Reanalysis of a Meta-Analysis of the Validity of the Wechsler Scales in the Diagnosis of Learning Disability

1987 ◽  
Vol 10 (3) ◽  
pp. 198-202 ◽  
Author(s):  
James Inglis ◽  
J.S. Lawson
Keyword(s):  
Learning Disability ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Test Performance ◽  
Meta Analysis ◽  
Wechsler Scales ◽  
Components Analysis ◽  
Scale Data

A meta-analysis of Wechsler scale data on 9,372 LD children failed to distinguish these children from their normal peers on any of the ability patterns that have conventionally been held to characterize LD children's test performance. However, a reanalysis of their data using a learning disability index (LDI), derived from a principal-components analysis of the WISC-R, was found to discriminate reliably between the two groups of children.

Micro-Interpretation of a Meta-Analysis: A Reply to Inglis and Lawson

1987 ◽  
Vol 10 (3) ◽  
pp. 249-252 ◽  
Author(s):  
Steven R. Forness ◽  
Kenneth A. Kavale ◽  
Kazuo Nihira
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Meta Analysis ◽  
Explanatory Power ◽  
Components Analysis

Responding to a meta-analysis of 94 studies which fails to support the presence of distinctive Wechsler profiles for LD children, Inglis and Lawson contend that a distinctive LD pattern indeed exists. As evidence they cite their own pattern derived from an unrotated principal-components analysis. Although this approach is statistically sound, it nonetheless offers a limited empirical solution with little explanatory power beyond the finding that LD groups seem to find Verbal subscales more difficult than Performance subscales. Further, the authors' response implies a lack of understanding of the purposes of meta-analysis.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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