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CONCLUSION Although this chapter separated the influence of parents and siblings into two different sections, in reality, the two units probably exert an interactional influence. Thus, familybased research is necessary. One recent example of this approach was provided by C t (1999), who conducted indepth interviews with four families, each of which included an elite young athlete. The interviews were conducted using a retrospective format so that family influences on the individual child s sport career could be assessed from a developmental perspective. As the results of this study clearly showed, the role of parents and siblings (as well as significant others in the child s sport environment) changed as a function of the athlete s age and skill level. It was also apparent that the sibling relationship changed over time as well. Thus, this study, although limited to four families, does provide a broader and more inclusive perspective on the role of the family as a whole and on its individual parts. Using a more quantitative approach, Fredricks and Eccles (2005) conducted a broader-based assessment of family influences in the sport setting. These researchers used self-report questionnaires to assess a variety of family environmental variables as well as children s attitudes, values, beliefs, and behaviors across a 3-year period. Obtained data were examined using both standard regression procedures and pattern-centered analyses. The value of this multiple analytic approach was reflected in the fact that Fredricks and Eccles were able to assess the cumulative effects of a number of sport-related promotive factors that were present in the family context. Thus, this research study also extends our knowledge and understanding regarding the role that all individuals in the family context play.

Fisher (1925) proposed that researchers supplement null hypothesis signi cance tests with measures of strength of association Since then over 40 supplementary measures of effect magnitude have been proposed (Kirk, 1996) The majority of the measures fall into one of two categories: measures of strength of association and measures of effect size (typically, standardized mean differences) Hays (1963) introduced a measure of strength of association that can assist a researcher in assessing the importance or usefulness of a treatment: omega squared, 2 Omega squared estimates the proportion of the population variance in the dependent variable accounted for by a treatment For experiments with several treatments, as in the weight-loss experiment, partial omega squared is computed For example, the proportion of variance in the dependent variable, Y, accounted for by treatment A eliminating treatment B and the A B interaction is denoted by 2 |A B, AB .

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IP-PBX, IP CENTREX, AND IP-BASED PBX TIE LINES IP-PBXs are PBX devices that support the following: a. Various IP telephony and/or VoIP features; b. Call processing/control and attendant features/functions that are available from traditional circuit-switched PBXs; c. One or more of the following types of phones: analog, digital, ISDNBRI, IP, and so on; and d. One or more T1/E1-CAS/PRI links and digital subscriber lines (DSLs) for connectivity to PSTN switches and IP trunks for local and/or wide area data/packet networking. The IP trunks can be used to interconnect the IP-PBXs of a corporation in di erent geographical locations over an IP-based corporate virtual private network (VPN). Deployment of IP-PBX not only reduces the costs and enhances the features and capabilities of enterprise communications, it also simpli es the software upgrading and management of the integrated voice and data infrastructure. In addition, IP tie lines or IP trunks can be used to interconnect the IP-PBXs in di erent geographical locations. The use of IP tie lines can (a) make the same advanced call control features of the corporation s headquarters available to employees in remote branch locations and (b) allow employees to hold conference calls over a wide geographic area, avoiding long-distance telephone charges.

Similarly, 2 |B A, AB denotes the proportion of the Y Y variance accounted for by treatment B eliminating treatment A and the A B interaction For the weight-loss experiment, the partial omega squareds for treatments A and B are, respectively, ( p 1)(FA 1) ( p 1)(FA 1) + npq (3 1)(4247 1) = 018 (3 1)(4247 1) + (5)(3)(2).

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