Models incorporating response to intervention

All of the approaches reviewed so far are based on assessments administered at a single timepoint. They become unwieldy and impractical if extended to multiple assessments. As Francis et al. (in press) showed, it would be difficult to implement this kind of model with any great reliability when a single time point is used. To certain extent, the intra-individual differences approach avoids some of these difficulties by using multiple tests at the same timepoint, looking for recurrent discrepancies that might make up a profile. However, the measures used in this approach typically have reliabilities that are much lower than those apparent for norm referenced IQ and achievement tests, magnifying the problem of reliably identifying profile variations. In contrast, models incorporating response to intervention typically involve identification based in part on multiple probe assessments of the same core area, such as reading or math. By tying multiple assessments to specific attempts to intervene with the child, the construct of unexpected underachievement can be operationalized in part on the basis as non-responsiveness to instruction to which most other students respond (Fuchs & Fuchs, 1998; Gresham, 2002).

Such approaches do not obviate the measurement issues involved in the assessment of discrepancy (Fletcher et al., 2003). In fact, discrepancy is still part of the model, but is assessed relative to learning expectations based on multiple administrations of the same test over time as opposed to a comparison of two tests, or multiple different tests administered at the same timepoint. There are issues involved in the intervention component, estimation of slope and intercept effects, as well as decisions that have to be made about "cutpoints" that will differentiate responders and non-responders (Gresham, 2002). For these reasons alone, response to intervention cannot be the sole criterion for identification and flexibility in decision making will be required. At the same time, there appears to be considerable validity to this approach, implying that it is indeed possible to reliably identify non-responders as a group with "unexpected underachievement." Studies of children defined using different methods as responders and non-responders clearly show large differences in cognitive skills. For example, Stage et al. (2003), Vellutino et al. (1996) and Vaughn, Linan-Thompson, and Hickman-Davis (in press) found that non-responders to early intervention differed from responders in both pre-intervention achievement scores and pre-intervention cognitive tasks. Those who were non-responders were usually more severe. In our imaging studies involving both early intervention and remediation of older students (see Fletcher et al., in press), we have found that individuals who were non-responders showed more severe reading difficulties prior to intervention. More dramatic were the differences in neuroimaging correlates between those who responded to intervention and those who did not respond to intervention. We have found that non-responders persist with a brain activation pattern that generally demonstrated a failure to activate left hemisphere areas known to be involved in the development of reading skills. In fact, those who were non-responders showed predominant right hemisphere activity much like that observed in children and adults with identified reading disabilities (Fletcher et al., in press).

In addition to this evidence for validity (and the greater reliability of the underlying psychometric model), the model does not require the use of exclusionary criteria (especially emotional disturbance and economic disadvantage) to operationalize unexpected underachievement, thus capturing the construct of LD. This is an important consideration given the lack of evidence validating classifications that utilize these particular exclusions (Kavale, 1988; Lyon et al., 2001).

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