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Setting and removing breakpoints A breakpoint allows you to stop a movie running in the Flash Player at a specific line of ActionScript. You can use breakpoints to test possible trouble spots in your code. For example, if you ve written a set of if..else if statements and can t determine which one is executing, you can add a breakpoint before the statements and step through them one by one in the Debugger.

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This conception of psychopathy is also consistent with evolutionary perspectives that see strivings for status in the face of competition for resources as a universal adaptation Competition for resources, including mates, involves impeding others chances of acquiring resources that may take the form of stealing, cheating, attacking, humiliating, or ensuring compliance of the other, and several writers associate psychopathy with a cheating strategy (Beck & Freeman, 1990; Harris et al, 2001; Mealey, 1995; Millon & Davis, 1996) Although not addressing psychopathy, Gilbert (1989) suggested that competitiveness is a major source of individual differences reflected in the hostile-dominance described by the interpersonal circle..

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with basis parameters A[t](g) de ned by the application. Figure 18.10 gives a path diagram representing several kinds of multiple-group growth models (McArdle, 1991; McArdle & Epstein, 1987; McArdle & Hamagami, 1992). The persons in the groups are assumed to be independent, so this kind of grouping can only be done for observed categorical variables (i.e., sex). The rst two groups in Figure 18.10 can be considered as data separated into males or females (or experimentals and controls). Although not necessary, in Figure 18.10 we assume some of the Y[t] occasions were considered incomplete, possibly to represent a collection gathered at unequal intervals of time. In structural modeling diagrams (and programs), the unbalanced data for Y[3] and Y[5] are simply included as latent variables (see McArdle & Aber, 1990). In any case, this multiple-group model now allows us the opportunity to examine a variety of invariance hypotheses. The multiple-group growth model permits the examination of the presumed invariance of the latent basis functions, A[t](1) = A[t](2) = . . . A[t](g) = . . . A[t](G) . (18.16) The rejection of these constraints (based on 2 /df ) implies that some independent groups have a different basic shape of the growth curve. This is one kind of model that is not easy to represent using standard mixed-effects or multilevel models (Equation 18.13). If a reasonable level of invariance is found, we can further examine a sequence of other group differences. For example, we may examine the equality of the

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McArdle, J J, & Cattell, R B (1994) Structural equation models of factorial invariance in parallel proportional pro les and oblique confactor problems Multivariate Behavioral Research, 29(1), 63 113 McArdle, J J, & Epstein, D B (1987) Latent growth curves within developmental structural equation models Child Development, 58(1), 110 133 McArdle, J J, Ferrer-Caja, E, Hamagami, F, & Woodcock, R W (in press) Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the lifespan Developmental Psychology McArdle, J J, & Goldsmith, H H (1990) Some alternative structural equation models for multivariate biometric analyses Behavior Genetics, 20(5), 569 608 McArdle, J J, & Hamagami, E (1991) Modeling incomplete longitudinal and cross-sectional data using latent growth structural models In L M Collins & J L Horn (Eds.

), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp 276 304) Washington, DC: American Psychological Association McArdle, J J, & Hamagami, E (1992) Modeling incomplete longitudinal and cross-sectional data using latent growth structural models Experimental Aging Research, 18(3), 145 166 McArdle, J J, & Hamagami, F (1996) Multilevel models from a multiple group structural equation perspective In G Marcoulides & R Schumacker (Eds), Advanced structural equation modeling techniques (pp 89 124) Hillsdale, NJ: Erlbaum McArdle, J J, & Hamagami, F (2000) Linear dynamic analyses of incomplete longitudinal data In L Collins & A Sayer (Eds), Methods for the analysis of change (pp 137 176) Washington, DC: American Psychological Association McArdle, J J, Hamagami, F, Meredith, W, & Bradway, K P (in press) Modeling the dynamic hypotheses of Gf-Gc theory using longitudinal life-span data.

Caruso, J., & Cliff, N. (1997). Empirical size, coverage, and power of con dence intervals for Spearman s rho. Educational and Psychological Measurement, 57, 637 654.

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