142,490 responses from 1,898 respondents to 244 items.
| Description | The current study established a normative database for 244 trivia questions with a large sample (N = 1498) and examined how pre-answer interest (i.e., interest in the question) and post-answer interest (i.e., interest in the answer) relate to learning performance |
|---|---|
| Reference | Fastrich, G. M., Kerr, T., Castel, A. D., & Murayama, K. (2018). The role of interest in memory for trivia questions: An investigation with a large-scale database. Motivation Science, 4(3), 227–250. https://doi.org/10.1037/mot0000087 |
| DOI | 10.1037/mot0000087 |
| Licence | CC BY 4.0 |
| Source data | https://osf.io/kjahf/overview |
| Responses | 142,490 |
|---|---|
| Respondents | 1,898 |
| Items | 244 |
| Response categories | 2 |
| Responses per respondent | 75.074 |
| Responses per item | 583.975 |
| Density | 0.308 |
| Longitudinal | TRUE |
| age range | Adult (18+) |
|---|---|
| sample | Internet-based |
| construct type | Cognitive/educational |
| measurement tool | Test |
| item format | Constructed Response |
| primary language(s) | eng |
| construct name | Memory |
cov_agecov_confidencecov_corranswercov_curiositycov_educationcov_englishcov_gendercov_interestcov_questioncov_responsecov_trialdateiditemresprtwave
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("trivia_fastrich_2017")
# Python
pip install irw
import irw
df = irw.fetch("trivia_fastrich_2017")
| 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 |