Growth-based approaches to federal accountability are receiving considerable attention because they have the potential to reward schools and teachers for improving student performance over time by measuring student progress at all levels of the performance spectrum, including progress by students who have not reached proficiency on state accountability assessments. The use of growth in accountability holds promise for students with disabilities, but measuring changes over time in academic performance with large-scale annual assessments is complex. The authors discuss practical challenges in measuring and modeling growth for students with disabilities. In addition, they identify and describe three areas in need of research on the measurement of growth: the impact of testing accommodations, the impact of test difficulty, and the longitudinal characteristics of the population of students with disabilities.
Abt Associates. (2006). Improving results for students with disabilities: Key findings from the 1997 National Assessment Studies. Bethesda, MD: Abt Associates.
2.
AhearnE. (2009). Growth models and students with disabilities: Report of state interviews. Alexandria, VA: National Association of State Directors of Special Education. Retrieved from http://www.projectforum.org
AlbusD.LazarusS. S.ThurlowM. L.CormierD. (2009). Characteristics of states’ alternate assessments based on modified academic achievement standards in 2008 (Synthesis Report 72). Minneapolis: University of Minnesota, National Center on Educational Outcomes.
BetebennerD. (2009). Norm- and criterion-referenced student growth. Educational Measurement: Issues and Practice, 28, 42–51.
7.
BraunH. (2005). Using student progress to evaluate teachers: A primer on value-added models. Princeton, NJ: Educational Testing Service, Policy Information Center.
8.
BraunH.ChudowskyN.KoenigJ. (Eds.). (2010). Getting value out of value-added: Report of a workshop. Washington, DC: National Academies Press.
9.
BraunH.WainerH. (2007). Value-added modeling. In RaoC. R.SinharayS. (Eds.), Handbook of statistics: Vol. 26. Psychometrics (pp. 475–501). Amsterdam: Elsevier.
10.
BriggsD.WeeksJ. (2009). The impact of vertical scaling decisions on growth interpretations. Educational Measurement: Issues and Practice, 28, 3–14.
11.
Center on Education Policy. (2009, November). Has progress been made in raising achievement for students with disabilities?Author: Washington, DC.
12.
ChristensenL. L.LazarusS. S.CroneM.ThurlowM. L. (2008). 2007 State policies on assessment participation and accommodations for students with disabilities (Synthesis Report 69). Minneapolis: University of Minnesota, National Center on Educational Outcomes.
Council of Chief State School Officers. (2009). Guide to United States Department of Education Growth Model Pilot Program, 2005–2008. Washington, DC: Council of Chief State School Officers, Accountability Systems and Reporting State Collaborative.
DunnJ.AllenJ. (2009). Holding schools accountable for non-proficient students: Coordinating measurement and accountability. Educational Measurement: Issues and Practice, 28, 27–41.
19.
EckesS.SwandoJ. (2009). Special education subgroups under NCLB: Issues to consider. Teachers College Record, 111(11). Retrieved from http://www.tcrecord.org/library
20.
FuchsL.FuchsD. (2001). Helping teachers formulate sound test accommodation decisions for students with learning disabilities. Learning Disabilities Research and Practice, 16(3), 174–181.
21.
HendricksonA. (2007). An NCME instructional module on multistage testing. Educational Measurement: Issues and Practice, 26, 44–52.
22.
HillR.GongB.MarionS.DePascaleC.DunnJ.SimpsonM. A. (2006). Using value tables to explicitly value growth. In LissitzR. (Ed.), Longitudinal and value added models of student performance (pp. 255–290). Maple Grove, MN: JAM Press.
23.
KleinJ. A.WileyH. I.ThurlowM. L. (2006). Uneven transparency: NCLB tests take precedence in public assessment reporting for students with disabilities (Tech. Rep. No. 43). Minneapolis: University of Minnesota, National Center on Educational Outcomes.
24.
KolenM. J.BrennanR. L. (2004). Test equating, scaling, and linking (2nd ed.). New York: Springer.
25.
KoretzD. M.HamiltonL. S. (2006). Testing for accountability in K–12. In BrennanR. L. (Ed.), Educational Measurement (4th ed., pp. 531–578). Westport, CT: American Council on Education and Praeger.
26.
LaitusisC. C.CookL. L.BuzickH. M.StoneE. A. (in press). Adaptive testing options for accountability assessments. In RussellM. (Ed.), Assessing students in the margins: Challenges, strategies, and techniques. Charlotte, NC: Information Age.
27.
LissitzR. W.HuynhH. (2003). Vertical equating for state assessments: Issues and solutions in determination of adequate yearly progress and school accountability. Practical Assessment, Research and Evaluation. Retrieved from http://PAREonline.net/getvn.asp?v=8&n=10
28.
National Center for Learning Disabilities. (2009). Growth models for accountability: Considerations and recommendations for including students with disabilities (Policy Briefing). Washington, DC: Author.
PitoniakM. J.RoyerJ. M. (2001). Testing accommodations for examinees with disabilities: A review of psychometric, legal, and social policy issues. Review of Educational Research, 71(1), 53–104.
31.
RaudenbushS. W.BrykA. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
32.
ShrinerJ.DeStefanoL. (2007). Assessment accommodation considerations for middle school students with disabilities. In LaitusisC. C.CookC. (Eds.), Large-scale assessment and accommodations: What works?Washington, DC: Council for Exceptional Children.
33.
SireciS. G.ScarpetiS. E.LiS. (2005). Test accommodations for students with disabilities: An analysis of the interaction hypothesis. Review of Educational Research, 75, 457–490.
ThurlowM.LazarusS.QuenemoenR.MoenR. (2010). Using growth for accountability: Considerations for students with disabilities (Policy Directions 21). Minneapolis: University of Minnesota, National Center on Educational Outcomes. Retrieved from http://education.umn.edu/NCEO/OnlinePubs/Policy21
36.
ThurlowM. L.QuenemoenR.AltmanJ. R.CuthbertM. (2008). Trends in the participation and performance of students with disabilities (Tech. Rep. 50). Minneapolis: University of Minnesota, National Center on Educational Outcomes.
37.
U.S. Department of Education. (2003, December9). Title I—Improving the academic achievement of the disadvantaged: Final regulations. Federal Register, 68(236), 68698–68708.
38.
U.S. Department of Education. (2005, November18). Secretary Spellings announces growth model pilot, addresses Chief State School Officers’ Annual Policy Forum in Richmond. Retrieved from http://www2.ed.gov/news/pressreleases/2005/11/11182005.html
39.
U.S. Department of Education. (2007, April9). Final Rule 34 CFR Parts 200 and 300: Title I—Improving the academic achievement of the disadvantaged; Individuals With Disabilities Education Act (IDEA). Federal Register, 72(67), 17748–17781.
40.
WeissD. J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 6, 473–492.
41.
YenW. M. (2007). Vertical scaling and No Child Left Behind. In DoransN. J.PommerichM.HollandP. W. (Eds.), Linking and aligning scores and scales (pp. 273–282). New York: Springer.
42.
YenW. M. (2008). Measuring academic growth in California. Princeton, NJ: ETS.
43.
YsseldykeJ. E.BielinskiJ. (2002). Effect of different methods of reporting and reclassification on trends in test scores for students with disabilities. Exceptional Children, 68(2), 189–200.
44.
YsseldykeJ. E.ThurlowM. L.LangenfeldK. L.NelsonJ. R.TeelucksinghE.SeyfarthA. (1998). Educational results for students with disabilities: What do the data tell us?Minneapolis: University of Minnesota, National Center on Educational Outcomes.
