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In short, a great deal happens in three years the effects of intervention, developmental changes, regression to the mean, and changes in what some subtests measure at different ages. The group data provided by Watkins and Canivez do not have implications for the individual method of profile interpretation that we advocate. The strengths and weaknesses that we believe might have useful applications for developing educational interventions are based on cognitive functioning at a particular point in time. They need to be cross-validated at that time to verify that any supposed cognitive strengths or weaknesses are consistent with the wealth of observational, referral, background, and other test data that are available for each child who is evaluated. Only then will those data-based findings inform diagnosis and be applied to help the child.

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A NEW THEORY-BASED APPROACH TO PROFILE INTERPRETATION et al, 1992) Indeed, one of us (DPF) has been frankly critical of ipsative analysis that ignores normative analysis (Flanagan & Ortiz, 2002) We have relied on all of these criticisms to modify and enhance our interpretive method Following are a few of the most salient ways in which we and our colleagues have attempted to improve the practice of ipsative analysis (Flanagan & Kaufman, 2004; Kaufman & Kaufman, 2004; Kaufman, Lichtenberger, Fletcher-Janzen, & Kaufman, 2005) First, we recommend interpreting test data within the context of a well-validated theory Use of the Cattell-Horn-Carroll (CHC) theory of the structure of cognitive abilities is becoming commonplace in test construction and interpretation because it is the best supported theory within the psychometric tradition (Daniel, 1997; Flanagan & Ortiz, 2001; Horn & Blankson, 2005; McGrew, 2005).

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Without knowledge of theory and an understanding of its research base, there is virtually no information available to inform interpretation Second, we recommend using composites or clusters, rather than subtests, in intra-individual analysis Additionally, the clusters that are used in the analysis must represent unitary abilities, meaning that the magnitude of the difference between the highest and lowest score in the cluster is not uncommon in the general population Furthermore, the clusters that are included in the interpretive analysis should represent basic primary factors in mental organization (eg, visual processing, auditory short-term memory) When the variance that is common to all clusters (as opposed to subtests) is removed during ipsatization, proportionately more reliable variance remains And, it is precisely this shared, reliable variance that we believe ought to be interpreted because it represents the construct that was intended to be measured by the cluster.

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For example, when the following clusters are ipsatized Gf, Gc, Gsm, Gv, and Glr the variance that is common to all of them (presumably g ) is removed, leaving the variance that is shared by the two or more tests that comprise each cluster That is, if the Gf cluster emerged as a significant relative weakness, then our interpreta-.

The simple finding that reevaluation data at age 12 do not support the stability of children s data-based strengths and weaknesses at age 9 says nothing about the validity of Kaufman and Lichtenberger s interpretive approach. If one s blood pressure is high when assessed in January and is normal when assessed three months later, does this suggest that the physician s categories (e.g., high, normal, low) are unreliable Does it suggest that the blood pressure monitor is unreliable Or, does it suggest that the medication prescribed to reduce the individual s blood pressure was effective

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