ChernyshenkoO. S.StarkS.ChanK.DrasgowF.WilliamsB. (2001). Fitting item response theory models to two personality inventories: Issues and insights. Multivariate Behavioral Research, 36, 523-562.
2.
de la TorreJ.StarkS.ChernyshenkoO. S. (2006). Markov chain Monte Carlo estimation of item parameters for the generalized graded unfolding model. Applied Psychological Measurement, 30, 216-232.
3.
DrasgowF.ChernyshenkoO. S.StarkS. (2010). 75 years after Likert: Thurstone was right!Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 465-476. doi:10.1111/j.1754-9434.2010.01273.x
4.
GewekeJ. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In BernardoJ.BergerJ.DawidA.SmithA. (Eds.), Bayesian statistics4, Oxford, UK: Oxford University Press, 1-30.
5.
HastingsW. K. (1970). Monte Carlo sampling methods using Markov Chains and their applications. Biometrika, 57, 97-109.
6.
MetropolisN.RosenbluthA. W.RosenbluthM. N.TellerA. H.TellerE. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087-1092.
7.
RobertsJ. S.DonoghueJ. R.LaughlinJ. E. (2000). A general item response theory model for unfolding unidimensional polytomous responses. Applied Psychological Measurement, 24, 3-32.
8.
RobertsJ. S.FangH.CuiW.WangY. (2006). GGUM2004: A Windows-based program to estimate parameters in the generalized graded unfolding model. Applied Psychological Measurement, 30, 64-65.
9.
WangW.TayL.DrasgowF. (2013). Assessing differential item functioning of polytomous items for an ideal point response process. Applied Psychological Measurement, 37, 316-335. doi:10.1177/0146621613476156
