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, 1983) and negative (Brewer, Cornelius, Van Raalte, Brickner, Sklar, et al, 2004; Feller et al, 2004; Pizzari et al, 2005; Shelbourne & Wilckens, 1990) correlations between adherence and outcome have been obtained in other studies (or analyses within studies) It is likely that the magnitude and direction of the adherence-outcome correlation depends on a variety of factors, including the nature of the injury, the rehabilitation protocol, the phase of rehabilitation, the particular measures of adherence and outcome (Brewer, 1999a), and the age of the individuals undergoing rehabilitation (Pizzari et al, 2005) Further complicating the adherence-outcome relationship is the possibility that athletes who find themselves healing quickly from injury may perceive their recovery as independent from their behavior and reduce their adherence to rehabilitation accordingly.

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Pseudoiteration: Use anything as the initial estimate, solve for the number of factors (see the following discussion for how to estimate the number of factors), and calculate the communalities from these factors Then use the observed communalities as new estimates of the communalities, extract factors again, and calculate the communalities from these factors Continue the process until little change is noted from one pass to the next or a maximum number of passes has made Note that this is not true iteration True iteration occurs when it has been proven both that the iterated values necessarily converge and that they necessarily converge to the right values But neither necessarily happens with pseudoiteration Gorsuch (1974, 1983) has noted a case in which the process would not converge, so the requirement for true iteration that the values converge is not met.

The condition that they converge to the right values is not met because they sometimes converge to an impossibly large value For example, in practice, communalities computed by this process often exceed 10 (Values greater than 10 are referred to as Heywood cases after the author of the rst published discussion of the situation Actually, those using the criterion of 10 to conclude the estimates are incorrect are optimists; the actual upper limit for communalities are the reliabilities of the variables, which are almost always less than 10 Thus, more violations of the upper limit occur than just the Heywood cases) The fact that the process need not converge to values that are possible means this process is not an iterative process in the mathematical sense In mathematics a procedure is iterative if and only if it is found to converge on the population value.

Therefore the so-called iteration for communalities is only pseudoiteration Why is pseudoiteration widely used I suspect that there are two reasons First, mathematical iteration is an excellent procedure, so iteration was certainly worth a try even though there is no mathematical proof it meets mathematical criteria for iteration Second, when starting from 10 as the initial communality estimate, we see that the rst few pseudoiterations obviously lower the communality estimates from the too-high value of 10 to a more reasonable estimate SMCs with two to three iterations: This procedure starts with the SMC noted previously Then the solution is iterated two or three times and stopped Although it is still a pseudoiteration, it has never in my usage produced an estimate over 10 Snook and Gorsuch (1989) found the resulting communalities to not differ signi cantly from the communalities designed into the study.

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where is the molecular weight of the molecule concerned. At T = 20 C and = 100, v T = 249.1 m s 1 , which suggests that mass transfer associated with free molecular velocities would be very fast. The mass ux onto a surface (mass per unit area per unit time) associated with the free molecular distribution, (3.3), is obtained by integrating cmvx with positive v x over the distribution:

element into a rst segment, followed by another segment. Figure 4.25 shows the subdivision of the ESD element into four segments. As more segments are added, and the size of the network load is more distributed, the network becomes a more ideal transmission line, and the broadband matching of the 50 input source and the 50 output load improves [34 40]. The RF ESD design practices can be as follows: With a distributed RF ESD network, the deleterious loading effects of an ESD network can be reduced (e.g., impedance mismatch, re ection, and power transfer). With a distributed RF ESD network, s-parameter degradation of re ection parameter S11 and transmission parameter S21 is minimized (compared with single lumped RF ESD network). With transmission lines in a distributed RF ESD network, circuit linearity can be improved because of the nonvoltage dependent nature of LC transmission lines.