Journal Articles & Book Chapters

  • Jiao, H., Liao, D., & Zhan, P. (in press). Utilizing process data for cognitive diagnosis. In M. von Davier & Y. Lee (Eds.), Handbook of Diagnostic Classification Models.
  • Jiao, H., & Li, C. (2018). Progress in International Reading Literacy Study (PIRLS) data. In The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
  • Jiao, H., & Liao, D. (2018). Testlet response theory. In The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
  • Qiao, X., & Jiao, H. (in press). Review of the book Bayesian psychometric modeling, by Levy and Mislevy. Measurement: Interdisciplinary Research and Perspectives, 16(2).
  • Qiao, X., & Jiao, H. (2018). Data Mining Techniques in Analyzing Process Data: A Didactic. Frontiers in psychology9https://doi.org/10.3389/fpsyg.2018.02231

  • Qiao, X., & Jiao, H. (2018). Review of the book Bayesian Psychometric Modeling, by Levy. R & Mislevy. R. JMeasurement: Interdisciplinary Research and Perspectives16, 135-137, DOI: 10.1080/15366367.2018.1437307

  • Man, K., Harring, J., Jiao, H., & Zhan, P. (in press). Conditional joint modeling of compensatory multidimensional item responses and response times. Applied Psychological Measurement.

  •  Zhan, P., Jiao, H., Liao, D. & Li, F (in press). A longitudinal higher-order diagnostic classification model. Journal of Educational and Behavioral Statistics.

  •  Zhan, P., Wang, W.-C., Jiao, H., & Bian, Y. (2018). The probabilistic-inputs, noisy conjunctive models for cognitive diagnosis. Frontiers in Psychology.

  • Zhan, P., Jiao, H., Liao, M., & Bian, Y. (2018). Bayesian DINA modeling incorporating within-item characteristics dependency. Applied Psychological Measurementhttps://doi.org/10.1177/0146621618781594

  • Qiao*, X., & Jiao, H. (2018). Comparing data mining techniques in analyzing process data: A case study on PISA 2012 problem-solving items. Frontiers in Psychology.

  • Zhan, P., & Jiao, H. (2018). Using JAGS for Bayesian cognitive diagnosis modeling: a tutorial. Journal of Educational and Behavioral Statistics.

  • Liao, M., & Jiao, H. (in press). Book review: Psychometric methods: theory in practice, by L. R. Price. Psychometrika.

  • Qiao*, X., & Jiao, H. (in press). Book review: Bayesian psychometric modelingMeasurement: Interdisciplinary Research and Perspectives, 16(2).

  • Zhan, P. Jiao, H., & Liao, D. (2017). Cognitive diagnosis modeling incorporating item response times. British Journal of Mathematical and Statistical Psychology.. DOI: 10.1111/bmsp.12114
  • Li, Y., Xiao, T., Liao, D., & Lee, M.-L. (2017). Using threshold regression to analyze survival data from complex surveys: with application to mortality linked NHANES III Phase II genetic data. Statistics in Medicine..
  • Luo, Y., & Jiao, H. (2017). Using the Stan program for Bayesian item response theory. Educational and Psychological Measurement. DOI: 10.1177/0013164417693666
  • Jiao, H., Lissitz, R. W., & Zhan, P. (2017). Calibrating innovative items embedded in multiple contexts. In H. Jiao & R.W. Lissitz (Eds.), Technology-enhanced innovative assessment: Development, modeling, scoring from an interdisciplinary perspective. Charlotte, NC: Information Age Publishing.
  • Li, T., Xie, C., & Jiao, H. (2016). Assessing fit of alternative unidimensional polytomous item response models using posterior predictive model checking. Psychological Methods.
  • Li*, T., Jiao, H., & Macready, G. (in press). Different approaches to covariate inclusion in the mixture Rasch model. Educational and Psychological Measurement.
  • Carroll, A. J., Corlett-Rivera, K., Hackman, T., & Zou, J. (2016). E-Book Perceptions and Use in STEM and Non-STEM Disciplines: A Comparative Follow-Up Study. portal: Libraries and the Academy, 16(1), 131-163.
  • Li, Y., Panagiotou, O. A., Black, A., Liao, D., & Wacholder, S. (2016). Multivariate piecewise exponential survival modeling. Biometrics.
  • Jiao, H., Kamata, A., & Xie, C. (2015). A multilevel cross-classified testlet model for complex item and person clustering in item response modeling. In J. Harring, L. Stapleton, & S. Beretvas (Eds.), Advances in multilevel modeling for educational research: Addressing practical issues found in real-world applicationsm. Charlotte, NC: Information Age Publishing.
  • Jiao, H., & Zhang*, Y. (2015). Polytomous multilevel testlet models for testlet-based assessments with complex sampling designs. British Journal of Mathematical and Statistical Psychology1, 65-83. Online first,DOI:10.1111/bmsp.12035.
  • Luo, Y., Jiao, H., & Lissitz, R. W. (2015). An empirical study of the impact of the choice of persistence model in value-added modeling upon teacher effect estimates. In L. A. van der Ark, D. Bolt, W.-C. Wang, J. A. Douglas & S.-M. Chow (Eds.), Quantitative psychology research (pp.133-143). Springer, Switzerland.
  • Wolfe, E. W., Jiao, H., & Song, T. (2015). A family of rater accuracy models. Journal of Applied Measurement16
  • Wolfe, E., Song, T. W., & Jiao, H. (2015). Features of difficult-to-score essays. Assessing Writing. 27, 1-10.
  • Jiao, H., & Lissitz, R. W. (2014). Direct modeling of student growth with multilevel and mixture extensions. In R. W. Lissitz & H. Jiao (Eds.), Value added modeling and growth modeling with particular application to teacher and school effectiveness. Charlotte: Information Age Publishing Inc.
  • Jiao, H., & Chen, Y.-F. (2014). Differential item and testlet functioning. In A. Kunnan (Ed.), The companion to language assessments (pp.1282-1300). John Wiley & Sons, Inc.
  • Chen, Y.-F., & Jiao, H. (2014). Does model misspecification lead to spurious latent classes? An evaluation of model comparison indices. In R. E. Millsap et al. (Eds.), New development in quantitative psychology, Springer Proceedings in Mathematics & Statistics, 66. DOI 10.1007/978-1-4614-9348-8_22, Springer Science +Business Media, New York.
  • Jiao, H., & Zhang, Y. (2014). Polytomous multilevel testlet models for testlet-based assessments with complex sampling designs. British Journal of Mathematical and Statistical Psychology. Advance online publication. DOI:10.1111/bmsp.12035
  • Wolfe, E. W., Jiao, H., & Song, T. (in press). A family of rater accuracy models. Journal of Applied Measurement.
  • Chen, Y.-F. & Jiao, H. (2014). Exploring the utility of background and cognitive variables in explaining latent differential item functioning: An example of the PISA 2009 reading assessment. Educational Assessment19, 77-96.
  • Li, Y. & Lissitz, R. W. (2012). Exploring the full-information bi-factor model in vertical scaling with construct shift. Applied Psychological Measurement, 36, 3-20.
  • Lissitz, R. W., Hou, X., & Slater, S. (2012). The contribution of constructed response items to large scale assessment: measuring and understanding their impact. Journal of Applied Testing Technology, 13, 1-50.
  • Jiao, H., & Chen, Y.-F. (2014). Differential item and testlet functioning. In A. Kunnan (Ed.), The companion to language assessments (pp.1282-1300). John Wiley & Sons, Inc.
  • Jiao, H., Wang, S., & He, W. (2013). Estimation methods for one-parameter testlet models. Journal of Educational Measurement50, 186-203.
  • Wang, S., Jiao, H., & Zhang, L. (2013). Validation of longitudinal achievement constructs of vertically scaled computerized adaptive tests: A multiple-indicator, latent-growth modeling approach. International Journal of Quantitative Research in Education1, 383-407.
  • Tao, J., Xu, B., Shi, N., & Jiao, H. (2013). Refining the two-parameter testlet response model by introducing testlet discrimination parameters. Japanese Psychological Research55, 284-291.
  • Wang, S., McCall, M., Jiao, H., & Harris, G. (2013). Construct validity and measurement invariance of computerized adaptive testing: Application to Measures of Academic Progress (MAP) using confirmatory factor analysis. Journal of Educational and Developmental Psychology3, 88-100.
  • Jiao, H., Macready, G., Liu, J., & Cho, Y. (2012). A mixture Rasch model based computerized adaptive test for latent class identification. Applied Psychological Measurement, 36, 469-493.
  • Li, Y., Jiao, H., & Lissitz, R.W. (2012). Applying multidimensional IRT models in validating test dimensionality: An example of K-12 large-scale science assessment. Journal of Applied Testing Technology, issue 2.
  • Lissitz, R. W. (2012). Standard setting: Past, present and perhaps the future. In Ercikan, K, Simon, M, and Rousseau, M (Eds), Improving large scale education assessment: Theory, issues and practice. Taylor and Francis/Routledge.
  • Lissitz, R. W., & Caliço, T. (2012). Validity is an action verb: Commentary on: Clarifying the consensus definition of validity. Journal of Measurement: Interdisciplinary Research and Perspectives10, 75-79.
  • Schafer, W. D., Lissitz, R. W., Zhu, X., Zhang, Y., Hou, X., & Li, Y. (2012). Evaluating teachers and schools using student growth models. Practical Assessment, Research & Evaluation, 17(17), 2.
  • Jiao, H., Lissitz, R. W., Macready, G., Wang, S. & Liang, S. (2011) Comparing the use of mixture Rasch modeling and judgmental procedures for standard setting. Psychological Test and Assessment Modeling, 53, 499-522.
  • Lissitz, R. W., & Li, F. F. (2011). Standard setting in complex performance assessments: An approach aligned with cognitive diagnostic models. Psychological Test and Assessment Modeling53, 461-485.
  • Templin, J., & Jiao, H. (2011). Applying model-based approaches to identify performance categories. In G. Cizek (Ed.), Setting performance standards: Foundations, methods, and innovations (pp. 379-397). New York, NY: Routlege.
  • Fan, W., & Lissitz, R. W.  (2010). A multilevel analysis of students and schools on high school graduation exam: A case of Maryland. International Journal of Applied Educational Studies9, 1-18.
  • Jiao, H., & Wang, S. (2010). A multifaceted approach to investigating the equivalence between computer-based and paper-and-pencil assessments: An example of Reading Diagnostics. International Journal of Learning Technology, 5, 264-288.
  • Lissitz, R. W., & Wei, Hua (2008). Consistency of Standard Setting in an Augmented State Testing System. Educational Measurement: Issues and Practice, 27, 46-56.
  • Lissitz, R. W., & Samuelsen, K. (2007). A suggested change in terminology and emphasis regarding validity and education. Educational Researcher, 36, 437-448.
  • Lissitz, R. W., & Samuelsen, K. (2007). Further clarification regarding validity and education. Educational Researcher, 36, 482-484.
  • Schafer, W. D., Liu, M., & Wang, H. (2007). Content and Grade Trends in State Assessments and NAEPPractical Assessment Research & Evaluation , 12.
  • Lissitz, R., Doran, H., Schafer, W., & Wilhoft, J.(2006). Growth modeling, value added modeling, and linking: An introduction (2006). In Lissitz, R. W. (Ed.), Longitudinal and value-added models of student performance (pp. 1-46). Maple Grove, MN: JAM Press.
  • Schafer, W. (2006). Growth Scales as Alternative to Vertical ScalePractical Assessment Research & Evaluation 11.
  • Schafer, W., & Twing, J. (2006). Growth scales and pathways. In Lissitz, R. W. (Ed.), Longitudinal and value-added models of student performance (pp. 321-345). Maple Grove, MN: JAM Press.
  • Walston, J., Lissitz, R. W., & Rudner, L. (2006). The Influence of Web-based Questionnaire Presentation Variations on Survey Cooperation and Perceptions of Survey Quality. The Journal of Official Statistics, 22, 271-291.
  • Li, Y., & Schafer, W. (2005). Increasing the homogeneity of CAT's item-exposure rates by minimizing or maximizing varied target functions while assembling shadow tests. Journal of Educational Measurement 42, 245-269.
  • Schafer, W. (2005). Technical documentation for alternate assessments (2005). Practical Assessment Research & Evaluation 10.
  • Shafer, W., Gagne, P. & Lissitz, R. (2005). Resistance to Confounding Style and Content in Scoring Constructed Response Items. Educational Measurement: Issues and Practice24, 22-28.