University of Oregon

Related Readings

Here is a list of recommended readings to introduce and explain theoretical and methodological issues related to our research.


  • Abedi, J. (2006). Psychometric issues in the ELL assessment and special education eligibility. Teachers College Record, 108, 2282-2303.
  • Abedi, J., & Herman, J. (2010). Assessing English language learners opportunity to learn mathematics: Issues and limitations. Teachers College Record, 112, 723-746.
  • Abedi, J., Leon, S., & Kao, J. C. (2008). Examining differential item functioning in reading assessments for students with disabilities. National Center for Research on Evaluation, Standards, and Student Testing (CRESST).
  • Alonzo, J., Tindal, G., Ulmer, K., & Glasgow, A. (2006). easyCBM online progress monitoring assessment system. Eugene, OR: Center for Educational Assessment Accountability. Available at http://easycbm.com.
  • American Educational Research Association (AERA), American Psychological Association (APA), & National Council on Measurement in Education (NCME) (1999). Standards for educational and psychological testing. Washington, DC: Author.
  • Amrein-Beardsley, A. (2008). Methodological concerns about the education value-added assessment system. Educational Researcher, 37, 65-75.
  • Andrage, H. L., & Cizek, G. J. (2009). Handbook of formative assessment. London, UK: Routledge
  • Barton, P. (2005). Unfinished business: More measured approaches in standards-based reform. Princeton, NJ: Educational Testing Services.
  • Bloom, H. S., Hill, C. J., Black, A. R., & Lipsey, M. W. (2008). Performance trajectories and performance gaps as achievement effect-size benchmarks for educational interventions. Journal of Research on Educational Effectiveness, 1, 289-328.
  • Braun, H. (2004). Value-added modeling: What does due diligence require? Princeton, NJ: Educational Testing Service.
  • Bryk, A. S., Thum, Y. M., Easton, J. Q., & Luppescu, S. (1998). Assessing school academic productivity: The case of Chicago school reform. Social Psychology of Education, 2, 103-142.
  • Carlberg, C., & Kavale, K. (1980). The efficacy of special versus regular class placement for exceptional children: A meta-analysis. Journal of Special, 14, 295-309.
  • Center on Education Policy (2011). State policy differences greatly impact AYP numbers: A background paper from the Center on Education Policy. Washington, DC: Author.
  • Deno, S. (1987). Curriculum-based measurement. Teaching Exceptional Children, 20, 41-47.
  • Downey, D. B., von Hippel, P. T., Hughes, M. (2008). Are "failing" schools really failing? Using seasonal comparisons to evaluate school effectiveness. Sociology of Education, 81(3), 242-270.
  • Eckes, S. E., Swando, J. (2009). Special education subgroups under NCLB: Issues to consider. Teachers College Record, 111(11), 2479-2504.
  • Erpenbach, W. J. (2009). Determining adequate yearly progress in a state performance or proficiency index model. Washington, DC: Council of Chief State School Officers.
  • Goldschmidt, P., Roschewski, P., Choi, K., Auty, W., Hebbler, S., Blank, R., & Williams, A. (2005). Policymakers guide to growth models for school accountability: How do accountability models differ? Washington, DC: The Council of Chief State School Offic
  • Gong, B., Perie, M., Dunn, J. (2006). Using student longitudinal growth measures for school accountability under No Child Left Behind: An update to inform design decisions. Center for Assessment, Retrieved August 1, 2009 from http://www.nciea.org/publicat
  • Hanushek, E., & Raymond, M. (2005). Does school accountability lead to improved student performance? Journal of Policy Analysis and Management, 24(2), 297-327.
  • Heck, R. (2006). Assessing school achievement progress: Comparing alternative approaches. Educational Administration Quarterly, 42, 667-699.
  • Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W. (2007). Empirical benchmarks for interpreting effect sizes in research. New York, NY: MDRC.
  • Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences. Boston, MA: Houghton Mifflin Company.
  • Kiplinger, V. (2008). Reliability of large-scale assessments and accountability systems: The future of test-based educational accountability (pp. 93-114). New York, NY: Routledge.
  • Ladd, H. & Lauen, D. (2009). Status versus growth: The distributional effects of school accountability policies (CALDER Working Paper 21). Washington, DC: Urban Institute.
  • Linn, R., (2008). Educational accountability systems. In K. Ryan & L. Shepard (Eds.), The future of test-based educational accountability (pp. 3-24). New York: Routledge.
  • Linn, R., & Haug, C. (2002). Stability of school-building accountability scores and gains. Educational Evaluation and Policy Analysis, 24, 29-36.
  • McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 76-101.
  • McDonnell, L., McLaughlin, M., & Morison, P. (1997). Educating one and all: Students with disabilities and standards-based reform. Washington, DC: National Academy Press.
  • Perie, M., Marion, S., Gong, B. (2009). Moving toward a comprehensive assessment system: A framework for considering interim assessment. Educational Measurement: Issues and Practice, 28(3), 5-13.
  • Perie, M., Marion, S., & Gong, B. (2007). A framework for considering interim assessments. National Center for the Improvement of Educational Assessment, Dover, NH.
  • Ponisciak, S. M., & Bryk, A. S. (2005). Value-added analysis of the Chicago Public Schools: An application of hierarchical models. In R. Lissitz (Ed,), Value-added modeling: Issues with theory and applications (pp. 40-81). Maple Grove, MN:JAM Press.
  • Raudenbush, S. W. (2004). Schooling, statistics, and poverty: Can we measure school improvement? Paper presented at the William H. Angoff Memorial Lecture Series. Retrieved from http://www.ets.org/research/.
  • Rogosa, D. (1995). Myths about longitudinal research. In J. M. Gottman (Ed.), The analysis of change. Mahwah, NJ: Erlbaum.
  • Schulte, A. C., Osborne, S. S., & Erchul, W. P. (1998). Effective special education: A United States dilemma. School Psychology Review, 27, 66-76.
  • Schulte, A., Villwock, D. (2004). Using high stakes tests to derive school-level measures of special education efficacy. Exceptionality, 12, 107-127.
  • Sheinker, J. & Erpenbach, W. J. (2007, April). Alternate assessments for students with significant cognitive disabilities--Strategies for states' preparation for and responses to peer review. Washington, DC: Council of Chief State School Officers.
  • Singer, J., & Willet, J. (2003). Applied longitudinal data analyses: Modeling change and event occurrence. New York: Oxford University Press.
  • Stevens, J. (2005). The study of school effectiveness as a problem in research design. In R. Lissitz (Ed), Value-added models in education: Theory and applications. Maple Grove, MN: JAM Press
  • Teddlie, C., & Reynolds, D. (2000). The international handbook of school effectiveness research. New York: Falmer Press.
  • U.S. Department of Education, Office of Planning, Evaluation and Policy Development, Policy and Program Studies Service, Final Report on the Evaluation of the Growth Model Pilot Project, Washington, DC, 2011.
  • Webb, N. L. (2006). Identifying content for student achievement tests. In S. M. Downing & T. M. Haladyna (Eds.), Handbook of test development, pp. 155-180. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Webster, W. J. (2005). The Dallas school-level accountability model: The marriage of status and value-added approaches. In R. Lissitz (Ed.). Value-added models in education: Theory and applications (pp. 233-271). Maple Grove, MN: JAM Press.
  • Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116(2), 363-381.
  • Willett, J. B., Singer, J. D., & Martin, N. C. (1998). The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations. Development and Psychopathology, 10, 395-426.
  • Willms, J. D. (1992). Monitoring school performance: A guide for educators. London: Falmer Press.
  • Zvoch, K., & Stevens, J. J. (2005). Sample exclusion and student attrition effects in the longitudinal study of middle school mathematics performance. Educational Assessment, 10(2), 105-123.