SLD. Therefore, the team may look elsewhere for the cause of the academic deficit. Appendix XI provides information on two commonly used models of psychological processing and includes charts showing correlations between specific academic skills and the associated cognitive processing areas according to the CHC and Integrated School Neuropsychology/CHC model. 2. Evaluators are reminded of the requirement in MUSER V 2 B(2) and similar requirements in federal regulations that assessments must not be limited to a single measure or instrument. In order to adequately measure all cognitive processes, multiple measures must be used. Best practice suggests that multiple measures of a specific process should be administered and that the composite, cluster or index score should be utilized in calculating whether this score represents a significant strength or weakness (See Appendix II). It is also important to remember that inconsistencies within composite areas must be resolved through further testing using subtests/tests that measure similar constructs (i.e. fluid reasoning, comprehension/knowledge, auditory processing, etc.) The current regulations do not provide cut-off scores for calculating statistically significant strengths or weaknesses. Many assessment instruments provide publisher based statistical calculations of strengths and weaknesses. When these are not available, such as when comparing scores between different instruments, teams are encouraged to use best practices. Often, the standard deviation or confidence interval are used as guidelines for determining statistically relevant variance. 3. Considerations when assessing students who come from culturally and linguistically diverse backgrounds: Chapter 101 includes an exemption of the requirement for identifying a processing disorder using standardized measures for students who come from culturally and linguistically diverse backgrounds, have physical disabilities or other contributing factors that result in no normed instruments being available. For students who are outside the cohort on which the standardized assessments are normed, it is recommended that teams use a combination of grade based norms and data from a multi-tiered problem solving approach such as analysis of work samples and other performance data to determine the likelihood of a learning disability. 5. Relevant behavior noted during the observation(s) and its relationship to academic functioning (MUSER VII.2.L.(2)(f)). The child must be observed in the learning environment (including the regular classroom setting) to document the child’s academic performance and behavior in the areas of difficulty. (MUSER VII.2.L(2)(f)(i)) NOTES:
SLD. Therefore, the team may look elsewhere for the cause of the academic deficit. Appendix XI provides information on two commonly used models of psychological processing and includes charts showing correlations between specific academic skills and the associated cognitive processing areas according to the CHC and Integrated School Neuropsychology/CHC model. 2. Evaluators are reminded of the requirement in MUSER V 2 B(2) and similar requirements in federal regulations that assessments must not be limited to a single measure or instrument. In order to adequately measure all cognitive processes, multiple measures must be used. Best practice suggests that multiple measures of a specific process should be administered and that the composite, cluster or index score should be utilized in calculating whether this score represents a significant strength or weakness (See Appendix II). It is also important to remember that inconsistencies within composite areas must be resolved through further testing using subtests/tests that measure similar constructs (i.e. fluid reasoning, comprehension/knowledge, auditory processing, etc.) The current regulations do not provide cut-off scores for calculating statistically significant strengths or weaknesses. Many assessment instruments provide publisher based statistical calculations of strengths and weaknesses. When these are not available, such as when comparing scores between different instruments, teams are encouraged to use best practices. Often, the standard deviation or confidence interval are used as guidelines for determining statistically relevant variance. 3. Considerations when assessing students who come from culturally and linguistically diverse backgrounds: Chapter 101 includes an exemption of the requirement for identifying a processing disorder using standardized measures for students who come from culturally and linguistically diverse backgrounds, have physical disabilities or other contributing factors that result in no normed instruments being available. For students who are outside the cohort on which the standardized assessments are normed, it is recommended that teams use a combination of grade based norms and data from a multi-tiered problem solving approach such as analysis of work samples and other performance data to determine the likelihood of a learning disability. 5. Relevant behavior noted during the observation(s) and its relationship to academic functioning (MUSER VII.2.L.(2)(f)). The child must be observed in the learning environment (including the regular classroom setting) to document the child’s academic performance and behavior in the areas of difficulty. (MUSER VII.2.L(2)(f)(i)) NOTES:
Clinical%20Guidance%20on%20Implementation-%20For%20the%20Identification%20of%20Students%20Suspected%20of%20Having%20a%20Specific%20Learning%20Disability-2017%20.docx