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Elementary schools administer reading screening assessments to identify students in need of remedial instruction. However, the administration of additional assessments comes at an opportunity cost and it is currently unclear the extent to which multiple types of reading screeners warrant the increase in resources that could be used for instruction. This study compared the cost-accuracy ratios for three types of reading screeners in Grade 3: curriculum-based measurement (Acadience), computer adaptive assessment (Star), and informal reading inventory (Fountas and Pinnell Benchmark Assessment System). Further, we demonstrated how schools can use classification and regression tree analysis to identify local cut-scores, maximizing classification accuracy. Results suggested that a multivariate approach, which included a computer adaptive assessment and oral reading fluency yielded the greatest accuracy and lowest cost-accuracy ratio. Furthermore, the Classification and Regression Tree analysis signaled two different instructional implications for subgroups of students with specific profiles on the screening measures.

This article describes the emergence and influence of evidence-based practice and data-based decision making in educational systems. Increasingly, educators and consumers want to know that resources allocated to educational efforts yield strong effects for all learners. This trend is reflected by the widespread influence of evidence-based practice (EBP) in education and the demand for data-based decision-making models like response to intervention (RtI). This article describes the ways in which EBP and RtI can be used to improve efficacy, efficiency, and equity of educational services. The article concludes with concrete recommendations for implementing RtI.

This study used simulation techniques to evaluate the technical adequacy of three methods for the identification of specific learning disabilities via patterns of strengths and weaknesses in cognitive processing. Latent and observed data were generated and the decision-making process of each method was applied to assess concordance in classification for specific learning disabilities between latent and observed levels. The results showed that all three methods had excellent specificity and negative predictive values, but low to moderate sensitivity and very low positive predictive values. Only a very small percentage of the population (1%–2%) met criteria for specific learning disabilities. In addition to substantial psychometric issues underlying these methods, general application did not improve the efficiency of the decision model, may not be cost effective because of low base rates, and may result in many children receiving instruction that is not optimally matched to their specific needs.

RTI as a framework for decision making has implications for the diagnosis of specific learning disabilities. Any diagnostic tool must meet certain standards to demonstrate that its use leads to predictable decisions with minimal risk. Classification agreement analyses are described as optimal for demonstrating the technical adequacy of RTI decisions. The benefits and limitations of commonly used classification agreement estimates are described in this article and suggestions are made for their use in ensuring technically adequate use of RTI.