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may make the athlete wish to demonstrate superiority to others, and so the athlete becomes ego-involved in the task Thus, goal states are dynamic and ebb and flow depending on the perception of the athlete The measurement of goal states is a particularly challenging task It has been done in three ways One has been to take an existing goal orientation measure and reword the stem to obtain a state measure (eg, Hall & Kerr, 1997; Williams, 1998) A second has been to use single-item measures asking participants to indicate whether they focus on achieving a personal standard of performance (self-referenced) or beating others in an upcoming contest (other-referenced; eg, Harwood & Swain, 1998) The third way is to ask participants to view video replays of the event and retrospectively reflect on their goal involvement at any one point in the contest (eg, J.

= SSE( X 1 X 2 X 3 ) SSE( X 1 X 2 X 3 X 1 X 3 X 2 X 3 ) = 388.5385 360.7500 = 27.7885 with ( p 1)(q 1) = 2 degrees of freedom. The regression error sum of squares corresponding to SSWCELL in ANOVA is

Smith & Harwood, 2001) Although the first two procedures may be more predictive of the initial state of involvement than the orientation measures per se (Duda, 2001), Duda has argued that these procedures may not capture the essence of task and ego involvement In addition, it may be argued that because the states are so dynamic, even if you are able to reflect the state of involvement at the outset of the competition, as the state of involvement ebbs and flows as task and competitive information is processed, we have no indication of the changes that may occur (Roberts, 2001) It is naive and conceptually inconsistent to assume that the state of involvement will remain stable throughout the contest The best way of estimating the state of involvement currently available is the procedure used by J Smith and Harwood (2001).

At least we obtain participants observations of their goal involvement at different times of the contest This is a superior procedure to determine goal involvement that takes into consideration its dynamic nature However, this procedure is very labor-intensive; it has to be done with each participant over the course of the contest Clearly, the development of an assessment procedure for the state of goal involvement is a major task, especially when one recognizes that achievement goal theory is predicated on one s task or ego involvement in the achievement task As has been the case with measuring state anxiety, obtaining repeated measures while an athlete is engaged in competition is a practical nightmare.

And we have to recognize that repetitive assessments of goal involvement during a competitive encounter may have the effect of changing an athlete s goal involvement state (Duda, 2001)! Certainly, forcing task-involved athletes to consider why they are doing what they are doing may make them more self-aware and ego-involved in the task To reduce the like-.

6 5 4 Sensory-discriminative 3 2 1 0 15 30 45 60 75 90 105 120 Motivational-affective Cognitive-evaluative

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Small, W. W. (1900). An experimental study of the mental processes of the rat. American Journal of Psychology, 11, 133 165. Spence, K. W. (1956). Behavior theory and conditioning. New Haven, CT: Yale University Press. Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. Psychological Review Monograph Supplements, Vol. 2 (No. 4, Whole No. 8). Tinbergen, N. (1953). The herring gull s world. London: Collins. Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison Wesley. Washburn, M. F. (1936). The animal mind (4th ed.). New York: Macmillan. Zucchini, W. (2000). An introduction to model selection. Journal of Mathematical Psychology, 44, 41 61.

mental transactions. Undoubtedly, the more modern method of performing the night audit on a PMS will be used, but you should also be familiar with the components up close and personal.

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Two main classes of estimation algorithms have been proposed: worst-case and movingaverage estimators. The former are very simple, as they use the Pe(k) value related to the last frame. The latter are real estimators, as all the system history contributes to the current Pe(k) estimates. For example, Figure 7.16 shows the system throughput by assuming that the system interference characteristics generate linear Pe(k) values, i.e., Pe(0) = 0, Pe(16) = 1, and that the function increases linearly among these two extremes. In the figure, we also show the ideal case results that have been analytically derived by using the Pe(k) formula. As shown in the figure, it is possible to design Pe(k) estimators that drive the system close to its ideal behavior, and that also work well with a channel that shows a rapid change of the interference levels.