jablonska_2020_profile_grooming
2,922 responses from 974 respondents to 3 items.
About this table
| Description | Profile-grooming scale, female Instagram users, 3 items, N=974 |
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| Reference | Jablonska, M. R., et al. (2020). Artificial neural networks for predicting social comparison effects among female Instagram users. PLOS ONE. |
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| DOI | 10.1371/journal.pone.0229354 |
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| Licence | CC BY 4.0 |
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| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229354 |
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Size and shape
| Responses | 2,922 |
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| Respondents | 974 |
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| Items | 3 |
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| Response categories | 7 |
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| Responses per respondent | 3 |
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| Responses per item | 974 |
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| Density | 1 |
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| Longitudinal | FALSE |
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Classification
| age range | Adult (18+) |
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| sample | Internet-based, Targeted/specific |
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| construct type | Behavioral |
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| measurement tool | Survey/questionnaire |
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| item format | Likert Scale/selected response |
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| primary language(s) | pol |
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| construct name | Profile-grooming scale |
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Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Instagram usage and social comparison questionnaire (Jabłońska & Zajdel, 2020) - profile grooming scale |
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| Mean words per item | 7.667 |
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| Mean characters per item | 47 |
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| Mean characters per response | 21 |
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| Flesch-Kincaid grade level | 8.948 |
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Get the data
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("jablonska_2020_profile_grooming")
# Python
pip install irw
import irw
df = irw.fetch("jablonska_2020_profile_grooming")
Version and provenance
| IRW version | v393 |
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| Redivis dataset | item_response_warehouse_3 v7.0 |
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| Redivis dataset DOI | 10.57761/pqqn-pm43 |
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| Manifest pin for this IRW version | v7.0 |
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| Metadata source | irw_meta v23.0 |
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