EDMS Less Recent Publications (Faculty and Students)
> Cheng, Y., Patton, J. M., & Shao, C. (2015). a-Stratified computerized adaptive testing in the presence of calibration error. Educational and Psychological Measurement, 75, 260-283.
> Sweet, T. M. (2015). Inrating covariates into block models. Journal of Behavioral and Educational Statistics. DOI: 10.3102/1076998615606110.
> Chen, J., Choi, J., Weiss, B. A., & Stapleton, L. (2014). An empirical evaluation of mediation effect analysis with manifest and latent variables using Markov chain Monte Carlo and alternative estimation methods. Structural Equation Modeling: A Multidisciplinary Journal,21, 253-262. doi: 10.1080/10705511.2014.882688
> Schafer, W. D., Lissitz, R. W., Zhu, X., Zhang, Y., & Li, Y. (2012) Evaluating teachers and schools using student growth models. PAREONLINE.net/pdf/v17n17.pdf
> Li, Y., Jiao, H., & Lissitz, R. W. (2012). Applying multidimensional item response theory models in validating test dimensionality: An example of K-12 large-scale science assessment. Journal of Applied Testing Technology, 13(2).
> 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 Perspectives, 10(1).
> Jiao, H., & Lissitz, R. W. (2012). Computer-based testing in K-12 state assessments. In H. Jiao & R. W. Lissitz (Eds.), Computers and their impact on state assessment: Recent history and predictions for the future. Charlotte, NC: Information Age Publishing, Inc.
> Lissitz, R. W., Hou, X., & Slater, S. (2012). The contribution of constructed response items to large scale assessment: meaning and understanding their impact. Journal of Applied Testing Technology, 13, 1-52.
> 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. (2012). Standard setting: Past, present, and perhaps the future. In K. Ercikan, M. Simon, & M. Rousseau (Eds.), Improving large scale education assessment: Theory, issues, and practice. New York: Taylor and Francis/Routledge.
> Harring, J. R., & Hancock, G. R. (Eds.) (2012). Advances in longitudinal methods in the social and behavioral sciences. Charlotte, NC: Information Age Publishing, Inc.
> Harring, J. R. (2012). Finite mixtures of nonlinear mixed effects models. In J. R. Harring & G. R. Hancock (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 159-192). Charlotte, NC: Information Age Publishing, Inc.
> Dardick, W., & Harring, J. R. (2012). Automated path tracing for general linear models. Multiple Linear Regression Viewpoints, 8, 38-50. Accompanying SAS Program for APT paper.
> Mislevy, J. L., Rupp, A. A., & Harring, J. R. (2012). Detecting local item dependence in polytomous adaptive data. Journal of Educational Measurement, 49, 127-147.
> Harring, J. R., Kohli, N., Silverman, R., & Speece, D. L. (2012). Fitting a second-order conditionally linear mixed effects model as an SEM in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 19, 118-136.
> Mislevy, J., Stapleton, L. M., & Rupp, A. (2012). Sampling and complex test designs. In M. Simon, K. Ercikan, & M. Rousseau (Eds). Handbook on large-scale assessments (pp. 207-237). New York, NY: Routledge.
> Stapleton, L. M. (2012). Evaluation of conditional weight approximations for two-level models. Communications in Statistics: Simulation and Computation, 41, 182-204.
> Pituch, K. A., & Stapleton, L. M. (2012) Distinguishing between cross- and cluster-level mediation processes in the cluster randomized trial. Sociological Methods & Research, 41, 630-670.
> Jiao, H., Kamata, A., Wang, S., & Jin, Y. (2012). A multilevel testlet model for dual local dependence. Journal of Educational Measurement, 49, 82-100.
> Fan, W., & Hancock, G. R. (2012). Robust means modeling: An alternative to hypothesis testing of independent means under variance heterogeneity and nonnormality. Journal of Educational and Behavioral Statistics, 37, 137-156.
> Hancock, G. R., & Liu, M. (2012). Bootstrapping standard errors and data-model fit statistics. In R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 296-306). New York: Guilford Press.
> Stemler, S. E., Elliott, J .G., McNeish, D., Grigorenko, E. L., & Sternberg, R. J. (2012). Examining the construct and cross-cultural validity of the Teaching Excellence Rating Scale (TERS). The International Journal of Educational and Psychological Assessment, 9(2), 121-138.
> Zieffler, A. S., Harring, J. R., & Long, J. D. (2011). Comparing groups: Randomization and bootstrap methods using R. New York: Wiley.
> Jiao, H., Lissitz, R., Macready, G., Wang, S., & Liang, S. (2011). Exploring using the Mixture Rasch Model for standard setting. Psychological Testing and Assessment Modeling, 53, 499-522.
> Jiao, H., Liu, J., Haynie, K., Woo, A., & Gorham, J. (2011). Comparison between dichotomous and polytomous scoring of innovative items in a large-scale computerized adaptive test. Educational and Psychological Measurement. First published online on November 8, 2011 as doi:10.1177/0013164411422903.
> 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.
> Rupp, A. A., Templin, J., & Henson, R. J. (2010). Diagnostic measurement: Theory, methods, and applications. New York: Guilford Press
> Hancock, G. R., & Mueller, R. O. (Eds.). (2010). The reviewer's guide to quantitative methods in the social sciences. New York: Taylor & Francis.
> Roberts, J. S., Rost, J., & Macready, G. B. (2010). MIXUM: An unfolding mixture model to explore the latitude of acceptance concept in attitude measurement. In S. Embretson (Ed.), Measuring psychological constructs: Advances in model-based approaches. Washington, D.C.: American Psychological Association.
> Cudeck, R., & Harring, J. R. (2010). Developing a random coefficient model for nonlinear repeated measures data. In S.-M. Chow, E. Ferrer, & F. Hsieh (Eds.), Statistical methods for modeling human dynamics: An interdisciplinary dialogue. New York: Routledge.
> Rupp, A. A., Gushta, M., Mislevy, R. J., & Shaffer, D. W. (2010). Evidence-centered design of epistemic games: Measurement principles for complex learning environments. Journal of Technology, Learning, and Assessment, 8(4). Available online at http://escholarship.bc.edu/jtla/vol8/4
> Mislevy, R. J., Behrens, J. T., Bennett, R. E., Demark, S. F., Frezzo, D. C., Levy, R., Robinson, D. H., Rutstein, D. W., Shute, V. J., Stanley, K., & Winters, F. I. (2010). On the roles of external knowledge representations in assessment design. Journal of Technology, Learning, and Assessment, 8(2). http://escholarship.bc.edu/jtla/vol8/2
> Lissitz, R. W. (Ed.) (2009). The concept of validity: Revisions, new directions and applications. Charlotte, NC: Information Age Publishing Inc.
> Schafer, W. D., & Lissitz, R. W. (Eds.) (2009). Assessment for alternate achievement standards: Current practices and future directions. Baltimore, MD: Brooks Publishing.
> Choi, J., Harring, J. R., & Hancock, G. R. (2009). Latent growth modeling for logistic response functions. Multivariate Behavioral Research, 44, 620-645.
> Mislevy, R. J., & Yin, C. (2009). If language is a complex adaptive system, what is language testing? Language Learning (special issue: 2009, Volume 59, Supplement 1).
> Levy, R., Mislevy, R. J., & Sinharay, S. (2009). Posterior predictive model checking for multidimensionality in item response theory. Applied Psychological Measurement, 33, 519-537.
> Frezzo, D. C., Behrens, J. T., & Mislevy, R. J. (2009). Activity theory and assessment theory in the design and understanding of the Packet Tracer ecosystem. The International Journal of Learning and Media, 2. http://ijlm.net/knowinganddoing/10.1162/ijlm.2009.0015
> von Davier, M., Gonzalez, E., & Mislevy, R. J. (2009). What are plausible values and why are they useful? IERI Monograph Series, Volume 2 (pp. 9-36). Princeton, NJ: IEA-ETS Research Institute.
> Mislevy, R. J. (2009). Validity from the perspective of model-based reasoning. In R. L. Lissitz (Ed.), The concept of validity: Revisions, new directions and applications (pp. 83-108). Charlotte, NC: Information Age Publishing.
> Harring, J. R. (2009). A nonlinear mixed effects model for latent variables. Journal of Educational and Behavioral Statistics, 34, 293-318.
> Hancock, G. R., Stapleton, L. M., & Arnold-Berkovits, I. (2009). The tenuousness of invariance tests within multisample covariance and mean structure models. In T. Teo & M. S. Khine (Eds.), Structural equation modeling: Concepts and applications in educational research. Rotterdam, Netherlands: Sense Publishers.
> Wang, S., & Jiao, H. (2009). Construct equivalence across grades in a vertical scale for a K-12 large-scale reading assessment. Educational and Psychological Measurement, 69, 760-777.
> Frey, A., Hartig, J., & Rupp, A. A. (2009). An NCME instructional module on booklet designs in large-scale assessments of student achievement: Theory and practice. Educational Measurement: Issues and Practice, 28, 39-53.
> Wang, S., Jiao, H., Young, M. J., Brooks, T., & Olson, J. (2008). Comparability of computer-based and paper-and-pencil testing in K-12 reading assessments: A meta-analysis of testing mode effects. Educational and Psychological Measurement, 68, 5-24.
> Cudeck, R., Harring, J. R., & du Toit, S. H. C. (2009). Marginal maximum likelihood estimation of a latent variable model with interaction. Journal of Educational and Behavioral Statistics, 34, 131-144.
> Rupp, A. A., & Templin, J. (2008). The effects of Q-matrix misspecification on parameter estimates and classification accuracy in the DINA model. Educational and Psychological Measurement, 68, 78-96.
> Blozis, S. A., Harring, J. R., & Mels, G. (2008). Using LISREL to fit nonlinear latent curve models. Structural Equation Modeling. A Multidisciplinary Journal, 15, 346-369.
> Jiao, H., Wang, S., & Kamata, A. (2007). Modeling local item dependence with the hierarchical generalized linear model. In E. V. Smith & R. M. Smith (Eds.), Rasch measurement: Advanced and specialized applications. Maple Grove, MN: JAM press.
> Hancock, G. R., & Samuelsen, K. M. (Eds.). (2008). Advances in latent variable mixture models. Charlotte, NC: Information Age Publishing, Inc.
> Mueller, R. O., & Hancock, G. R. (2008). Best practices in structural equation modeling. In J. W. Osborne (Ed.), Best practices in quantitative methods (pp. 488-508). Thousand Oaks, CA: Sage Publications.
> Mislevy, R. J. (2007). Validity by design. Educational Researcher, 36, 463-469.
> Dayton, C. M., & Macready, G. B. (2007). Latent class analysis in psychometrics. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics (pp. 421-446). Elsevier.
> Lissitz, R. W. (Ed.). (2007). Assessing and modeling cognitive development in school: Intellectual growth and standard setting. Maple Grove, MN: JAM press.
> Rupp, A. A., & Mislevy, R. J. (2007). Cognitive foundations of structured item response theory models. In J. Leighton & M. Gierl (Eds.), Cognitive diagnostic assessment in education: Theory and applications (pp. 205-241). Cambridge: Cambridge University Press.
> Cudeck, R., & Harring, J. R. (2007). The analysis of nonlinear patterns of change with random coefficient models. Annual Review of Psychology, 58, 615-637.
> Levy, R., & Hancock, G. R. (2007). A framework of statistical tests for comparing mean and covariance structure models. Multivariate Behavioral Research, 42, 33-66.
> Schafer, W. D., Liu, M., & Wang, H-F. (2007). Content and grade trends in state assessments and NAEP. Practical Assessment, Research & Evaluation, 12(9). http://pareonline.net/pdf/v12n9.pdf
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