scholarly journals A Revised Discrepancy Method for Identifying Dyslexia

2020 ◽  
Vol 6 (1) ◽  
pp. 27-43
Author(s):  
Donald D. Hammill ◽  
Elizabeth A. Allen
Keyword(s):  
Discrepancy Method

The Discrepancy Method

2000 ◽  
Author(s):  
Bernard Chazelle
Keyword(s):  
Discrepancy Method

Improving correlation accuracy of crashworthiness applications by combining the CORA and MADM methods

2021 ◽  
pp. 095440702110696
Author(s):  
Christophe Bastien ◽  
Alexander Diederich ◽  
Jesper Christensen ◽  
Shahab Ghaleb
Keyword(s):  
Numerical Models ◽  
Monotonic Functions ◽  
Virtual Test ◽  
Industry Standard ◽  
Discrepancy Method ◽  
Original Equipment Manufacturers ◽  
The Relationship ◽  
Correlation Accuracy ◽  
New Framework ◽  
Selection Of

With the increasing use of Computer Aided Engineering, it has become vital to be able to evaluate the accuracy of numerical models. This research poses the problem of selection of the most accurate and relevant correlation solution to a set of corridor variations. Specific methods such as CORA, widely accepted in industry, are developed to objectively evaluate the correlation between monotonic functions, while the Minimum Area Discrepancy Method, or MADM, is the only method to address the correlation of non-injective mathematical variations, usually related to force/acceleration versus displacement problems. Often, it is not possible to differentiate objectively various solutions proposed by CORA, which this paper proposes to answer. This research is original, as it proposes a new innovative correlation optimisation framework, which can select the best CORA solution by including MADM as a subsequent process. The paper and the methods are rigorous, having used an industry standard driver airbag computer model, built virtual test corridors and compared the relationship between different CORA and MADM ratings from 100 Latin Hypercube samples. For the same CORA value of ‘1’ (perfect correlation), MADM was capable to objectively differentiate between 13 of them and provide the best correlation possible. The paper has recommended the MADM settings n = 1; m = 2 or n = 3; m = 2 for a congruent relationship with CORA. As MADM is performed subsequently, this new framework can be implemented in already existing industrial processes and provide automotive manufacturers and Original Equipment Manufacturers (OEM) with a new tool to generate more accurate computer models.

Revisiting Discrepancy Theory in Learning Disabilities: What Went Wrong and Why We Should Go Back

2012 ◽  
Vol 23 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Sarah Callinan ◽  
Everarda Cunningham ◽  
Stephen Theiler
Keyword(s):  
Learning Disabilities ◽  
Response To Intervention ◽  
Cognitive Processes ◽  
Baseline Measure ◽  
Discrepancy Theory ◽  
Discrepancy Method ◽  
Theoretical Issues ◽  
Iq Tests ◽  
Identification Procedures

The rise in popularity of Response to Intervention (RTI) as a method of identifying Learning Disabilities (LD) is partially due to the psychometric and theoretical issues inherent to the use of IQ tests in the once popular discrepancy method of identification. However, both RTI and discrepancy theories have their shortcomings, and criticisms directed at either method are usually applicable to both. This conceptual article puts forward a justification for using tests of the cognitive processes that are implicated in LD as a better method of LD identification. Although the unsuitability of the discrepancy method to accurately identify LD students is well established, it does represent the construct of LD well. Therefore, the discrepancy method can be used as an effective baseline measure against which improved identification procedures based on cognitive processes can be measured. Once these cognitive processes are more clearly defined, tests of these processes offer promise for LD identification.

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