Fitting Bayesian item response models in Stata and Stan ∗

@inproceedings{Grant2016FittingBI,
  title={Fitting Bayesian item response models in Stata and Stan ∗},
  author={Robert L. Grant and Daniel Furr and Bob Carpenter and Andrew Gelman},
  year={2016}
}
Stata users have access to two easy-to-use implementations of Bayesian inference: Stata’s native bayesmh command and StataStan, which calls the general Bayesian engine, Stan. We compare these implementations on two important models for education research: the Rasch model and the hierarchical Rasch model. StataStan fits a more general range of models than can be fit by bayesmh and uses a superior sampling algorithm, that is, Hamiltonian Monte Carlo using the no-Uturn sampler. Furthermore… CONTINUE READING