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In field studies, where access to athletes is often limited, Feltz and Lirgg (2001) recommended that measures be taken at least within 24 hours of performance Myers, Paiement, and Feltz (in press) provide empirical evidence for this recommendation, and therefore, this recommendation served as the basis for judgment of adequate temporal proximity in our review of the related literature Research designs that allow for proximal and concordant measures of collective efficacy and subsequent performance are well situated to provide maximal information about this relationship, assuming other design flaws are not present Illustrated in Table 362, not all of the relevant studies were judged to have an acceptable level of both collective efficacy and team performance concordance, and collective efficacy and team performance proximity About half of the studies were judged to meet both criteria to a reasonable degree (ie.

In this section, I describe some additional charting techniques. I discuss two examples that demonstrate how to use VBA to change the data used by a chart.

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FIGURE 3.18 Service allocation versus frequencies in Galileo. (Source: Galileo Joint Undertaking.)

(41)

FIGURE 2.7 Comparison of total cost for intradomain optimization without consideration of WPAN interference.

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, Bray, 2004; Feltz & Lirgg, 1998; Hodges & Carron, 1992; Myers, Feltz, et al, 2004; Myers, Payment, et al, 2004) In all of these studies, the expected relation between collective efficacy and subsequent team performance was observed As predicted by Bandura (1997), studies that maximize both measure concordance and temporal proximity of the measures are quite likely to demonstrate the expected relationships Subsequent studies should work toward such designs The Lichacz and Partington (1996) study was judged to not meet the temporal proximity condition In this study, collective efficacy appears to have been measured after performance The order of these measurements would not have been judged to be problematic if the goal was to predict subsequent collective efficacy with a relevant prior team performance Unfortunately, the authors appeared to.

In the preceding example, Item is an object variable (more specifically, a Worksheet object). There s nothing special about the name Item; you can use any valid variable name in its place.

1. 2. 3. 4. 5. 6. 7. 8.

10:3

(16.55)

Figure 26.5 Barbara and Imilienski s TS/AT scheme (L = broadcast latency; w = window size; k > 1 for the TS scheme and k = 1 for the AT scheme).

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well as to nonexperimental research and is useful in evaluating the researcher s interpretation of the experimental manipulation Overview of the Conduct of Studies Using Confirmatory Factor Analysis and Structural Equation Modeling In this section I provide an overview of some critical issues relating to the conduct, interpretation, and reporting of the results of CFA /SEM analyses Although based in part on my own experience, I also acknowledge useful overviews, including textbooks (eg, Bollen, 1989; Byrne, 1998; Hancock & Mueller, 2006; Kaplan, 2000), chapters (eg, Hoyle & Panter, 1995; Millsap, 2002), and journal articles (eg, Boomsma, 2000; McDonald & Ho, 2002; Shah & Goldstein, 2006) It is convenient to discuss three broad stages of the conduct of CFA /SEM studies: model specification, parameter estimation, and model fit evaluation.

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