Bibliography for Russell Almond

• R.G. Almond, D. Yan, and L.A. Hemat. Parameter recovery studies with a diagnostic Bayesian network model. Behaviormetrika, Under Review. Under Review.
• Russell G. Almond, Robert J. Mislevy, and Duanli Yan. Using anchor sets to identify scale and location of latent variables. Draft in preparation. To be presented at NCME 2007, Draft.
• Lisa A. Hemat and Russell G. Almond. Irt versus bayes nets: Proficiency estimates and parameter recovery. Research report, Educational Testing Serivce, Draft. Under Review for Publication.
• Russell G. Almond, Linda S. Steinberg, and Sinharay. Method and system for calibrating evidence models. U.S. Patent Application, Pending. Attorney Docket No. 122467.03601, Pepper Hamilton.
• Linda S. Steinberg, Robert J. Mislevy, and Russell G. Almond. Portal assessment design system for educational testing. U.S. Patent application, Pending. Attorney Docket No. 246400.0159, Wilmer, Culter and Pickering.
• Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, David M. Williamson, and Duanli Yan. Bayesian Networks in Educational Assessment. Springer, In Preparation. Draft in preparation.
• R. G. Almond. Estimating parameters of periodic assessment models. Draft manuscript submitted to ETS Research Memorandum series, Revision: 1.15 Date: 2008/01/28, 2008.
• Russell G. Almond, Valerie J. Shute, Jody S. Underwood, and Juan-Diego Zapata-Rivera. Bayesian networks: A teacher's view. International Journal of Approximate Reasoning, page In Press, 2008. (doi:10.1016/j.ijar.2008.04.011)
• Valerie J. Shute, Eric G. Hansen, and Russell G. Almond. You can't fatten a hog by weighing it - or can you? evaluating an assessment for learning system called aced. International Journal of Artificial Intelligence in Education, 18(4):In Press, 2008.
• Linda van der Gaag and Russell Almond. Preface to special issue on bayesian applications. International Journal of Approximate Reasoning, 2008. (doi:10.1016/j.ijar.2008.06.001)
• Russell G. Almond. Cognitive modeling to represent growth (learning) using Markov decision processes. Technology, Instruction, Cognition and Learning (TICL), 5:313–324, 2007.
• Russell G. Almond. `I can name that Bayesian network in two matrixes'. In K. B. Laskey, S. M. Mahoney, and J. A. Goldsmith, editors, Proceedings of the 5th UAI Bayesian Modelling Applications Workshop, volume 268, page (online). CEUR Workshop Proceedings, 2007. Held in conjunction with the 23rd Conference on Uncertainty in Artificial Intelligence (UAI-2007).
• Russell G. Almond. An illustration of the use of Markov decision processes to represent student growth (learning). Research Report RR-07-40, ETS, 2007.
• R.G. Almond, R.J. Mislevy, D.M. Williamson, and D. Yan. Bayesian networks in educational assessment. Tutorial Presented at Annual meeting of the National Council on Measurement in Education (NCME), 2007.
• Russell G. Almond, Louis V. DiBello, Brad Moulder, and Juan-Diego Zapata-Rivera. Modeling diagnostic assessment with Bayesian networks. Journal of Educational Measurement, 44(4):341–359, 2007.
• Russell G. Almond, Robert J. Mislevy, and Duanli Yan. Using anchor sets to identify scale and location of latent variables. Paper presented at Annual meeting of the National Council on Measurement in Education (NCME), 2007.
• V. J. Shute, E. G. Hansen, and R. G. Almond. An assessment for learning system called ACED: The impact of feedback and adaptivity on learning. Research Report RR-07-26, ETS, 2007.
• V. J. Shute, E. G. Hansen, and R. G. Almond. Evaluation of ACED: The impact of feedback and adaptivity on learning. In Proceedings of the AIED 2007 (Artificial Intelligence and Education) conference, Los Angeles, CA, July 2007.
• Valerie J. Shute, E. G. Hansen, and R.G. Almond. Towards accessible test delivery systems: A small-scale evaluation of a prototype system for improving the accessibility of mathematics tests by individuals with visual disabilities. Paper Presented at Annual meeting of the National Council on Measurement in Education (NCME), 2007.
• Sandip Sinharay and Russell G. Almond. Assessing fit of cognitively diagnostic models—a case study. Educational and Psychological Measurement, 67(2):239–257, 2007.
• Russell G. Almond. Building Bayesian network proficiency models from factor analysis results. Draft Report. Revision 1.8, October 10, 2006, 2006.
• Russell G. Almond. Building self-efficacy: How mentorpower works. Talk given as part of 2006 mentor training session., October 2006.
• Russell G. Almond. Cognitive modeling to represent growth (learning) using Markov decision processes. In Annual meeting of the American Educational Research Association (AERA), 2006.
• Russell G. Almond. XML for data analysts. Presentation given as part of the Tukey Seminar on Data Analysis at ETS., December 2006.
• Russell G. Almond and Richard Roberts. Bayesian scoring for situational judgment tests. White Paper for SJT project, October 2006.
• R.G. Almond, R.J. Mislevy, D.M. Williamson, and D. Yan. Bayesian networks in educational assessment. Tutorial Presented at Annual meeting of the National Council on Measurement in Education (NCME), 2006.
• Russell G. Almond, Joris Mulder, Lisa A. Hemat, and Duanli Yan. Models for local dependence among observable outcome variables. ETS Research Report RR-06-36, Educational Testing Service, 2006.
• Russell G. Almond, Valerie J. Shute, Jody S. Underwood, and Juan-Diego Zapata-Rivera. Bayesian networks: A teacher's view. In Proceedings of the 4th Bayesian Modelling Applications Workshop, 2006. Held in conjunction with the 22nd Conference on Uncertainty in Artificial Intelligence (UAI-2006).
• Russell G. Almond, Duanli Yan, Alexander Matukhin, and Denise Chang. Statshop testing. ETS RM 06-04, Educational Testing Service, 2006.
• Robert J. Mislevy, Linda S. Steinberg, Russell G. Almond, and Janice F. Lukas. Concepts, terminology and basic models of evidence-centered design. In David M. Williamson, Robert J. Mislevy, and Isaac I. Bejar, editors, Automated Scoring of Complex Tasks in Computer-Based Testing, pages 15–47. Lawrence Erlbaum Associates, 2006.
• David M. Williamson, Russell G. Almond, Robert J. Mislevy, and Roy Levy. An application of Bayesian networks in automated scoring of computerized simulation tasks. In David M. Williamson, Robert J. Mislevy, and Isaac I. Bejar, editors, Automated Scoring of Complex Tasks in Computer-Based Testing, pages 201–257. Lawrence Erlbaum Associates, 2006.
• Duanli Yan, Russell G. Almond, Robert J. Mislevy, and David M. Williamson. Bayesian network applications. Presentation at National Tsing-Huan University, November 2006. Taipei, Taiwan.
• Duanli Yan, Russell G. Almond, Robert J. Mislevy, and David M. Williamson. Bayesian network applications. Presentation at Institute of Information Science, November 2006. Academica Sinica.
• Feifei Ye, Russell Almond, and Robert Mislevy. Diagnostic assessment of student learning of patterns in 8th grade math classes: an evidence-centered design approach. Paper presented at Annual meeting of the National Council on Measurement in Education (NCME), 2006.
• Russell G. Almond and Lisa A. Hemat. ICT model repository. ETS Internal Website, 2005.
• Russell G. Almond and Joris Mulder. Models for local dependence among observable outcome variables. In Third Annual Bayesian Application Workshop at the 2005 Uncertainty in Artificial Intelligence Application Workshop, 2005.
• Lisa Hemat, Russell Almond, and Jan Lukas. StatShop Documentation. Educational Testing Service, Version 2.0 edition, December 2005. URL is internal to ETS.
• Duanli Yan and Russell Almond. Using ROC curves to improve evidence for diagnostic scoring. In Third Annual Bayesian Application Workshop at the 2005 Uncertainty in Artificial Intelligence Application Workshop, 2005.
• Duanli Yan, Russell Almond, and Lisa Hemat. Bayesian network model for the ICT literacy assessment. Research Report Pending, Educational Testing Service, 2005. Under Review.
• R.G. Almond, L.A. Hemat, J.D. Zapata-Rivera, and D.M. Williamson. Procrustean method for reconciling expert opinion. In 2nd Bayesian Application Workshop at Uncertainty in Artificial Intelligence, 2004.
• R.G. Almond, R.J. Mislevy, D.M. Williamson, and D. Yan. Bayesian networks in educational assessment. Tutorial Presented at National Council on Measurement in Education, 2004.
• I. R. Katz, D. M. Williamson, H. L. Nadelman, I. Kirsch, R. G. Almond, P. L. Cooper, M. L. Redman, and D. Zapata. Assessing information and communications technology literacy for higher education. In International Association for Educational Assessment, 2004.
• R.J. Mislevy, R.G. Almond, and J. Lukas. A brief introduction to evidence-centered design. CSE Technical Report 632, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 2004. Also ETS Research Report RR-03-16.
• Sandip Sinharay, Russell G. Almond, and Duanli Yan. Assessing fit of models with discrete proficiency variables in educational assessment. Research Report RR-04-07, Educational Testing Service, 2004.
• David M. Williamson, Robert J. Mislevy, and Russell G. Almond. Evidence-centered design for certification and licensure. CLEAR Exam Review, 14:14–18, 2004.
• Duanli Yan, Russell G. Almond, and Robert J. Mislevy. Comparison of two models for cognitive diagnosis. Research Report RR-04-02, Educational Testing Service, 2004.
• F. Ye, R.G. Almond, R.J. Mislevy, and D. Yan. Sensitivity to prior distributions in calibration of a Bayesian network. In Annual meeting of the National Council on Measurement in Education, 2004.
• Russell Almond, David Williamson, and Duanli Yan. Testing tests: Quality control for assessment design. In 1st Bayesian Application Workshop at Uncertainty in Artificial Intelligence, 2003.
• L.T. Frase, M. Chudorow, R.G. Almond, J. Burstein, K. Kukich, R.J. Mislevy, L.S. Steinberg, and K. Singley. Technology and assessment. In H.F. O'Neil and R. Perez, editors, Technology applications in assessment: A learning view, pages 213–244. Erlbaum, 2003.
• R. J. Mislevy, L.S. Steinberg, R.G. Almond, G. Haertel, and W. Penuel. Leverage points for improving educational assessment. In B. Means and G. Haertel, editors, Evaluating the Effects of Technology in Education, pages 149–180. Earlbaum, 2003.
• Robert J. Mislevy, Linda S. Steinberg, and Russell G. Almond. On the structure of educational assessment (with discussion). Measurement: Interdisciplinary Research and Perspective, 1(1):3–62, 2003.
• L.S. Steinberg, R.G. Almond, A.B. Baird, C. Cahallan, H. Chernick, L.V. Dibello, A.C.H. Kindfield, R.J. Mislevy, D. Senturk, and D. Yan. Introduction to the biomass project: An illustration of evidence-centered assessment design and delivery capability. CSE Report 609, National Center for Research on Evaluation, Standards, and Student Testing (CRESST)., 2003.
• Duanli Yan, Robert J. Mislevy, and Russell G. Almond. Design and analysis in a cognitive assessment. Research Report RR-03-32, Educational Testing Service, 2003.
• Russell G Almond. Using evidence models to aggregate information. White Paper developed for ONR call for suggestions about the future of assessment., 2002.
• R.G. Almond, A. Matukhin, L. Steinberg, S. Sinharay, D.M. Williamson, and D. Yan. A framework for calibrating evidence models. Unpublished StatShop documentation, 2002.
• R.G. Almond, A. Matukhin, L. Steinberg, D.M. Williamson, and D Yan. A framework for evidence accumulation. Unpublished StatShop design documention, 2002.
• R.G. Almond, R.J. Mislevy, D.M. Williamson, and D. Yan. Graphical models in educational assessment. Tutorial Presented at National Council on Measurement in Education, 2002.
• Russell G. Almond, Linda S. Steinberg, and Robert J. Mislevy. Enhancing the design and delivery of assessment systems: A four-process architecture. Journal of Technology, Learning, and Assessment, 1:(online), 2002.
• Russell G. Almond, Linda S. Steinberg, and Robert J. Mislevy. A framework for reusing assessment components. In H. Yanai, Okada. A., K. Shigemasu, Y. Kano, and J. J. Meulman, editors, New Developments in Psychometrics, pages 281–288. Springer, 2002.
• Robert J. Mislevy, Russell G. Almond, Lou DiBello, Frank Jenkins, Linda S. Steinberg, Duanli Yan, and Deniz Senturk. Modeling conditional probabilities in complex educational assessments. CSE Technical Report 580, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 2002. Numerical example for testing Ergo.
• Robert J. Mislevy, Russell G. Almond, and Linda S. Steinberg. Design and analysis in a task-based language assessment. Language Testing, 19(4):477–496, 2002.
• Robert J. Mislevy, Linda S. Steinberg, and Russell G. Almond. On the roles of task model variables in assessment design. In S. Irvine and P. Kyllonen, editors, Generating items for cognitive tests: Theory and practice, pages 97–128. Erlbaum, 2002.
• R. G. Almond. Question and test interoperability in the new millenium. In International Meeting of the Psychometric Society, 2001.
• R.G. Almond, L. Dibello, F. Jenkins, R.J. Mislevy, D. Senturk, L.S. Steinberg, and D. Yan. Models for conditional probability tables in educational assessment. In T. Jaakkola and T. Richardson, editors, Artificial Intelligence and Statistics 2001, pages 137–143. Morgan Kaufmann, 2001.
• R.G. Almond, L.S. Steinberg, and R.J. Mislevy. The four process assessment delivery architecture. In Cognition and Assessment: Theory to Practice. University of Maryland, August 2001.
• R.J. Mislevy, L.S. Steinberg, F.J. Breyer, R.G. Almond, and L. Johnson. Making sense of data from complex assessments. CSE Technical Report 538, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 2001.
• C. Smythe and R.G. Almond. The QTI framework for sharing assessment data and results. In International Meeting of the Psychometric Society, 2001.
• R.G. Almond, C. Lewis, J.W. Tukey, and D. Yan. Displays for comparing a given state to many others. The American Statistician, 54:89–93, 2000.
• David M. Williamson, Robert J. Mislevy, and Russell G. Almond. Model criticism of Bayesian networks with latent variables. In C. Boutilier and M. Goldszmidt, editors, Uncertainty in Artificial Intelligence 16, pages 634–643. Morgan Kaufmann, 2000.
• Russell G. Almond. Undoing statistical advice. In D.J. Hand, J.N. Kok, and M.R Berthold, editors, Advances in Intelligence Data Analysis, pages 357–368. Springer, 1999.
• Russell G. Almond and Robert J. Mislevy. Graphical models and computerized adaptive testing. Applied Psychological Measurement, 23:223–238, 1999.
• R.G. Almond, E. Herskovits, R.J. Mislevy, and L.S. Steinberg. Transfer of information between system and evidence models. In D. Heckerman and J. Whittaker, editors, Artificial Intelligence and Statistics 99, pages 181–186, 1999.
• F.J. Breyer, R.J. Mislevy, L.S. Steinberg, and R.G. Almond. Designing technology-based assessments: It's the evidence for the inferences that are important. In Annual Convention of the Society for Industrial Organizational Psychology, 1999.
• R.J. Mislevy, R.G. Almond, and L.S. Steinberg. Evidentiary considerations in performance assessment. In Annual Meeting of the National Council on Measurement in Education, 1999.
• R.J. Mislevy, L.S. Steinberg, F.J. Breyer, R.G. Almond, and L. Johnson. A cognitive task analysis, with implications for designing a simulation-based assessment system. Computers and Human Behavior, 15:29–42, 1999.
• Robert J. Mislevy, Russell G. Almond, Duanli Yan, and Linda S. Steinberg. Bayes nets in educational assessment: Where the numbers come from. In K. B. Laskey and H. Prade, editors, Uncertainty in Artificial Intelligence '99, pages 437–446. Morgan-Kaufmann, 1999.
• R.G. Almond, R.J. Miselvy, and L.S. Steinberg. A multivariate frameork for educational testing. In Taipei Interanational Statistics Symposium, 1998.
• R.J. Mislevy, R.G. Almond, and L.S. Steinberg. A note on knowledge-based model construction in educational assessment. CSE Technical Report 480, The National Center for Research on Evaluation, Standards, Student Testing (CRESST), 1998. Numerical example for testing Ergo.
• R.J. Mislevy, L.S. Steinberg, and R. G. Almond. Evidence-centered assessment design. In Annual Conference of the National Center for Research on Evaluation, Standards, Student Testing (CRESST), 1998.
• D. Madigan, K. Mosurski, and R.G. Almond. Graphical explanation in belief networks. Journal of Computational Graphics and Statistics, 6(2):160–181, 1997.
• H.F. Martz and R.G. Almond. Using higher-level failure data in fault tree quantification. Reliability Engineering and Safety Systems, 56:29–42, 1997.
• R.J. Mislevy, R.G. Almond, and L.S. Steinberg. Task design, student modeling, and evidentiary reasoning in complex educational assessments. In Section on Bayesian Statistical Science at the Annual Meeting of the American Statistical Association, 1997.
• Russell G. Almond. Intelligent support of secondary data analysis. In 6th International Workshop on AI and Statistics,, 1996.
• R.G. Almond. Fuzzy logic: Better science or better engineering. Technometrics, 37(147):267–270, 1995. comment to M. Laviolette, J.W. Seaman, Jr., J.D. Barrett and W.H. Woodall, A probabilistic and statistical view of fuzzy methods.
• R.G. Almond. Hypergraph grammars for knowledge based model construction. StatSci Research Report 23, 1995.
• Russell G. Almond. Capturing reliability knowledge in Graphical-Belief. StatSci Research Report 31. Presented in special NASA collection, 1995.
• Russell G. Almond. Graphical Belief Modeling. Chapman and Hall, 1995.
• R. G. Almond and J. Schimert. Missing data models as meta-data. StatSci Research Report 29, 1995.
• D. Madigan and R.G. Almond. Test selection strategies for belief networks. In D. Fisher and H.J. Lenz, editors, Learning from Data: AI and Statistics V, pages 89–98. Springer-Verlag, 1995.
• Russell G. Almond. Brushing histories to compare models. StatSci Research Report 17., 1994.
• Russell G. Almond. ElToY: Implementing Bayesian computation through constraints. StatSci Research Report 24, 1994.
• Russell G. Almond. Graphical-Belief: Phase II annual report. SBIR final report, 1994.
• R.G. Almond, E. Arbogast, M.Y. Jaisimha, and S.F. Elston. Intelligent image browsing and feature extraction. StatSci Research Report 25., 1994.
• R.G. Almond, J.M. Bradshaw, and D. Madigan. Reuse and sharing of graphical belief network components. In P. Cheeseman and W. Oldford, editors, Selecting Models from Data: Artificial Intelligence and Statistics IV, pages 113–122. Springer-Verlag, 1994.
• D. Madigan, A. E. Raftery, J. C. York, J. M. Bradshaw, and R. G. Almond. Strategies for graphical model selection. In P. Cheeseman and W. Oldford, editors, Selecting Models from Data: Artificial Intelligence and Statistics IV, pages 91–100. Springer-Verlag, 1994.
• D. Madigan, J.C. York, J.M. Bradshaw, and R.G. Almond. Bayesian graphical models for predicting errors in databaes. In P. Cheeseman and W. Oldford, editors, Selecting Models from Data: Artificial Intelligence and Statistics IV, pages 123–132. Springer-Verlag, 1994.
• Russell G. Almond. Lack of information based control in expert systems. In D.J. Hand, editor, Artificial Intelligence Frontiers in Statistics: AI and Statistics III, pages 82–89. Chapman and Hall, 1993.
• Russell G. Almond and D. Madigan. Using Graphical-Belief to predict risk of coronary artery disease. StatSci Research Report 19., 1993.
• J.M. Bradshaw, C.R. Chapman, K.M. Sullivan, R.G. Almond, D. Madigan, D. Zarley, J. Gavrin, J. Nims, and N. Bush. Ks-3000: an application of DDUCKS to bonemarrow transplant patient support. In Proceedings of the Sixth Annual Florida AI Research Symposium (FLAIRS '93), pages 78–83, 1993.
• Russell Almond. Reduced parameter representation of model components. StatSci Research Report 1, 1992.
• Russell G. Almond. An approach to style in data analysis. Technical Report 225, University of Washington, Department of Statistics, 1992.
• Russell G Almond. An extended example for testing Graphical-Belief. StatSci Research Report 6, 1992.
• Russell G. Almond. Building blocks for graphical belief models. Journal of Applied Statistics, 18:63–76, 1991.
• Russell G. Almond. Fiducial inference and belief functions. Technical Report 206, University of Washington, Department of Statistics, 1991.
• R.G. Almond and C-T. A. Kong. Some heuristics for building an optimal tree of cliques from a graph or hypergraph. Research Report 329, University of Chicago, Department of Statistics, 1991. Finn Jensen dubbed the tree produced by this algorithm as the "Almond Tree.".
• R[ussell] G. Almond. Breaking dependence in graphical belief models. In Connie Page and Raul LaPage, editors, Computing Science and Statistics: Proceedings of the 22nd Symposium on the Interface, pages 296–299. Springer-Verlag, 1990.
• Scott Marley. Crazy days, arabian nights. GAMES, 13(5):14–17,53., 1989. I was a contributing Author to the game reviewed in this article.
• R[ussell] G. Almond. Fusion and propagation in graphical belief models. In Edward J. Wegman, Donald T. Gantz, and John J. Miller, editors, Computing Science and Statistics: Proceedings of the 20th Symposium on the Interface., pages 365–370. American Statistical Association, 1988.
• R. Almond, P. Huber, P. Kempthorne, and L. Roseman. Exploratory multivariate analysis: The crabs data. Harvard University, 1987. 20 min. film.
• R. Almond, P. Huber, E. Ramos, L. Roseman, and M. Thoma. Visual aspects of 3d data graphics: A movie. Siam Conference on Computers is Statistics, 1984. 10 min. film.