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Although p values can help identify critical predictors, they tell us nothing about the direction and relative magnitude of effects Because hazard models represent relationships between the entire hazard pro le and predictors, specifying an understandable effect size is not easy One useful approach is to interpret the parameter estimate associated with each predictor in a way that is similar to interpreting a regression coef cient In continuous-time survival analysis the parameter estimate represents a difference in elevation of the log-hazard pro le corresponding to predictor values one unit apart The parameter estimate s sign indicates the direction of the movement, telling us whether positive differences in the value of the predictor correspond to positive or negative differences in the risk of event occurrence.

$60,000 ( 21,000) $39,000 $ 2,500 19,500 9,000 4,000 ( 35,000) $ 4,000

We nd it helpful to imagine the pro le on a log-hazard plot moving up (or down, if the estimate is negative) for a one-unit difference in the predictor Predictors with larger parameter estimates produce larger elevation differences per unit difference in the predictor (In discrete-time survival analysis, the conceptualization is identical, but the interpreter of the ndings is dealing with differences in the elevation of the logit, rather than log, hazard pro le) Even after considerable experience with hazard models, however, ready visualizations in the transformed world of log hazard may remain tortured A mathematically complex but intuitively simple approach involves the transformation of the outcome back into the more familiar metric of risk, antilogging parameter estimates as necessary Of course, a researcher must use different transformations and interpretations depending on whether continuous- or discrete-time models have been tted.

We illustrate these ideas with the continuous-time hazard model in Equation 221 Antilogging both sides, we have h(t) = e.

Step 3a: Selection. The sum of the tness values for the second generation is i f (x i ) = 0.076274, which means that the average tness among the chromosomes in the second generation is three times that of the rst generation. The selection probabilities are calculated and shown in Table 6.2.

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

(15.278)

demonstrated yet, it presumably would be the case that a spontaneous explicit measure could predict spontaneous behavior above and beyond that predicted by a spontaneous implicit measure. To the extent that these effects hold, it suggests that both dimensions of attitudes (implicit-explicit, spontaneous-deliberate) are important to consider in predicting behavior. After discussing the major approaches to attitude change in the next section, we return to the implicitexplicit attitude distinction and discuss some implications of this distinction for understanding attitude change.

P (Eij ),

23 that both SSE and MSE are reported to equal zero; this 2 is consistent with the perfect values for R 2 and Radj However, the standard error of the estimate, s, is reported to differ (however slightly) from zero We know that s = MSE; thus, it is likely that the zero and 100% results shown in Table 323 are.

approaches take a more interactive, needs-based approach to interventions L Hardy and Parfitt (1994) evaluated their participation in two philosophically different mental training programs The first program used a program-centered prescriptive approach in which consultants served as the experts to formally assess athletes mental skills and needs using inventories, interviews, and observation, and then provided written reports and tutorials to athletes and coaches, prescribing the mental training activities for athletes based on their individual profiles The second program scrapped the consultant as expert formal prescriptive approach of assessment and reports and focused on the needs of athletes and coaches from their perspective A key philosophical tenet of this athlete-centered approach was that the consultants, athletes, and coaches were all equal in terms of knowledge and expertise.

Consultants focused on being available to meeting athletes and coaches needs when requested and responded to the valuable insights and experiences that athletes and coaches brought to the consulting relationship using a collaborative, problem-solving intervention philosophy L Hardy and Parfitt (1994) admitted that the athletecentered program was more difficult for them as consultants in that they had to serve in unforeseen and multiple roles, yet their effectiveness as consultants was evaluated more positively by athletes in the athlete-centered program However, this is not to say that one philosophy is better than another, as intervention philosophies should be carefully developed with regard to program objectives, the social-cultural context, and consultant skills and training.