Modeling Students' Response to Intervention Using an Individualized Piecewise Growth Model
Author: Zvoch, Keith
Date: 2011-00-00
Type: Reports - Research
Subjects: Response to Intervention,Reading Instruction,Statistical Analysis,Models,Literacy,Reading Difficulties,Student Characteristics,Naming

The early identification of students at-risk for future reading difficulty has become a focal point for K-12 stakeholders seeking to actively prevent the emergence of student reading deficits. Early and active intervention efforts for struggling readers have taken on greater urgency given the accountability pressures that stem from the No Child Left Behind (NCLB, 2002) federal legislation and in light of the observation that reading trajectories establish early and remain relatively stable over time (Juel, 1988; Schatschneider et al., 2004). Interest in preventing initial reading difficulties from snowballing into long-term reading failure (Chard et al., 2008; Coyne &amp; Harn, 2006; Torgesen, 2002) has promoted the development and implementation of the Response to Intervention (RtI) instructional model. The RtI model is an integrated framework designed to provide early screening of student reading performance, enable continuous monitoring of student reading progress, and allow active and focused intervention in cases where reading difficulties are observed (Fuchs, Fuchs, &amp; Vaughn, 2008). Although practitioners and researchers continue to debate issues regarding how to identify students in need of supplemental instruction, structure and deliver instructional support, and determine the adequacy of response to strategic intervention (Gersten et al., 2008), RtI-based treatment models have been endorsed by the federal government (IDEA, 2004) and are increasingly being adopted and implemented in elementary and secondary school settings (Spectrum, 2010). With respect to the challenges and opportunities associated with RtI-based designs, the purpose of this paper is to present a multilevel piecewise model that estimates a staggered interrupted time series individualized for each participant. The demonstration draws on a unique data set that contains a time series of upward of 16 academic year test scores, student background characteristics, and the date at which a supplemental literacy intervention was implemented for each program participant. Two-level unconditional and conditional piecewise growth models were applied to the data obtained over the course of the study as a means for estimating student outcomes. The following research questions were investigated, (1) What was the shape of the pre and post intervention growth functions? (2) On average, did students' level of literacy performance and rate of literacy growth increase after supplemental reading support was initiated? (3) Was there statistical variation in students' response to intervention initially and over time? and, (4) Were one or more student and instructional characteristics predictive of the variation in intervention responses? The research was conducted on data obtained from a moderately-sized school district in the Pacific Northwest. The analytic sample was comprised of 155 struggling readers who began kindergarten either in the 2008-09 or 2009-10 school year. This paper concludes that individual response to intervention may differ initially and over time. Variability in outcomes may be due to characteristics of the individual and/or characteristics of the treatment context. Beyond descriptively tracking outcomes, understanding why different individuals respond in different ways should be a goal of researchers and practitioners. Application of staggered ITS models enables analytic flexibility to model the dynamic and fluid nature of contemporary instructional and clinical practice and the diversity and individualization of treatment for students at different points in time. Nonetheless, while the present demonstration offered a novel approach for modeling the wealth of data obtained in RtI contexts, examination of additional data sets that include a variety of outcome measures, indicators describing the components of instruction and their delivery, and more diverse student samples are recommended to better understand the generality of these results. (Contains 1 table and 2 figures.)