I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. - -Sir Arthur Conan Doyle, British mystery author and physician (1859-1930), The Sign of Four, A Scandal in Bohemia1

In this issue of Topics in Language Disorders (TLD), issue editors Kathleen Whitmire and Maureen Staskowski (2014) have taken on the complex and controversial topic of methods for identifying learning disabilities (LDs). Readers will benefit from articles provided by the experts enlisted by Whitmire and Staskowski to explain the advantages and challenges associated with differing approaches for identifying LD. Although there are indeed controversies associated with this topic, the authors (like Sir Arthur Conan Doyle's famous Sherlock) agree on the need for data to answer the question of who should be identified as having LD, and essentially on the components to consider; they present arguments, however, for different levels of emphasis and to some extent, timing.

The topic of challenges in LD identification is especially timely because a decade now has passed since changes were enacted in the Individuals with Disabilities Education Act (IDEA; 2004, with regulations issued in 2006). It was then that "Additional Procedures for Identifying Children with Specific Learning Disabilities" were included in the regulations, as follows:

[S] 300.307 Specific learning disabilities

(a) General. A state must adopt, consistent with [S] 300.309, criteria for determining whether a child has a specific learning disability as defined in [S] 300.8(c)(10). In addition, the criteria adopted by the State-

(1) Must not require the use of a severe discrepancy between intellectual ability and achievement for determining whether a child has a specific learning disability, as defined in [S] 300.8(c)(10);

(2) Must permit the use of a process based on the child's response to scientific, research-based intervention; and

(3) May permit the use of other alternative research-based procedures for determining whether a child has a specific learning disability, as defined in [S]300.8(c)(10).

Zumeta, Zirkel, and Danielson (2014) open this issue of TLD with a synopsis of the historical and legal context in which these changes have occurred, prior to, as well as since the 2006 regulations were published. These three authors, having held front row seats along the way, take readers from early efforts to define and identify LD to the present, describing essential constructs and controversies, including legal challenges. Although it was a relatively minor point within the broad scope of their review, I noted concerns expressed by Zumeta et al. that once identified with LD, some children appear to receive less intervention and less progress monitoring than their peers in general education. This is an observation beyond the scope of this issue, but one that deserves further attention.

The general education processes to which Zumeta et al. referred were those stipulated in the 2006 regulations for IDEA ([S] 300.307(a)(2)), which indicated that States must "permit the use of a process based on the child's response to scientific, research-based intervention." For the most part, this process, often called "response to intervention" (RTI), occurs under the auspices of general education. Two of the articles in this issue focus directly on the rationale, uses, and cautions associated with evaluating data generated by the RTI process as part of identifying LD. Reschly (2014) sets the stage by describing how RTI can be used for varied purposes, including prevention and early identification-intervention, as well as contributing to LD identification. When RTI is used for identification, Reschly notes the importance of implementing tiers of scientific, research-based intervention (also called Multi-Tiered Systems of Support; MTSS) with fidelity, accompanied by screenings to assess the multiple areas that must be ruled in or out to identify LD, followed by comprehensive evaluation if data supporting limited response are found.

Fletcher et al. (2014) extend the focus on RTI as part of a comprehensive approach to LD identification. They help readers understand the fine points associated with measuring students' responsiveness, noting advantages of dual-discrepancy methods to gather data of two types: (1) data reflecting change during interventions and (2) data showing students' status after intervention. They also offer empirical and simulated data to explain why different measurement techniques may not always agree, emphasizing the importance of adopting measures with high reliability. To improve the reliability (i.e., consistency) of diagnostic decisions about LD, Fletcher et al. recommend using multiple measures, including performance on curriculum-based measures and short norm-referenced measures of performance fluency.

Johnson (2014) expresses a slightly different perspective on the type of data needed to inform LD identification decisions. She uses case examples to explain why it is important to conduct assessments of varied cognitive processes to understand why a particular child might not be responding adequately to tiers of intervention. Johnson reminds readers that the construct of LD incorporates the notion of neurological differences in cognitive processing as an explanation for why children with LD do not respond as well to the same educational experiences in which most children learn adequately to read, write, and use mathematics. Johnson's best practice recommendations are to use a combined model that measures both a student's RTI and his or her cognitive processes. Among candidate processing difficulties, Johnson includes phonological processes, as well as working memory, global executive functioning deficits, and access and retrieval deficits.

In addition to these, I recommend that the "cognitive processes" assessed during screening and comprehensive evaluation include the language processes needed to support learning to read and write. This would include not only phonological abilities but also the morphemic, vocabulary, grammar, and discourse skills needed to develop listening and reading comprehension skills and oral and written expression skills sufficient for meeting core curricular standards. Consider the wording of the definition of LD in IDEA (2004, 2006), which describes psychological processes as follows:

[S] 300.8 (c) (10) Specific Learning Disability-(i) General. Specific learning disability means a disorder in one or more of the basic psychological processes involved in understanding or using language, spoken or written [emphasis added], that may manifest itself in the imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations, including conditions such as perceptual disabilities, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia.

In their article, Sun and Wallach (2014) do emphasize the need to gather data about students' language processing abilities within the contexts of the curriculum, which is inherently language based and characterized by mounting linguistic demands across grades. Sun and Wallach also consider questions surrounding the overlapping nature of language disorders and LD, noting that a language disorder is, in fact, LD (and vice versa). Yet, this connection may be obscured when students are identified first as having a language impairment (called "speech or language impairment" in the federal regulations), often in the preschool years, and later are identified as having LD. Sun and Wallach provide rationale for use of the term "language learning disability" (LLD) to encompass the diagnoses of language disorders and LD. This term has appeared frequently in the pages of TLD, with advantages, I would agree, for describing the broader heterogeneous population without implying dichotomies where none exist.

Although the authors who write about varied LD identification practices within the pages of this issue differ in their perspectives regarding which data are most critical for decision making, none of them questions the value of data or that a construct of LD can be identified. We readers, both researchers and practitioners, can don our Sherlock-like deer stalking caps and thank Whitmire and Staskowski (2014), along with their contributing authors, for providing data that we can use to refine our theories and practices.

-Nickola Wolf Nelson, PhD

Editor

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