16,370 responses from 107 respondents to 5 items.
| Description | We gathered and examined subjective ratings of emojis from 138 German speakers along five essential dimensions: valence, arousal, familiarity, clarity, and visual complexity. |
|---|---|
| Reference | Scheffler, T., & Nenchev, I. (2024). Affective, semantic, frequency, and descriptive norms for 107 face emojis. Behavior Research Methods, 56(8), 8159-8180. |
| DOI | 10.3758/s13428-024-02444-x |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/vbmpj/files/735md |
| Responses | 16,370 |
|---|---|
| Respondents | 107 |
| Items | 5 |
| Response categories | 101 |
| Responses per respondent | 152.991 |
| Responses per item | 3,274 |
| Density | 30.598 |
| Longitudinal | FALSE |
| age range | Non-human |
|---|---|
| sample | Non-human |
| construct type | Affective/mental health, Cognitive/educational, Opinion/attitude |
| measurement tool | Survey/questionnaire |
| item format | Slider/continuous |
| primary language(s) | ger |
| construct name | valence, arousal, familiarity, clarity, and visual complexity. |
iditemraterresprt
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("emoji_scheffler_2024")
# Python
pip install irw
import irw
df = irw.fetch("emoji_scheffler_2024")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_2 v19.0 |
| Redivis dataset DOI | 10.57761/qtx5-4m80 |
| Manifest pin for this IRW version | v19.0 |
| Metadata source | irw_meta v23.0 |