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The project team decided to acquire permission to survey schools from the ve prefecture school directors whose disposition towards the project was estimated to be either neutral or positive Stakeholder interviews would be carried out following the completion of the selection phase and before the ministerial decision on the selected schools, so as any intervention to be addressed towards the ESF Directorate and not towards the project team Mapping power distribution proved to be a useful tool, and timing the in uence on dominate stakeholders was highlighted as, equally, important The timing was also right for school selection The summer school holidays were, in fact, the right time to survey schools with minimum disruption of school operation and minimum dissemination of project objectives, hence, minimising the probability of local community intervention.

10.2 A BRIEF REFRESHER OF PROBABILITY THEORY The main goal of this section is to review elementary probability theory results that are useful for analyzing the performance of our protocols. For a more detailed discussion of background material we refer the reader to [13]. For a random variable X, E[X] denotes the expected value of X. Let X be a random variable denoting the number of successes in n independent Bernoulli trials with para-

are analyzed at the student level and there is a sizable intraclass correlation, the standard errors may be too small, and inaccurate conclusions may be drawn If the data are analyzed only at the level of the classroom then power is substantially reduced Traditional methods also do not allow for cross-level predictors (ie, predictors from one level predicting outcomes at another level) For example, participation in DARE (a classroom-level variable) may predict students Acceptance of Risky Behavior (an individual-level variable) HLM models are speci ed in a multistage process A good- tting model is established hierarchically After good tting models are established at each phase, a multiple group model is tested that allows prediction across levels of measurement This ability is one of the strongest advantages of multilevel modeling.

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With the successful and timely completion of the school selection phase, the project team concentrated on mapping the next phases Emphasis was placed on the design and procurement phase leading to the implementation phase of the project Power distribution and dependencies were mapped as shown in Figure 207 The decision made to seek the cooperation of the University Technical Services was included However, monitoring and control of implementation on a maximum of 20 (17 after selected schools were announced) isolated (island) locations was not a simple task Regular monitoring was foreseen but for day-to-day updates, the project team had to depend on school principals and staff These dependencies were, also, mapped While studying the dependencies during the two successive phases, two stakeholders (the University and the Ministry of Education Personnel and Libraries) remained dominate, while the relation between the project and the (potential) subcontractors and the project.

In the example to follow, a multilevel model will be employed to examine a two-group model of children nested within classrooms After establishing a good tting model for the children (level 1), the second-level model (the classrooms) is added to the model, and a multiple group model is tested Speci cally, a level 1 (children) model is tested for each unit (in this example, each classroom) Then, hypothesized parameters from the individual level 1 models (regression coef cients, variances and covariances of independent variables, and intercepts) are used as dependent variables for second-level (classroom level) predictors An Empirical Example of Hierarchical Linear Models A simple example using data from program evaluations of Drug Abuse Resistance Education (DARE) in Colorado Springs will be used to illustrate an HLM model (Ullman, Stein, & Dukes, 2001) DARE.

dissatisfaction perceives it as a helpful emotion because this emotion helps him or her to try harder, to maintain a fighting spirit, to be better than his or her opponent, to put more effort into the game, and to be more alert Harmful effects of too much satisfaction (complacency) are reflected in being too concerned with success, not trying to play better, being too arrogant, not careful, and too risky; as result, skating becomes difficult (Hanin & Syrj , 1995, pp 180 181) A more detailed description of perceived functional effects of selected emotions across four global categories (P+, N+, P , and N ) is found elsewhere (Ruiz, 2004; Syrj , 2000) Explaining Individual Differences Numerous empirical studies revealed large interindividual variability of emotion intensity and emotion content in athletes performing similar and different sporting tasks.