learning disability intervention manual revised edition
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learning disability intervention manual revised editionVery good condition with little wear; previous owner's name inside.This manual was designed specifically to e used to develop goals, objectives, and educational intervention strategies for students based on the results of the Learning Disability Evaluation Scale (LDES) (McCARNEY, 1983.Our BookSleuth is specially designed for you. All Rights Reserved. Please download the Free Intervention Manual Demo to determine if the software meets your needs. It provides the user with the ability to maintain individual student reports, the ability to print appendix pages, and three report options: Please download the Free Intervention Manual Demo to determine if the software meets your needs. The primary issue is how to distinguish students who are likely to have dyslexia from the considerable number of students who are simply poor readers. The present study explores the feasibility of developing a valid method for selecting students with dyslexia to serve as subjects in research studies and to enroll in special intervention programs. After consulting 16 definitions of dyslexia, five common elements were identified, and operational criteria were developed for four of the elements. These criteria were applied to 70 school-identified students with dyslexia residing in eight states. The results were used to establish three categories of likelihood for dyslexia: very likely, likely, and not very likely. According to our revised discrepancy method, 51 of the students currently receiving services under the dyslexia label satisfied the dyslexia likelihood criteria of very likely or likely. The remaining 49 did not satisfy the dyslexia likelihood criteria (i.e., they were not very likely to have dyslexia).
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The following sections (a) describe various methods currently used today to identify students who have dyslexia; (b) discuss the importance of creating valid criteria for identifying students who actually have dyslexia; (c) review 16 definitions of dyslexia; (d) extract the most common elements from those definitions; (e) provide operational criteria for each of those elements; (f) overview the methods used to validate the criteria; (g) review the results of applying these criteria to a sample of 70 students identified with dyslexia; and, finally, (h) discuss the study’s results, implications, and limitations. Not surprisingly, these methods for identifying students with dyslexia often focus on different defining aspects. Chief among these are low reading achievement, discrepancy between reading and other cognitive abilities, unresponsiveness to special reading intervention, and the presence of intraindividual differences among cognitive processing strengths and weaknesses. For an in-depth review of each of these methods, see Fletcher et al. ( 2018 ). All of the aforementioned identification methods use exclusionary criteria that require that the reading problem cannot be because of (a) intellectual disabilities, (b) uncorrected visual or auditory acuity, (c) other mental or neurological disorders, (d) emotional issues, (e) poor oral language ability, or (f) inadequate educational instruction. Unfortunately, most of these identification methods have problems with reliability and lack agreement with each other. For example, Waesche et al. ( 2011 ) found that applying a cognitive discrepancy method and an RTI-based method to the same sample resulted in only 31 agreement regarding which students qualified as having dyslexia. Applying the cognitive discrepancy method and low-achievement method to the same sample resulted in only 32 agreement. Further, these methods do not differentiate garden variety poor readers from students with dyslexia, a shortcoming that results in much confusion in the field regarding incidence rates and prognosis. In the next section, we review some of the benefits of developing alternative identification methods. Without such criteria, professionals cannot differentiate between truly reading-disabled students and other types of poor readers (e.g., garden variety poor readers, struggling readers). This being the case, a large percentage of the subjects in research studies that claim to investigate aspects of dyslexia and the progress of students enrolled in intervention programs that are intended for students with dyslexia do not have dyslexia. In fact, researchers usually provide little or no evidence to show that they do. The absence of generally agreed upon criteria for identifying dyslexia has plagued the field since its conception. This failure has resulted in the publication of questionable research in which the sample is referred to as having a specific learning disability (SLD) or dyslexia but, in reality, is composed of a considerable number of garden variety poor readers (Adelman 1989, 1992; DeLoach et al. 1981; Kirk and Chalfant 1984; Mather and Roberts 1994 ). Later in this paper, we show that 49 of the subjects used in our validating sample were classified as not very likely to have dyslexia, according to our revised discrepancy method. The availability of generally agreed upon discrepancy criteria would be most helpful when selecting subjects for use in investigations of both theoretical and applied aspects of dyslexia and may also have implications for qualifying students for dyslexia intervention services in schools and clinics. The validity of all such research studies ultimately depends on the assumption that the individuals in the study who are labeled with “dyslexia” do, in fact, have dyslexia (Flanagan et al. 2010; Flanagan et al.https://ayurvedia.ch/dead-space-game-manual 2006; Harrison and Holmes 2012; Kavale et al. 2009; Swanson 1991 ). This being the case, a set of valid identification criteria would allow researchers to replicate the findings of other researchers, a useful scientific activity. Using such criteria, researchers could confidently identify the incidence of dyslexia in the population, the causes of dyslexia, the correlates of dyslexia, and useful methods for teaching reading to students with dyslexia. The potential value of having valid criteria for identifying students who do in fact have dyslexia and thereby need special services and attention is obvious. While we agree that the traditional discrepancy method has its shortcomings, we believe that it can be revised into a valid method of identification on the basis of definitional criteria and specific cut scores. Further discussion of how we propose to revise the traditional discrepancy criteria to select students for special services is dealt with later in “ Procedures ” section of this paper. Definitions of Dyslexia The effort to develop a set of definition-based discrepancy criteria that can be used to identify students with dyslexia logically begins by studying existent definitions of dyslexia in order to determine those elements of agreement across definitions. Once identified, these elements can be converted into operational criteria and validated empirically. The definitions that were studied were taken from two popular dictionaries, three psychological dictionaries, two diagnostic manuals, a medical dictionary, two linguistic dictionaries, a special education and rehabilitation dictionary, a developmental disabilities dictionary, two professional organizations, and two notable persons in the field of dyslexia. A few of these definitions are old but still relevant, while the others are drawn from current editions of dictionaries and diagnostic manuals. The basic agreement among the definitions is noteworthy. These 16 definitions are presented in the Appendix of this paper. Readers who are not familiar with the field of dyslexia will find it useful to review the definitions at this point. Five Common Elements Among Dyslexia Definitions The 16 definitions agreed on five elements (i.e., the elements were mentioned specifically in over 50 of the definitions). Support for the idea that comprehension is a critical aspect of reading is found in the Reading First Impact Study Final Report (Gamse et al. 2008 ). This study investigated the impact of Reading First, a federal initiative designed to help all children read at or above grade level by the end of third grade. The definitions include those of Huey ( 1908, p. 6), Gray ( 1940, p. 6), Flesch ( 1955, p. 3), Goodman ( 1981, p. 477), and Kamhi and Catts ( 1989, p. 4). Evidence that impaired comprehension is the critical problem in the reading of students with dyslexia does not rest solely on the definitions. Results of every meta-analysis that has studied the correlates of reading have agreed that oral or silent measures of reading comprehension, identification of real words (either in isolation or in context), spelling, and writing conventions (i.e., orthographic rules) are the best predictors of reading ability. Graphophonemic abilities involving printed letters (e.g., sound-letter relations, grapheme-sound blending) are also important predictors of reading, though they lack the diagnostic accuracy of the best predictors (Hammill and McNutt 1981; Lonigan et al. 2009; Scarborough 1998; Swanson et al. 2003 ). Dyslexia Is Unexpected Nine definitions agreed that unexpectedly low reading ability, when contrasted with other cognitive abilities (e.g., speech, intellect), cultural opportunities, or emotional factors, is a defining characteristic of dyslexia. Specifically, the reading disability is not attributable to a more general intellectual disability; external factors such as economic or environmental disadvantage, chronic abuse, or lack of education; or to acquired neurological events (e.g., stroke, brain injury); or motor, vision, or hearing disorders. This element is the basis for the use of all discrepancy models for identifying dyslexia (e.g., achievement-aptitude discrepancies among cognitive abilities). This element was not operationalized because of the difficulty in setting psychometric criteria for neurological or genetic diagnostic factors. In reality, however, organicity does appear to play a role in understanding dyslexia. For example, any sample of students with dyslexia will exhibit very large differences between reading scores and cognitive scores and contain a disproportionately large number of boys. Both these observations could be interpreted as possible evidence for existence of genetic factors in individuals with dyslexia. In cases in which known readers lose their ability to read as a consequence of stroke, head injury, or similar maladies, no psychometric evidence of organicity is necessary. In such cases, we would use the term alexia rather than dyslexia. Numerous brain neuroimaging studies have found both structural and functional differences between individuals with dyslexia and nonimpaired controls (see Deutsch et al. 2005, and Shaywitz and Shaywitz 2008, for reviews of this research). These imaging studies reveal that individuals with dyslexia have differences in brain density and structure as well as differences in brain activation during reading tasks. Because of the complexity and plasticity of the brain, researchers are unable to determine if these differences are the cause of dyslexia or a result of dyslexia. Additionally, researchers using imaging and genetic tools have been unable to identify children with dyslexia at an acceptable level of diagnostic accuracy (Eicher and Gruen 2013; Shaywitz and Shaywitz 2008 ). Nonetheless, it is clear that some individuals with dyslexia may have differences in brain structure and function. We, however, do not attempt to provide operational criteria for this element. Operational Criteria for Four of the Five Dyslexia Elements The definitional elements of dyslexia described in the previous section were used to develop specific identification criteria for dyslexia. These specific criteria, based on four of the definitional elements, are discussed in detail in this section. Criteria for Element 1—Dyslexia Is a Serious Problem Because dyslexia is a serious reading problem, students should score 84 or fewer standard score points on word reading comprehension tests (i.e., more than 1.0 standard deviations below the mean) to satisfy this element. Such low scores are indicative of “low average” or below-average ability in reading. In fact, most people with dyslexia are accurate and fluent speakers. For the most part, they have difficulty extracting meaning from printed words and sentences that they have previously mastered in speech. This being the case, psychometric tests used to diagnose dyslexia should focus primarily on measuring print knowledge and word reading comprehension. When screening for or diagnosing dyslexia, avoid using tests of decoding or oral language, such as oral reading lists of nonwords or nonsense words, sound blending, sound discrimination, phonological awareness, auditory memory, or other phonemic-based tests that contain no graphemes. These tests yield phonological information that is more useful when identifying specific areas for intervention. Their use in identifying students with dyslexia can, however, result in unwanted numbers of false positives (see Catts et al. 2017 ). Criteria for Element 3—Dyslexia Is a Disorder Affecting Word Comprehension When selecting a suitable test of word comprehension to identify students with dyslexia, you should only use highly reliable (i.e., estimated alpha.90 or higher) measures of word reading comprehension. Specifically, word reading measures that assess the ability to read words accurately and fluently (either in isolation or context; silently or orally) should be given priority for use as measures of comprehension. Regardless of the measure and format that you choose, be sure to use highly reliable tests or subtests that measure the ability to read printed real words. Criteria for Element 4—Dyslexia Is Unexpected Finally, by definition, the degree of reading impairment should be unexpected when contrasted with other cognitive abilities (e.g., oral language, intellect), cultural opportunity, or emotional factors. Specifically, dyslexia is not the result of intellectual disability; external factors such as economic or environmental disadvantage, chronic abuse, or lack of education; or a neurological event (i.e., stroke or brain injury); or motor, vision, or hearing disorder. According to the DSM-5, individuals with intellectual disability have scores approximately 2.0 standard deviations or more below the population mean, including a 5-point margin for measurement error. On a test with a mean of 100 and a standard deviation of 15, we would begin to suspect intellectual disability at standard scores of 75 or lower. Both groups would be considered at risk for dyslexia. Method In this section, we provide initial empirical evidence to demonstrate the research basis for the revised discrepancy method discussed previously. Included are descriptions of the demographic characteristics of the subjects in the study, the specific measures used to assess reading comprehension and cognitive ability, and the procedures used to classify students as having dyslexia or not. Because our research is exploratory in nature, no attempt was made to collect a nationally representative sample of students receiving special services for dyslexia. Examiners were simply asked to collect data on students in their area who are officially diagnosed as having dyslexia. We did not ask how and when the students in the sample were originally identified as having dyslexia. Presumably, the identification criteria varied from state to state and from site to site. Similarly, we did not ask how long these students had been enrolled in intervention services for dyslexia. Most certainly this information should be the target of future research. The demographic characteristics of the total sample of 70 students presently being taught under the dyslexia label is provided in Table 1. Table 1 Demographic characteristics of the sample Full size table Measures Three well-known commercially available measures (two reading comprehension measures and one measure of general cognitive abilities) were administered to the sample. The measures are described briefly below. They both include four alternate forms that yield standard scores based on a mean of 100 and a standard deviation of 15. The subtests represent cognitive abilities that are developmental in nature and acquired incidentally as a result of environmental experience or directly as the result of instruction at home or school or self-study.Finally, the entire battery of subtests can be combined to form a General Cognitive Ability composite. We found very similar results in all cases; we chose to use the Reasoning Ability composite score as the estimate of cognitive ability in this paper because it was the most theoretically pleasing to us. We recognized, however, that the traditional discrepancy method has shortcomings. In an effort to improve upon the traditional discrepancy method, we created a revised discrepancy method based on common elements of the definitions for dyslexia, such as (a) a cut score floor for cognitive ability, (b) a cut score ceiling for reading ability, and (c) probability levels for likelihood of dyslexia. Many researchers (e.g., Fisher and DeFries 2002; Grigorenko 2005; Plomin and Kovas 2005; Shaywitz et al. 1992; Stanovich 1988; Wagner 2018 ) have suggested that specific reading disability (including dyslexia) lies on a continuum and have advocated for recognizing degrees or probability levels for dyslexia rather than a single cut score yielding a dichotomous classification. These are referred to as the three probability of dyslexia categories: Very likely: Students with Reasoning Ability standard scores of 76 points or more, Silent Reading Ability standard scores of 84 points or less, and Silent Reading Ability scores that were 30 points or more below the Reasoning Ability scores were included in this group. Likely: Students with Reasoning Ability standard scores of 76 points or more, Silent Reading Ability scores of 84 points or less, and Silent Reading Ability scores that were 15 to 29 points below the Reasoning Ability scores were included in this group. Not very likely: Students with Reasoning Ability standard scores of 75 points or less, or Silent Reading Ability standard scores of 85 points or more, or Silent Reading Ability scores that were 14 points or less below the Reasoning Ability scores were included in this group. Results In this section, we review the results of using our revised discrepancy method to reclassify the 70 students previously identified as having dyslexia into one of the three probability of dyslexia categories. Specifically, we discuss the demographic characteristics and the scores made by the students in each of the three categories. Student Demographics in the Three Probability of Dyslexia Categories In this study, we wanted to learn how well our revised discrepancy method for identifying students with dyslexia would match a sample of 70 students currently receiving educational services in schools under the dyslexia label. Therefore, we used our revised discrepancy method to reclassify the 70 students previously identified as having dyslexia into one of the three probability of dyslexia categories. The demographic characteristics for the three probability of dyslexia categories are presented in Table 2. Table 2 Demographics of the sample based on probability of dyslexia Full size table Based on our revised discrepancy method, 16 students met the criteria for the very likely category, 20 students met the criteria for the likely category, and 34 students met the criteria for the not very likely category. It is worth noting that 49 of the students were categorized as not very likely to have dyslexia according to our revised discrepancy method. As expected, the percentage of males (75) versus females (25) in the very likely probability category is consistent with the 2:1 to 3:1 male-to-female representation reported in the DSM-5. The percentage of males (53) versus females (48) in the likely probability category was more evenly split. The percentage of males (40) versus females (60) in the not very likely probability category is the opposite of what one would expect in a sample of students with dyslexia. The fact that 11 (69) of the students in the very likely probability category attend private schools could mean that (a) the parents of seriously impaired students with dyslexia are more likely to send their children to private schools than are parents of mild-to-moderately impaired students or (b) the parents of seriously impaired students with dyslexia are more financially affluent than the parents of students in the other groups. In any event, this finding is not surprising given the parent and professional advocacy for special services and more customized treatment for students with dyslexia. We examined the data for any systematic difference in how each group was categorized by the revised discrepancy method. We found that each age group was roughly equally represented in all three probability levels for dyslexia. Student Performance in Each Probability-of-Dyslexia Category Table 3 provides a score overview of the students in each probability of dyslexia category. In this table, we provide our own terms to describe the three levels of discrepancy. We describe students with reading-reasoning difference scores of 14 standard score points or less as insufficient for a dyslexia diagnosis, between 15 and 29 points as minimally acceptable, and 30 points or more as acceptable. When viewing the scores for the total sample (at the bottom of the table), one may reasonably conclude that, taken as a whole, the 70-case sample is minimally acceptable. While this finding is encouraging and interesting, it does not tell the whole story. A closer look at the table’s content indicates that 50 of the sample is not very likely to have dyslexia because, taken as a whole, this category does not pass the “unexpectedness” or “severity” criteria because the average difference between their reading and reasoning scores is only 7.68 points and their average reading score is 91.76 (“low average”). Table 3 Sample size, percentages, and average standard scores by probability for dyslexia category Full size table Our readers will find the data reported in Tables 4, 5 and 6 to be much more interesting. In these tables, we share with you the actual scores of each student in the three probability of dyslexia categories. Basically, we are sharing our data set with you. After parsing these scores, you can decide for yourself whether our three levels of dyslexia make good sense to you or not. Table 4 shows the scores of 16 students who met the dyslexia probability criteria for very likely. Most professionals working in dyslexia would probably agree that these students are very likely to have dyslexia. All six of these students passed the cognitive criterion (i.e., their reasoning composite scores were 76 points or more) and the unexpectedness criterion (i.e., their difference scores were 15 points or more) but failed the severity criterion. Taken as a group, we found no consistent pattern of cognitive strengths and weaknesses. On the individual level, some students did have clinically useful differences among abilities. Discussion In this section, we discuss the results of our study. Specifically, we (a) review the implications of this study for research and practice, (b) discuss the implications of reading correlates in identifying dyslexia, (c) review the implications for future directions and the study’s limitations, and (d) offer some conclusions. Implications for Research and Practice The common wisdom today is that the field of learning disabilities (LDs) and dyslexia is fraught with false positives and false negatives (Adelman 1992; Fletcher et al. 2014; Fletcher et al. 2018; Wagner 2018 ). In their prophetic article, Learning Disabilities: A Field in Danger of Extinction?, Mather and Roberts ( 1994 ) noted that “failure to discriminate LDs from other learning problems perpetuates misdiagnosis and threatens the integrity of the field” (p. 56). The revised discrepancy method described in this study can be used by researchers to identify students in their studies who have dyslexia and that most researchers might agree are prototypical examples of dyslexia (i.e., low reading ability, higher cognitive ability, and a difference of more than 1.0 standard deviation between the two scores). Currently, researchers rarely report in any detail how their samples are verified as having dyslexia. Without replicable dyslexia criteria, we can never be sure that any two studies of dyslexia are examining the same kind of subjects. When selecting a sample of students with dyslexia for research purposes, reading and cognitive ability cut points should be inflexibly applied because false negatives are less detrimental to the research results than false positives. Because questionable or problematic cases might or might not have dyslexia, they should be excluded in research samples. Research samples need to be as pure as possible. For research purposes, arbitrary cut points are an asset because gold standard samples are required in order to have confidence in the validity of the research. When identifying students with dyslexia for educational services in schools, we agree with Fletcher et al. ( 2018 ) that the cut points can be more flexible “or that confidence intervals be used around a particular cut point because false positive errors are less detrimental than false negative errors” (p. 59). In this case, arbitrary cut points are a detriment, but one that can be minimized. An examiner might use a confidence interval such as a standard error of estimation ( SE E ) around the cut scores to mitigate the effects of measurement error associated with the scores. SE E provides a band of error that reflects regression effects resulting in confidence intervals that are typically not symmetrically distributed around the obtained score (except those near the mean) but, rather, extend more toward the mean than away from the mean. See Salvia et al. ( 2017 ) for further discussion of this topic. Strictly speaking, the cases we have categorized as not very likely to have dyslexia do not meet our revised discrepancy criteria, but the identification of students to receive services should not be based on test results alone. Most current authorities (e.g., Flanagan et al. 2013; Fletcher et al. 2018; Wagner 2018 ) would probably agree that the identification of students with specific reading disabilities or dyslexia should not be based on a single factor (e.g., discrepancy between cognitive ability and reading achievement, severity of reading failure). Instead, they would likely recommend that diagnostic efforts should incorporate other important factors, as well. Any factors that lead to low achievement, including inclusion and exclusion criteria for dyslexia, need to be considered as part of a comprehensive evaluation. In the end, no methodology will eliminate all false positives or false negatives. Clinical judgment made by a qualified professional will always have an important role to play in the identification of students with dyslexia. A multifaceted approach should result in a more complete understanding of this disabling condition. In short, while all students with dyslexia are very poor readers, not all (or even most) poor readers have the condition. An important aim of a comprehensive evaluation should be to make that distinction. Implications of Reading Correlates in the Identification and Instruction of Dyslexia Many professionals believe that deficits in phonological (i.e., speech sounds) processing—specifically phonological awareness and oral vocabulary—cause difficulties in learning to decode, which leads to poor comprehension of printed words. However, the research in this area is far from convincing. Relative to the first point, we recommend reading Developmental Relationship between Language and Reading: Reconciling a Beautiful Hypothesis with Some Ugly Facts (Scarborough 2005 ). In her paper, Scarborough presents evidence and concludes that the phonological model has many inconsistencies and is incomplete in accounting for the relationship of oral to written language difficulties. For the second point, we recommend reading the Reading First Impact Study, Final Report (Gamse et al. 2008 ).