50,000 simulated responses from 1,000 respondents to 50 items.
Simulated data. These responses were generated by a script, not collected from people; see the generating script. The cov_true_alpha1, cov_true_alpha2, cov_true_alpha3, cov_true_alpha4, cov_true_order, cov_true_theta columns hold the true generating values, which a real dataset would not have.
| Description | Simulated longitudinal skill growth (higher-order hidden Markov cognitive diagnosis model): 1,000 persons x 50 DINA items over 5 waves (10 items per wave, counterbalanced blocks, 4 skills). True learning ability and per-wave skill mastery in cov_true_*, true slip/guess in itemcov_true_*, Q-matrix in qmatrix1-4. |
|---|---|
| Reference | Wang, S., Yang, Y., Culpepper, S. A., & Douglas, J. A. (2018). Tracking skill acquisition with cognitive diagnosis models: A higher-order, hidden Markov model with covariates. Journal of Educational and Behavioral Statistics, 43(1), 57-87. Generated with hmcdm::sim_alphas and sim_hmcdm (hmcdm R package, version 2.1.3). |
| DOI | 10.3102/1076998617719727 |
| Licence | CC0 1.0 |
| Generating script | https://cran.r-project.org/package=hmcdm |
| Responses | 50,000 |
|---|---|
| Respondents | 1,000 |
| Items | 50 |
| Response categories | 2 |
| Responses per respondent | 50 |
| Responses per item | 1,000 |
| Density | 1 |
cov_true_alpha1cov_true_alpha2cov_true_alpha3cov_true_alpha4cov_true_ordercov_true_thetaiditemitemcov_true_guessitemcov_true_slipqmatrix1qmatrix2qmatrix3qmatrix4respwave
id, item, resp, wave 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("hmcdm_learning_sim", source = "sim")
# Python
pip install irw
import irw
df = irw.fetch("hmcdm_learning_sim", source="sim")
@article{Wang_2017, title={Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates}, volume={43}, ISSN={1935-1054}, url={http://dx.doi.org/10.3102/1076998617719727}, DOI={10.3102/1076998617719727}, number={1}, journal={Journal of Educational and Behavioral Statistics}, publisher={American Educational Research Association (AERA)}, author={Wang, Shiyu and Yang, Yan and Culpepper, Steven Andrew and Douglas, Jeffrey A.}, year={2017}, month=July, pages={57–87} }
| 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 |