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There is a simple change that will x the problem with the proof. Instead of using X n , we will use the index W and prove a similar series of inequalities. Let W be uniformly distributed over {1, 2, . . . , 2nR }. Then Pr(W = W ) = Pe(n) and nR = H (W ) = H (W |W ) + I (W ; W ) 1 + Pe(n) nR + I (W ; W ) 1 + Pe(n) nR + I (W ; Y n ), (7.122) (7.123) (7.124)



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Table 28.1 Athlete Burnout Questionnaire Descriptive Statistics Available in the Extant Literature Physical / Emotional Exhaustion M (SD) 2.47 (0.83) 2.62 (0.86) 2.67 (0.57) 2.32 (0.72) Sport Devaluation M (SD) 2.02 (0.84) 2.02 (0.88) 2.00 (0.67) 2.16 (0.76) Reduced Accomplishment M (SD) 2.32 (0.74) 2.32 (0.76) 2.31 (0.55) 2.43 (0.68)

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Listing 9-4 (continued)





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corresponds to Guttman s (1954) equally spaced circumplex, and is given in Table 162 CIRCUM can also be used to t a less restrictive model, the unequally spaced circumplex Here no constraints are placed on the variables polar angles; they are also estimated, thus the model has more free parameters (ie, is less parsimonious) than the equally spaced alternative Finally, it is also possible using CIRCUM to estimate versions of the model in which neither angles nor communalities (ie, variables projections) are constrained (unequally spaced, unequal communalities model) This is the least parsimonious model, and arguably may not qualify technically as a circumplex (because the variables are not constrained to fall along the circumference of a circle) As a demonstration, we subjected the two PGI data sets to a CIRCUM analysis.

Peano et al., 2005

Schema Qualifier Context Qualifier 1 Priority 0..1 *

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For each data set, we successively tested the three models for t to the obtained correlation pattern Model t was evaluated using multiple indices, as is common practice in the SEM literature The t statistics included (a) 2 ; (b) F, the maximum likelihood discrepancy function; (c) GFI, the Goodness-of-Fit index (J reskog & S rbom, 1986); (d) AGFI, the Adjusted Goodness-of-Fit index (J reskog & S rbom, 1986); and (e) RMSEA, the Root Mean Square Error of Approximation (Browne & Cudeck, 1992; Steiger & Lind, 1980) Three of the measures 2 , F, and GFI are absolute measures of model t, whereas two measures AGFI and RMSEA are parsimony-weighted and thus compensate for the model s complexity (GFI and AGFI were computed from formulas presented in Maiti and Mukherjee (1990), and based on directions generously provided by Michael Browne) Table 16.

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Once the analysis step has been completed, we are ready to restructure the results to remove redundancies and make sure the schema is complete and minimal. There are no redundancies in our integrated schema so far, but there are structures that can be combined into hierarchies. As mentioned before, Grad_Student is a subtype of Student. Although each student takes classes, only graduate students can work and get support from a department. This supertype/subtype structure is shown as a thick double-lined arrow from the subtype to the supertype in Figure 12.21. Since each subtype inherits all the attributes and relationships of its supertype, the Name attribute type only needs to be associated with Student and not directly de ned for the Grad_Student. Similarly,

4 presents the results of the CIRCUM analyses Considering the female sample rst, none of the three models had poor t (eg, GFI and AGFI generally > 9, RMSEA < 13); but, even with adjustments made for their greater complexity (ie, more free parameters), the unequally spaced models appeared to offer better t than the highly constrained equally spaced circumplex CIRCUM also yields estimates of the variable s polar angles Examining these (not shown), it is apparent that most of the variables were indeed evenly spaced 45 gaps); however, a larger-than-expected gap of.

Essential Windows CE Application Programming by Robert Burdick Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471327476 Pub Date: 03/01/99

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