5,844 responses from 974 respondents to 6 items.
One of 7 tables from the same source; this one measures Social comparison scale (reconfigured for Instagram) - downward comparison. The others are listed under Related tables.
| Description | Downward social comparison scale, female Instagram users, 6 items, N=974 |
|---|---|
| Reference | Jablonska, M. R., et al. (2020). Artificial neural networks for predicting social comparison effects among female Instagram users. PLOS ONE. |
| DOI | 10.1371/journal.pone.0229354 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229354 |
| Responses | 5,844 |
|---|---|
| Respondents | 974 |
| Items | 6 |
| Response categories | 7 |
| Responses per respondent | 6 |
| Responses per item | 974 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Internet-based, Targeted/specific |
| construct type | Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | pol |
| construct name | Social comparison scale (reconfigured for Instagram) - downward comparison |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Identification-Contrast Scale (Van der Zee et al., 2000) reconfigured for Instagram use - downward comparison items |
|---|---|
| Mean words per item | 19.667 |
| Mean characters per item | 110.333 |
| Mean characters per response | 21 |
| Flesch-Kincaid grade level | 9.880 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, jablonska_2020_instagram.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 source’s questionnaire, S3_Appendix.docx, which shows the item wording.
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("jablonska_2020_downward_comparison")
# Python
pip install irw
import irw
df = irw.fetch("jablonska_2020_downward_comparison")
@article{Jab_o_ska_2020, title={Artificial neural networks for predicting social comparison effects among female Instagram users}, volume={15}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0229354}, DOI={10.1371/journal.pone.0229354}, number={2}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Jabłońska, Marta R. and Zajdel, Radosław}, editor={Huk, Maciej}, year={2020}, month=Feb, pages={e0229354} }
| IRW version | v569 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v14.0 |
| Redivis dataset DOI | 10.57761/rkkt-p907 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v43.0 |