40,000 simulated responses from 1,000 respondents to 40 items.
Simulated data. These responses were generated by a script, not collected from people; see the generating script. The cov_true_preknowledge, cov_true_tau, cov_true_theta columns hold the true generating values, which a real dataset would not have.
| Description | Simulated item preknowledge with response times: 1,000 persons x 40 items, 2PL scores and lognormal times; 10% of examinees know 40% of the items (scores redrawn at P = .90, log times x 0.75). True ability, speed and preknowledge flag in cov_true_*, true item parameters and compromised flag in itemcov_true_*. |
|---|---|
| Reference | Sinharay, S. (2017). Detection of item preknowledge using likelihood ratio test and score test. Journal of Educational and Behavioral Statistics, 42(1), 46-68. Generated with aberrance::sim (Gorney, K., & Deng, J., aberrance R package, version 0.3.0). |
| DOI | 10.3102/1076998616673872 |
| Licence | CC0 1.0 |
| Generating script | https://cran.r-project.org/package=aberrance |
| Responses | 40,000 |
|---|---|
| Respondents | 1,000 |
| Items | 40 |
| Response categories | 2 |
| Responses per respondent | 40 |
| Responses per item | 1,000 |
| Density | 1 |
cov_true_preknowledgecov_true_taucov_true_thetaiditemitemcov_true_aitemcov_true_alphaitemcov_true_bitemcov_true_betaitemcov_true_compromisedresprt
id, item, resp, rt are defined in the IRW data standard and mean the same in every table. The other columns are set by the generating script.
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("aberrance_pk_sim", source = "sim")
# Python
pip install irw
import irw
df = irw.fetch("aberrance_pk_sim", source="sim")
@article{Sinharay_2016, title={Detection of Item Preknowledge Using Likelihood Ratio Test and Score Test}, volume={42}, ISSN={1935-1054}, url={http://dx.doi.org/10.3102/1076998616673872}, DOI={10.3102/1076998616673872}, number={1}, journal={Journal of Educational and Behavioral Statistics}, publisher={American Educational Research Association (AERA)}, author={Sinharay, Sandip}, year={2016}, month=Oct, pages={46–68} }
| IRW version | v581 |
|---|---|
| Redivis dataset | irw_simsyn v1.4 |
| Redivis dataset DOI | 10.57761/d90d-aa22 |
| Manifest pin for this IRW version | v1.4 |
| Metadata source | irw_meta v44.0 |