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It should aid performers to put themselves in an optimally aroused, self-expectant, confident, and focused state immediately prior to as well as during execution Throughout the past 3 decades, starting at the early 1980s and continuing in the 1st decade of the twenty-first century, researchers and practitioners have examined the effectiveness of preperformance routines in self-paced tasks The purpose was not only to describe what skilled performers do before the execution of a self-paced task, but also to study the effectiveness of certain preparatory behaviors on achievement in individuals at mostly advanced, but also at early stages of, skill learning and development PREPERFORMANCE ROUTINES IN SELFPACED TASKS: EMPIRICAL SUPPORT The review on the effectiveness of preperformance routines in self-paced tasks is composed of 20 studies, which are divided into two categories: observational and experimental.



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Although a technical discussion of model identification is beyond the scope of this chapter (see Pearl, 2000), it is important that researchers have some understanding of this concept In nonmathematical terms, identification refers to whether one can obtain a unique solution estimate for the unknown parameters in the model If a model is identified, one and only one set of values will best reproduce the observed data, under the constraints imposed on the model by the researcher If more than one set of values can reproduce the data equally well, then the researcher will obtain only a set of arbitrary parameter estimates that have no substantive meaning regarding real-world effect sizes Such a model is referred to as underidentified If a unique set of parameters does not exist, then a model is not testable There are no fully satisfactory guidelines for identification.





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11 DCF does not adapt well under either low traf c load or congestion channel status due to a large number of competing stations Using one single CWmin, no matter big or small, yields very low throughput in most scenarios Second, the overhead of request-to-send/ clear-to-send/acknowledgment (RTS/CTS/ACK) and backoff per channel access consumes a signi cant portion of channel time, especially with high physical channel data rates and small frame sizes With the existing overhead, simply increasing the data rate cannot improve the throughput beyond a certain limit [2, 6] These two types of issues are called operating inef ciency and protocol inef ciency, respectively To further improve network capacity, multiple rate capability has been enabled and supported by most commercial wireless adapters For example, IEEE 80211b supports 2, 55, and 11 Mbps, depending on the channel condition and signal strength.

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In observational studies, the researcher observed the overt behaviors of the performer in a natural setting, such as when the performer is readying himself or herself for the execution of a self-paced sport task (Thomas & Nelson, 2005) Based on these observations, the researcher can accurately describe unique phenomena of the observed event, such as the pattern of behaviors demonstrated by an individual during his or her readying phase for the selfpaced act In experimental studies, the researcher can manipulate treatments or conditions, which have the potential to enhance behavior (Thomas & Nelson, 2005) One of the main objectives of experimental studies on preperformance routines is to examine the effectiveness of task-pertinent routines on performance quality Most of the studies on preperformance routines were conducted on two tasks: free throws in basketball and drives in golf.

Generally, given a certain bit error rate (BER) and maximum channel data rate, the higher the data rate used, the higher the loss ratio With higher loss ratio, data retransmission and collision probability signi cantly increase, yielding lower throughput To handle the trade-off between data rate and frame loss ratio, many adaptation protocols have been proposed to determine the best possible rate based on channel loss ratio [7 9], signal-to-noise ratio (SNR) [10, 11], or some channel quality measures [8] In this chapter, we provide a comprehensive study of the capacity analysis models and rate adaptation protocols, with an emphasis on various techniques that improve the network capacity Section 42 reviews representative performance models on network capacity, optimal throughput, and theoretical throughput limit Section 43 describes existing rate adaptation schemes such as autorate fallback (ARF) [7], receiver-based autorate (RBAR) [10], Onoe [9], and SampleRate [8] in detail.

A necessary, but not sufficient, test of identification is that the number of data points is greater than the number of parameters so that df is positive (Bollen, 1989) An underidentified model with df < 0 is not identified If a model is identified, and has df = 0, it is said to be just identified Because just-identified models will always be able to perfectly fit the data, they do not provide a test of the overall model (ie, they are not falsifiable) However, the parameter estimates from a just-identified model are still useful in describing relations among the variables If a model is identified and has df > 0, the model is said to be overidentified Most CFA /SEM models are overidentified; they can be tested in relation to their ability to fit the data and are falsifiable Having more date points than estimated parameters (ie.

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