Response to intervention: Empirical demonstration of a dual-discrepancy population via random effects mixture models

Response to Intervention (RtI) is a commonly used framework to identify students in need of additional or specialized instruction. Special education eligibility decisions within RtI rely on the assumption that there are subpopulations of students: those who demonstrate appropriate growth and those who do not demonstrate appropriate growth, when provided specialized instruction. The purpose of the present study was to illustrate the use of random-effects mixture models (RMMs) to estimate the likely number of (unobserved) subpopulations within one curriculum-based measurement of oral reading (CBM-R) progress monitoring dataset. The dataset comprised second grade students’ CBM-R data collected weekly over 20 weeks. RMMs were fit with several numbers of classes, and a two-class model best fit the data. Results suggest that RMMs are useful to understand subpopulations of students who need specialized instruction. Results also provide empirical support to some extent for the use of a dual-discrepancy model of learning disability identification within RtI.