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We determined that one possible explanation for her strong performance on the KAIT s Double Meanings subtest was a strength in her hypothetico deductive reasoning ability (also supported by her performance on Logical Steps = 99th percentile, Mystery Codes = 98th percentile, and global Fluid IQ = 98th percentile) Examination of her WAIS-III profile also shows support for this hypothesis with her relatively strong performance on both Matrix Reasoning (95th percentile) and Similarities (84th percentile) We would next investigate her complete background to see if there were any other data supporting this hypothesis Another hypothesis to explain her strong performance on Double Meanings is her reflective cognitive style First, we look to the behavioral observations of Dafne during the testing, which do support the fact that she tended to be reflective in processing information rather than impulsive Second, we look at her performance on the WAIS-III.

In many situations, the factor of interest has a large number of possible levels. The analyst is interested in drawing conclusions about the entire population of factor levels. If the experimenter randomly selects a of these levels from the population of factor levels, we say that the factor is a random factor. Because the levels of the factor actually used in the experiment were chosen randomly, the conclusions reached will be valid for the entire population of factor levels. We will assume that the population of factor levels is either of in nite size or is large enough to be considered in nite. Notice that this is a very different situation than we encountered in the xed effects case, where the conclusions apply only for the factor levels used in the experiment.

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Dafne tended not to do as well on speeded tests such as those in the Processing Speed Index (32nd percentile) and Object Assembly (16th percentile), which may be due, in part, to her reflective style Thus, the hypotheses that we investigated to explain her relative strength in KAIT Double Meanings seem to.

13-3.2

5

The linear statistical model is Yij e i j 1, 2, p , a 1, 2, p , n (13-19)

Example of How to Use the Guideline A. Double Meanings is selected as a significant strength for Dafne calculated in Step 6. B. As shown in Table 13.9, for all subtests that share an ability with Double Meanings, either a , +, or 0 was placed next to each subtest, as appropriate, indicating whether the scaled score was above, below, or equivalent to the mean scaled score of Dafne s Crystallized subtests.

where the treatment effects i and the errors ij are independent random variables. Note that the model is identical in structure to the xed-effects case, but the parameters have a different

This pattern addresses how to specify the rights of processes with respect to a new object. When a process creates a new object through a factory (see FACTORY METHOD and ABSTRACT FACTORY [GoF95]), the request includes the features of the new object. These features include a list of rights to access the object.

A. Select one of the statistically significant subtest strengths or weaknesses calculated in Step 6.

The variances 2 and 2 are called variance components, and the model, Equation 13-19, is called the components of variance model or the random-effects model. To test hypotheses in this model, we assume that the errors ij are normally and independently distributed with mean 0 and variance 2 and that the treatment effects i are normally and independently distributed with mean zero and variance 2.* For the random-effects model, testing the hypothesis that the individual treatment effects are zero is meaningless. It is more appropriate to test hypotheses about 2. Speci cally, H0: H1: If

B. Look at each ability and influence involved in that subtest (see Table 13.8) one at a time, and consider the person s performance on every subtest that involves that ability or influence. For each subtest that shares the ability, write a next to the subtest to indicate that performance was below the individual s own subtest scaled-score mean; write a + next to the subtest to indicate that performance was above the individual s own subtest scaled-score mean; write a 0 to indicate that performance was the same as the individual s subtest scaled-score mean (see Table 13.9 for an example of Guideline B).

2 2 2

This pattern addresses how to control access by a process to an object. Use a reference monitor to intercept access requests from processes. The reference monitor checks whether the process has the requested type of access to the object.

0, all treatments are identical; but if 2 0, there is variability between treatments. The ANOVA decomposition of total variability is still valid; that is, SST SSTreatments SSE (13-20)

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