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 Measurement75, 260-283.

> Sweet, T. M. (2015). Inrating covariates into block models. Journal of Behavioral and Educational Statistics. DOI: 10.3102/1076998615606110.
> Harring, J. R., Weiss, B. A., & Li, M. (2015). Assessing spurious interaction effects in structural equation modeling: A cautionary note. Educational and Psychological Measurement.
> Mao, X., Harring, J. R., & Hancock, G. R. (2015). A note on the specification of error structures in latent interaction models. Educational and Psychological Measurement, 75, 5-21.
> Hancock, G. R., & Schoonen, R. (2015). Structural equation modeling: Possibilities for language learning researchers. Language Learning, 65, 158-182.
> Mueller, R. O., & Hancock, G. R. (2015). Factor analysis and latent structure: Confirmatory factor analysis. In J. D. Wright (Ed.), International encyclopedia of the social and behavioral sciences (2nd ed.) (pp. 686-690). Oxford, England: Pergamon.
> Preacher, K. J., & Hancock, G. R. (2015). Meaningful aspects of change as novel random coefficients: A general method for reparameterizing longitudinal models. Psychological Methods, 20, 84-101.
> Harring, J. R., & Liu, J. (2015). A comparison of estimation methods for nonlinear mixed-effects models under model misspecification and data sparseness: A simulation study. Journal of Modern Applied Statistical Methods1, 1-28.


> 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

> Kang, Y., & Harring, J. R.Li, M. (2014). Reexamining the impact of non-normality in two-group comparison procedures. The Journal of Experimental Education, DOI: 10.1080/00220973.2013.876605.

> Li, M.Harring, J. R., & Macready, G. B. (2014). Investigating the feasibility of using Mplus in the estimation of growth mixture models. Journal of Modern Applied Statistical Methods, 13, 484-513.

> Harring, J. R., & Blozis, S. A. (2014). Fitting correlated residual error structures in nonlinear mixed-effects models using SAS PROC NLMIXED. Behavior Research Methods46, 472-484.

> 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 Assessment, 19, 77-96.

> Harring, J. R. (2014). A spline model for latent variables. Educational and Psychological Measurement. DOI: 10.1177/0013164413504295.

> Jiao, H., & Zhang, Y. (2014). A polytomous multilevel testlet model for testlet-based assessments with complex sampling designs. British Journal of Mathematical and Statistical Psychology. DOI: 10.1111/bmsp.12035

> Lissitz, R. W., & Jiao, H. (Editors, 2014) Value added modeling and growth modeling with particular application to teacher and school effectiveness. Charlotte, NC: Information Age Publishing, Inc.

> Liu, M., & Hancock, G. R. (2014). Unrestricted mixture models for class identification in growth mixture modeling. Educational and Psychological Measurement, 74, 557-584.

> McNeish, D. M. (2014). Modeling sparsely clustered data: Design-based, model-based, and single-level methods. Psychological Methods. DOI: 10.1037/met0000024

> Patton, J. M., & Cheng, Y. (2014). Effects of item calibration error on applications of item response theory. In Y. Cheng & H.-H. Chang (Eds.), Advancing methodologies to support both summative and formative assessments. Charlotte, NC: Information Age Publishing.

> Sweet, T. M., Thomas, A. C., & Junker, B. W. (2014). Hierarchical mixed membership stochastic block models for multiple networks and experimental interventions. In E. Airoldi, D. Blei, E. Erosheva, & S. Fienberg (Eds.), Handbook on mixed membership models and their applications. Boca Raton, FL: Chapman & Hall/CRC.

> Yuan, K.-H., Cheng, Y., & Patton, J. M. (2014). Information matrices and standard errors for MLEs of item parameters in IRT. Psychometrika, 79, 232-254.

> 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 developments in quantitative psychology, 66, 345-355, Springer Science +Business Media, New York.

> 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.

> Kang. Y.Harring, J. R., & Li, M. (2014). Reexamining the impact of nonnormality in two-group comparison procedures. The Journal of Experimental Education. DOI:10.1080/00220973.2013.876605

> McNeish, D. M. (2014). Analyzing clustered data with OLS regression: The effect of a hierarchical data structure. Multiple Linear Regression Viewpoints40, 11-16.

> Patton, J. M., Cheng, Y., Yuan, K.-H., & Diao, Q. (2014). Bootstrap standard errors for maximum likelihood ability estimates when item parameters are unknown. Educational and Psychological Measurement, 74, 697-712.

> Zopluoglu, C., Harring, J. R., & Kohli, N. (2014). FitPMM: An R routine to fit finite mixtures of piecewise mixed-effects models with unknown random knots. Applied Psychological Measurement. DOI: 10.1177/0146621614540482.

> Sweet, T. M., Thomas, A. C., & Junker, B. W. (2013). Hierarchical social network models for education interventions. Journal of Educational and Behavioral Statistics38, 295-318. doi: 10.3102/1076998612458702

> Kohli, N., Harring, J. R., & Hancock, G. R. (2013). Piecewise linear-linear latent growth mixture models with unknown knots. Educational and Psychological Measurement, 73, 935-955.

> Stapleton, L. M. (2013). Using multilevel structural equation modeling techniques with complex sample data. In G. R. Hancock & R. O. Mueller (Eds), Structural equation modeling: A second course (2nd ed.) (pp. 521-562). Charlotte, NC: Information Age Publishing.

> Kohli, N., & Harring, J. R. (2013). Modeling growth in latent variables using a piecewise function.Multivariate Behavioral Research48, 370-397.

> Hancock, G. R.Harring, J. R., & Lawrence, F. R. (2013). Using latent growth models to evaluate longitudinal change. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed.). Charlotte, NC: Information Age Publishing, Inc.

> Hancock, G. R., & French, B. F. (2013). Power analysis in covariance structure models. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed.) (pp. 117-159). Charlotte, NC: Information Age Publishing, Inc.

> Jiao, H., Wang, S., & He, W. (2013). Estimation methods for one-parameter testlet models. Journal of Educational Measurement50,186-203.

> 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.

> Lissitz, R. W. (Ed.) (2013). Informing the practice of teaching using formative and interim assessment: A systems approach. Charlotte, NC: Information Age Publishing, Inc.

> Hancock, G. R., Mao, X., & Kher, H. (2013). On latent growth models for composites and their constituents. Multivariate Behavioral Research, 48, 619-638.

> Hancock, G. R., & Mueller, R. O. (Eds.) (2013). Structural equation modeling: A second course (2nd ed.). Charlotte, NC: Information Age Publishing, Inc.

> 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. & CaliƧo, 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.

> Harring, J. R., Weiss, B. A., & Hsu, J. C. (2012). A comparison of methods for estimating quadratic effects in structural equation models. Psychological Methods, 17, 193-214.

> 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.

> Jiao, H., Macready, G., Liu, J., & Cho, Y. (2012). A mixture Rasch model-based computerized adaptive test for latent class identification. Applied Psychological Measurement. First published online on 29 June 2012 as DOI: 10.1177/0146621612450068.

> 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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