood A, Saleem S (2022) The role of institutional factors and cognitive absorption on students' satisfaction and performance in online learning during COVID 19. PLoS ONE. |
| DOI | 10.1371/journal.pone.0269609 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0269609.s003 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Responses | 1,212 |
|---|---|
| Respondents | 404 |
| Items | 3 |
| Response categories | 7 |
| Responses per respondent | 3 |
| Responses per item | 404 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Educational |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Task Technology Fit |
|---|---|
| Mean words per item | 10.333 |
| Mean characters per item | 62.333 |
| Mean characters per response | 14.429 |
| Flesch-Kincaid grade level | 8.614 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, butt_2022_online_learning.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). | ID |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate 1 = 20 or less · 2 = 21-30 · 3 = 31-40 | Age |
cov_education | Covariate 1 = Intermediate · 2 = Bachelors · 3 = Masters · 4 = MPhil · 5 = Ph.D. · 6 = Others | Education |
cov_gender | Covariate 1 = Males · 2 = Females | Gender |
cov_university | Covariate | University |
Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled values stand for is in the source’s questionnaire, S1_Appendix.docx, which shows the item wording.
Or load it directly in R or Python:
# R (no account needed)
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("butt_2022_task_tech_fit")
# Python (needs a free Redivis account)
pip install irw
import irw
df = irw.fetch("butt_2022_task_tech_fit")
@article{Butt_2022, title={The role of institutional factors and cognitive absorption on students’ satisfaction and performance in online learning during COVID 19}, volume={17}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0269609}, DOI={10.1371/journal.pone.0269609}, number={6}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Butt, Sameera and Mahmood, Asif and Saleem, Saima}, editor={Papa, Filomena}, year={2022}, month=June, pages={e0269609} }
| IRW version | v540 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v37.0 |