When using response to intervention (RTI) for special education eligibility decisions, multidisciplinary teams (MDTs) may use performance level and rate of improvement (ROI) as two metrics when evaluating student responsiveness. Although examining student responsiveness to instruction is a critical component of an evaluation using RTI, previous research suggests that final level of performance is the main factor in MDT decisions. ROI is influenced by pre-intervention performance and is also inherently captured in students’ postintervention level, potentially limiting ROI's utility as a distinct indicator of performance. To address these issues with ROI, this study examined whether initial performance level and ROI improve actual MDT decisions about special education eligibility beyond the final level of performance. Results indicate that initial level of performance and ROI add no additional value to predicting MDT's eligibility decisions when using RTI despite identified students having significantly lower initial performance levels and lower ROIs than their nonidentified peers.
AlgozzineB.ChristensonS. L.YsseldykeJ. E. (1982). Probabilities associated with the referral to placement process. Teacher Education and Special Education, 5(3), 19–23. https://doi.org/10.1177/088840648200500304
2.
ArdoinS. P.ChristT. J.MorenaL. S.CormierD. C.KlingbeilD. A. (2013). A systematic review and summarization of the recommendations and research surrounding curriculum-based measurement of oral reading fluency (CBM-R) decision rules. Journal of School Psychology, 51(1), 1–18. https://doi.org/10.1016/j.jsp.2012.09.004
3.
BoseM.KohliN.NewellK. W.ChristT. J. (2019). Response to intervention: Empirical demonstration of a dual-discrepancy population via random effects mixture models. Learning and Individual Differences, 71, 23–30. https://doi.org/10.1016/j.lindif.2019.03.004
4.
BurnsM. K.ScholinS. E.KosciolekS.LivingstonJ. (2010). Reliability of decision-making frameworks for response to intervention for reading. Journal of Psychoeducational Assessment, 28(2), 102–114. https://doi.org/10.1177/0734282909342374
5.
BurnsM. K.SenesacB. V. (2005). Comparison of dual-discrepancy criteria to assess response to intervention. Journal of School Psychology, 43(5), 393–406. https://doi.org/10.1016/j.jsp.2005.09.003
6.
ChristT. J. (2006). Short-term estimates of growth using curriculum-based measurement of oral reading fluency: Estimating standard error of the slope to construct confidence intervals. School Psychology Review, 35(1), 128–33. https://doi.org/10.1080/02796015.2006.12088006
7.
ChristT. J.ZopluogluC.LongJ. D.MonaghenB. D. (2012). Curriculum-based measurement of oral reading: Quality of progress monitoring outcomes. Exceptional Children, 78(3), 356–73. https://doi.org/10.1177/001440291207800306
8.
FastBridge (2015). Formative assessment system for teachers. Author.
FuchsD.DeshlerD. (2007). What we need to know about response-to-intervention (and shouldn’t be afraid to ask). Learning Disabilities Practice, 22(2), 129–36. https://doi.org/10.1111/j.1540-5826.2007.00237.x
11.
FuchsD.FuchsL. S.ComptonD. L. (2004). Identifying reading disabilities by responsiveness-to-instruction: Specifying measures and criteria. Learning Disability Quarterly, 27(4), 216–27. https://doi.org/10.2307/1593674
GoodR. H.SimmonsD. C.Kame’enuiE. J. (2001). The importance and decision-making utility of a continuum of fluency-based indicators of foundational reading skills for third-grade high-stakes outcomes. Scientific Studies of Reading, 5(3), 257–88. https://doi.org/10.1207/S1532799XSSR0503_4
14.
HauptH.LöselF.StemmlerM. (2014). Quantile regression analysis and other alternatives to ordinary least squares regression: A methodological comparison on corporal punishment. Methodology, 10(3), 81–91. https://doi.org/10.1027/1614-2241/a000077
15.
HendricksH. L.FuchsD. (2020). Are individual differences in response to intervention influenced by the methods and measures used to define response? Implications for identifying children with learning disabilities. Journal of Learning Disabilities, 53(6), 428–43. https://doi.org/10.1177/0022219420920379
16.
HunterD.BoneshefskiM.KovaleskiJ. F.RungeT. J. (2023). Examining the impact of federal specific learning disability inclusionary criteria on multidisciplinary team decision making when using response to intervention. Learning Disability Quarterly, 46(3), 204–15. https://doi.org/10.1177/07319487221115172
17.
KavaleK. A.ReeseJ. H. (1992). The character of learning disabilities: An Iowa profile. Learning Disability Quarterly, 15(2), 74–94. https://doi.org/10.2307/1511010
18.
KimY. S.PetscherY.SchatshneiderC.FoormanB. R. (2010). Does growth rate in oral reading fluency matter in predicting reading comprehension achievement?Journal of Educational Psychology, 102(3), 652–67. https://psycnet.apa.org/doi/10.1037/a0019643
KovaleskiJ. F.VanDerHeydenA. M.RungeT. J.ZirkelP. A.ShapiroE. S. (2022). The RTI approach to evaluating learning disabilities (2nd ed.). The Guilford Press.
21.
MakiK. E.AdamsS. R. (2020). Specific learning disabilities identification: Do the identification methods and data matter?Learning Disability Quarterly, 43(2), 63–74. https://doi.org/10.1177/0731948719826296
22.
MakiK. E.BarrettC. A.HajovskyD. B.BurnsM. K. (2020). An examination of the relationships between specific learning disabilities identification and growth rate, achievement, cognitive ability, and student demographics. School Psychology, 35(5), 343–352. https://doi.org/10.1037/spq0000400
23.
MakiK. E.BurnsM. K.SullivanA. (2017). Learning disability identification consistency: The impact of methodology and student evaluation data. School Psychology Quarterly, 32(2), 254–67. https://doi.org/10.1037/spq0000165
24.
MakiK. E.FloydR. G.RobersonT. (2015). State learning disability eligibility criteria: A comprehensive review. School Psychology Quarterly, 30(4), 457–69. https://doi.org/10.1037/spq0000109
25.
MiciakJ.FletcherJ. M. (2020). The critical role of instructional response for identifying dyslexia and other learning disabilities. Journal of Learning Disabilities, 53(5), 343–353. https://doi.org/10.1177/0022219420906801
NeseJ. F. T.BiancarosaG.CummingsK.KennedyP.AlonzoJ.TindalG. (2013). In search of average growth: Describing within-year oral reading fluency growth across grades 1-8. Journal of School Psychology, 51(5), 625–42. https://doi.org/10.1016/j.jsp.2013.05.006
28.
ParkerR.TindalG. (1992). Estimating trend in progress monitoring data: A comparison of simple line fitting methods. School Psychology Review, 21(2), 300–12. https://doi.org/10.1080/02796015.1992.12085616
29.
PetersonK. M. H.ShinnM. R. (2002). Severe discrepancy models: Which best explains school identification practices for learning disabilities?School Psychology Review, 31(4), 459–76. https://doi.org/10.1080/02796015.2002.12086168
30.
RungeT. J.BennyhoffC. F.FerchalkM. R.McCreaA. E. (2017). The role of measurement interval in rate of improvement calculation. School Psychology Forum: Research in Practice, 11(3), 77–90.
31.
SawilowskyS. S.BlairR. C. (1992). A more realistic look at the robustness of and Type II error probability of the t test to departures from population normality. Psychological Bulletin, 111(2), 352–60. https://doi.org/10.1037/0033-2909.111.2.352
32.
ShinnM. R.GoodR. H.SteinS. (1989). Summarizing trend in student achievement: A comparison of methods. School Psychology Review, 18(3), 356–70. https://doi.org/10.1080/02796015.1989.12085432
33.
SilberglittB.HintzeJ. M. (2007). How much growth can we expect: A conditional analysis of R-CBM growth rates by level of performance. Exceptional Children, 74(1), 71–84. https://doi.org/10.1177/001440290707400104
34.
SpeeceD. L.CaseL. P. (2001). Classification in context: An alternative approach to Identifying early reading disability. Journal of Educational Psychology, 93(4), 735–49. https://doi.org/10.1037/0022-0663.93.4.735
35.
SpeeceD. L.CaseL. P.MolloyD. E. (2003). Responsiveness to general instruction as the first gate to learning disabilities identification. Learning Disabilities Research & Practice, 18(3), 147–56. https://doi.org/10.1111/1540-5826.00071
36.
TolarT. D.BarthA. E.FletcherJ. M.FrancisD. J.VaughnS. (2014). Predicting reading outcomes with progress monitoring slopes among middle grade students. Learning and Individual Differences, 30, 46–57. https://doi.org/10.1016/j.lindif.2013.11.001
37.
TorgesenJ. K.AlexanderA. W.WagnerR. K.RashotteC. A.VoellerK. K. S.ConwayT. (2001). Intensive remedial reading instruction for children with severe reading disabilities: Immediate and long-term outcomes from two instructional approaches. Journal of Learning Disabilities, 34(1), 33–58. https://doi.org/10.1177/002221940103400104
38.
United States Department of Education. (2006, August 14). Assistance to states for the education of children with disabilities (Final Rule, 71 Federal Register 46539-46845).
39.
VellutinoF. R.ScanlonD. M.SipayE. R.SmallG. S.ChenR.PrattA.DencklaM. B. (1996). Cognitive profiles of difficult-to-remediate and readily remediated poor readers: Early intervention as a vehicle for distinguishing between cognitive and experiential deficits as basic causes of specific reading disability. Journal of Educational Psychology, 88(4), 601–38. https://doi.org/10.1037/0022-0663.88.4.601
40.
WaymanM. M.WallaceT.Ives WileyH.TichaR.EspinC. A. (2007). Literature synthesis on curriculum-based measurement in reading. The Journal of Special Education, 41(2), 85–120. https://doi.org/10.1177/00224669070410020401
41.
YsseldykeJ. E.AlgozzineB.ShinnM. R.McGueM. (1982). Similarities and differences between low achievers and students classified learning disabled. The Journal of Special Education, 16(1), 73–85. https://doi.org/10.1177/002246698101500404
