34,965 responses from 999 respondents to 35 items.
| Description | LLM-generated big 5 inventory |
|---|---|
| Reference | Russell-Lasalandra, L. L., Christensen, A. P., & Golino, H. (2024, September 12). Generative Psychometrics via AI-GENIE: Automatic Item Generation and Validation via Network-Integrated Evaluation. https://doi.org/10.31234/osf.io/fgbj4 |
| DOI | 10.31234/osf.io/fgbj4 |
| Licence | CC0 1.0 |
| Source data | https://osf.io/zcytb/?view_only=79d2c8bf12c24393863d60c4143f8a0e |
| Responses | 34,965 |
|---|---|
| Respondents | 999 |
| Items | 35 |
| Response categories | 5 |
| Responses per respondent | 35 |
| Responses per item | 999 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Internet-based |
| construct type | Personality |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
| construct name | Big Five |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | AI-GENIE GPT-4o generated Big Five personality inventory |
|---|---|
| Mean words per item | 8.343 |
| Mean characters per item | 46.029 |
| Mean characters per response | 14 |
| Flesch-Kincaid grade level | 6.120 |
cov_agecov_englishcov_gendercov_hispaniccov_raceiditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("genpsych_russell_2024_gpt4o")
# Python
pip install irw
import irw
df = irw.fetch("genpsych_russell_2024_gpt4o")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse v53.0 |
| Redivis dataset DOI | 10.57761/4g08-xt41 |
| Manifest pin for this IRW version | v53.0 |
| Metadata source | irw_meta v23.0 |