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Coverpages hosted by OASIS: Trading Partner Agreement Markup Language (tpaML), September 2000,

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Clinicians have long searched for characteristic subtest profiles associated with brain dysfunction and psychopathology with the same zeal that medical researchers apply to the search for biochemical patterns of cancer or AIDS patients, or that psychiatrists apply to the behavioral patterns of mass murderers. Mostly, these quests have not been fruitful, not unlike the quests of other scientists. Unforeseen and often unknown complexities frustrated Wechsler s search for a subtest profile associated with organic brain syndrome or the search of others for patterns that are pathognomonic of schizophrenia, psychotic depression, and countless other disorders. However, the fact that the search for characteristic profiles for groups has usually been unsuccessful does not alter the value of a diversity of theoretical and clinical approaches for gaining true in-

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Table 12-3 displays all 25 fitted values yi and the corresponding residuals. The fitted values and residuals are calculated to the same accuracy as the original data. Table 12-3 Observations, Fitted Values, and Residuals for Example 12-2 Observation Number 1 2 3 4 5 6 7 8 9 10 11 12 13 yi 9.95 24.45 31.75 35.00 25.02 16.86 14.38 9.60 24.35 27.50 17.08 37.00 41.95

8.38 25.60 33.95 36.60 27.91 15.75 12.45 8.40 28.21 27.98 18.40 37.46 41.46

[OMB2003]

Horn and Bannatyne formulations to interpret a variety of potentially meaningful dimensions. In addition to the sequential steps that examine the IQs and indexes, a method for calculating standard scores (mean = 100, SD = 15) for alternative subtest configurations is provided in this chapter. Examiners who choose to use these standard scores for Bannatyne and Horn clusters

12.26 15.81 18.25 64.67 12.34 36.47 46.56 47.06 52.56 56.31 19.98 21.00

will be able to apply the empirical system that is provided for determining significant strengths and weaknesses in a person s Horn or Bannatyne profile. Because the two interpretive approaches overlap to some extent, and both overlap with the four WAIS-III factor indexes, guidelines are discussed to help clinicians select the most appropriate system.

Computers are almost always used in fitting multiple regression models. Table 12-4 presents some annotated output from Minitab for the least squares regression model for wire bond pull strength data. The upper part of the table contains the numerical estimates of the regression coefficients. The computer also calculates several other quantities that reflect important information about the regression model. In subsequent sections, we will define and explain the quantities in this output. Estimating 2 Just as in simple linear regression, it is important to estimate 2, the variance of the error term , in a multiple regression model. Recall that in simple linear regression the estimate of 2 was obtained by dividing the sum of the squared residuals by n 2. Now there are two parameters in the simple linear regression model, so in multiple linear regression with p parameters a logical estimator for 2 is

Office of Management and Budget: E-Authentication Guidance for Federal Agencies, Memorandum M-04-04, Washington, DC, USA, 16 December 2003

This chapter integrates the logical analysis of each WAIS-III subtest ( 10) with the empirical treatment of the subtest profile to convert an individual s array of WAIS-III scaled scores into clinically meaningful hypotheses about his or her cognitive functioning. The two steps described in depth in this chapter (8 and 9) follow directly after Steps 1 7 presented in 11.

(12-16)

This is an unbiased estimator of 2. Just as in simple linear regression, the estimate of 2 is usually obtained from the analysis of variance for the regression model. The numerator of Equation 12-16 is called the error or residual sum of squares, and the denominator n p is called the error or residual degrees of freedom. Table 12-4 shows that the estimate of 2 for the wire bond pull strength regression model is 2 115.2 22 5.2364. The Minitab output rounds the estimate to 2 5.2.

Our goal in this chapter is to structure the examiner s task of identifying meaningful cognitive hypotheses that may be embedded in a person s WAIS-III profile by restructuring and regrouping the component subtests. Where these hypotheses have reasonably consistent research support re-

[OWASP] [Pel01] [Pet01]

12-1.4

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