Methodological Publications
Submitted/Under Revision
Liu, Y., & Pek, J. Summed versus estimated factor scores: Considering uncertainties when using observed scores.
Liu, Y., & Wang, W. What can we learn from a semiparametric factor analysis of item responses and response time? An illustration with the PISA 2015 data. https://arxiv.org/abs/2303.10079
Liu, Y., Hannig, J., & Murph, A. C. A Geometric Perspective on Bayesian and Generalized Fiducial Inference. https://arxiv.org/abs/2210.05462
Morell, M., Liu, Y., & Yang, J. A regression discontinuity model with latent variables.
In Press
Liu, Y., & Sweet T. M. Statistical inference: Bayesian approaches. In Tierney, R., Rizvi, F., and Ercikan, K. (Eds.), International Encyclopedia of Education, 4th Edition. Oxford: Elsevier Ltd.
2022
Magnus, B. E., & Liu, Y. (2022). Symptom presence and symptom severity as unique indicators of psychopathology: An application of multidimensional zero-inflated and hurdle graded response models. Educational and Psychological Measurement, 82(5), 938-966. https://doi.org/10.1177/00131644211061820
Wang, W., Liu, Y., & Liu, H. (2022). Testing differential item functioning without predefined anchor items using robust regression. Journal of Educational and Behavioral Statistics, 47(6), 666-692. https://doi.org/10.3102/10769986221109208
Xu, S., & Liu, Y. (2022). Characterizing sampling variability for projected IRT scores in a fixed-item-parameter linking design. Applied Psychological Measurement, 46(6), 509-528. https://doi.org/10.1177%2F01466216221108136
Liu, Y., & Wang, W. (2022). Semiparametric factor analysis for item-level response time data. Psychometrika, 87(2), 666-692. https://doi.org/10.1007/s11336-021-09832-8
2021
Liu, Y. (2021). Riemannian Newton and trust-region algorithms for analytic rotation in exploratory factor analysis. British Journal of Mathematical and Statistical Psychology, 74(1), 139-163. https://doi.org/10.1111/bmsp.12211
Tian, C., & Liu, Y. (2021). A rotation criterion that encourages a hierarchical factor structure. In Wiberg, M., Molenaar, D., González, J., Böckenholt, U., and Kim, J.-S. (Eds.), Proceedings of the 85th Annual (Virtual) Meeting of the Psychometric Society. New York, NY: Springer. https://doi.org/10.1007/978-3-030-74772-5_1
2020
Liu, Y. (2020). A Riemannian optimization algorithm for joint maximum likelihood estimation of high-dimensional exploratory item factor analysis. Psychometrika, 85(2), 439-468. https://doi.org/10.1007/s11336-020-09711-8
Wang, X., & Liu, Y. (2020). Detecting compromised items using information from secure items. Journal of Educational and Behavioral Statistics, 45(6), 667-689. https://doi.org/10.3102/1076998620912549
Zhang, S., Chen, Y., & Liu, Y. (2020). An improved stochastic EM algorithm for large-scale full-information item factor analysis. British Journal of Mathematical and Statistical Psychology, 73(1), 44–71. https://doi.org/10.1111/bmsp.12153
Man, K., Harring, J. R., & Liu, Y. (2020). Abstract: Methods of integrating multi-modal data for assessing aberrant test-taking behaviors. Multivariate Behavioral Research, 55(1), 155–156. https://doi.org/10.1080/00273171.2019.1699010
Morell, M., Yang, J. S., & Liu, Y. (2020). Abstract: Latent variable regression discontinuity design with cluster level treatment assignment. Multivariate Behavioral Research, 55(1), 146. https://doi.org/10.1080/00273171.2019.1696170
2019
Haberman, S., Liu, Y., & Lee, Y.-H. (2019). Distractor analysis for multiple-choice tests: An empirical study with international language assessment data. ETS Research Report Series, 19(39), 1-16. https://doi.org/10.1002/ets2.12275
Liu, Y., Hannig, J., & Pal Majumder, A. (2019). Second-order probability matching priors for the person parameter in unidimensional item response theory models. Psychometrika, 84(3), 529–553. https://doi.org/10.1007/s11336-019-09675-4
Wang, X., Liu, Y., Robin, F., & Guo, H. (2019). A comparison of methods for detecting examinee preknowledge of items. International Journal of Testing, 19(3), 207–226. https://doi.org/10.1080/15305058.2019.1610886
Liu, Y., Yang, J. S., & Maydeu-Olivares, A. (2019). Restricted recalibrations of item response theory models. Psychometrika, 84(2), 529–553. https://doi.org/10.1007/s11336-019-09667-4
2018
Magnus, B., & Liu, Y. (2018). A zero-inflated Box-Cox normal unipolar item response model for measuring constructs of psychopathology. Applied Psychological Measurement, 42(7), 571–589. https://doi.org/10.1177/0146621618758291
Chen, Y., Liu, Y., & Xu, S. (2018). Mutual information reliability for latent class analysis. Applied Psychological Measurement, 42(6), 460–477. https://doi.org/10.1177/0146621617748324
Liu, Y. & Yang, J. S. (2018). Bootstrap-calibrated interval estimates for latent variable scores in item response theory. Psychometrika, 83(2), 333-354. https://doi.org/10.1007/s11336-017-9582-9
Liu, Y., & Yang, J. S. (2018). Interval estimation of scale scores in item response theory. Journal of Educational and Behavioral Statistics, 43(3), 259–285. https://doi.org/10.3102/1076998617732764
Liu, Y., Magnus, B., Quinn, H., & Thissen, D. (2018). Multidimensional item response theory. In Hughes, D., Irwing, P., and Booth, T. (Eds.), Handbook of Psychometric Testing (pp. 445–493). Chichester, West Sussex: Wiley-Blackwell. https://doi.org/10.1002/9781118489772.ch36
Yang, J. S., Morell, M., & Liu, Y. (2018). Constructed-response items. In Frey, B., (Ed.), The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation (pp. 381-383). Los Angeles, CA: SAGE Publications, Inc. https://dx.doi.org/10.4135/9781506326139.n145
Depaoli, S., &, Liu, Y. (2018). Book Review: Bayesian Psychometric Modeling. Psychometrika, 83(2), 511–514. https://doi.org/10.1007/s11336-017-9567-8
2017
Chalmers, R. P., Pek, J., & Liu, Y. (2017). Profile-likelihood confidence intervals in item response theory models. Multivariate Behavioral Research, 52(5), 533–550. https://doi.org/10.1080/00273171.2017.1329082
Wang, X., Liu, Y., & Hambleton, R. K. (2017). Detecting candidate preknowledge of items using a predictive checking method. Applied Psychological Measurement, 41(4), 243-263. https://doi.org/10.1177/0146621616687285
Liu, Y. & Hannig, J. (2017). Generalized fiducial inference for logistic graded response models. Psychometrika, 82(4), 1097–1125. https://doi.org/10.1007/s11336-017-9554-0
2016
Liu, Y., & Hannig, J. (2016). Generalized fiducial inference for binary logistic item response models. Psychometrika, 81(2), 290–324. https://doi.org/10.1007/s11336-015-9492-7
Liu, Y., Magnus, B. E., & Thissen, D. (2016). Modeling and testing differential item functioning in unidimensional binary item response models with a single continuous covariate: A functional data analysis approach. Psychometrika, 81(2), 371–398. https://doi.org/10.1007/s11336-015-9473-x
2015
Maydeu-Olivares, A., & Liu, Y. (2015). Item diagnostics in multivariate discrete data. Psychological Methods, 20(2), 276–292. https://doi.org/10.1037/a0039015
Before 2015
Liu, Y. & Thissen, D. (2014). Comparing score tests and other local dependence diagnostics for the graded response model. British Journal of Mathematical and Statistical Psychology, 67(3), 496–513. https://doi.org/10.1111/bmsp.12030
Liu, Y., & Maydeu-Olivares, A. (2014). Identifying the source of misfit in item response theory models. Multivariate Behavioral Research, 49(4), 354–371. https://doi.org/10.1080/00273171.2014.910744
Liu, Y., & Hannig, J. (2014). Abstract: Generalized fiducial inference for binary logistic item response models. Multivariate Behavioral Research, 49(3), 290. https://doi.org/10.1080/00273171.2014.912920
Liu, Y. & Maydeu-Olivares, A. (2013). Local dependence diagnostics in IRT modeling of binary data. Educational and Psychological Measurement, 73(2), 254–274. https://doi.org/10.1177/0013164412453841
Liu, Y. & Thissen, D. (2012). Identifying local dependence with a score test statistic based on the bifactor logistic model. Applied Psychological Measurement, 36(8), 670–688. https://doi.org/10.1177/0146621612458174