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Alternative Approaches Simonton (1977) proposed a procedure that avoids the problem of model identi cation by using an estimate of the variance-covariance matrix based on pooling the observations across all subjects observed. This approach also requires a basic assumption, namely that all series are assumed to be an ARIMA (1, 0, 0) model. Although this assumption seems to be theoretically indefensible, empirical investigations indicate that this procedure works well in a wide variety of cases (Harrop & Velicer, 1985). Algina and Swaminathan (1977, 1979; Swaminathan & Algina, 1977) have proposed an alternative in which the sample variance-covariance matrix is employed as an estimator for T T in the modi ed least squares solution (see Equation 23.7). This approach, however, requires the assumption that the number of subjects is greater than the number of observations per subject. This is not a condition that is likely to be met in most applied research settings, where time series approaches are most appropriate. Velicer and McDonald (1984) have proposed a third alternative. Instead of trying to determine the speci c matrix, they have proposed the use of a general transformation matrix with the numerical values of the elements of T being estimated for each problem. The rationale for a general matrix is that all transformation matrices, T, have an identical form and use a lower triangular matrix with equal subdiagonals. Weight vectors with ve nonzero weights were found to be accurate for most cases. A greater number of weights can be employed where indicated by appropriate diagnostics (Velicer & McDonald, 1984). The accuracy of this approach has been supported by two simulation studies (Harrop & Velicer, 1985, 1990b), and it can be implemented with most existing computer programs by specifying a high-order autoregressive model, such as an ARIMA (5, 0, 0) model.

communicative (see Hanin, 1997, 2000, for a review) From this perspective, situational emotional experience (eg, anxiety or anger) is a component of the psychobiosocial state related to nonemotion components Current individual-oriented research focuses on emotional, motivational, and bodily components of performance state and their interactive effects Recent empirical evidence indicates that to describe performance-related experiences, athletes use their own vocabulary of idiosyncratic labels However, an athlete s vocabulary describing performance-related states usually includes not only self-generated emotion words but also labels describing nonemotion experiences: cognitive, motivational, bodily, motor-behavioral, operational, and communicative (Hanin, 1997; Hanin & Stambulova, 2002; Ruiz & Hanin, 2004a, 2004b) For instance, Hanin and Stambulova examined emotional experiences prior to, during, and after personally best and worst competitions in 85 skilled Russian athletes using a metaphor-generation method Each athlete had to complete a sentence, Prior to my best competition I felt like .

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