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where Z is the moderator variable (here, risk), s11 and s33 are the variances of the b1 and b3 regression coef cients, respectively, and s13 is the covariance between these regression coef cients. A t test for the signi cance of the simple slope is given as tb at Z = bat Z /sb at Z (19.31)

The response probability i|g from equation 333 is now reformulated in line with the Rasch model It can be seen that when there is only one latent class, the MRM is reduced to the Rasch model When there are different classes but no variation in the ability parameters within each class, the MRM correspondingly becomes a simple latent class model However, through its combination of latent class models and the Rasch model, the MRM provides two parameters for each person Based on their response patterns, persons are assigned a value on the qualitatively scaled latent class variable, and based on the number of items solved, they are assigned a class-specific, quantitative person parameter An extension of the MRM, the hybrid MRM (see, eg.

, Rost & von Davier, 1995), permits a free mixture of (a) latent classes with no quantitative differences between the persons and ( b) latent classes with quantitative person differences Of course, a minimum of two classes is necessary Von Davier and Rost (1995) presented an instructive application of the hybrid MRM model in a reanalysis of the self-monitoring data set from Gangestad and Snyder (1985) There are not only dichotomous latent class models and MRM models but also ordinal latent class models (Langeheine & Rost, 1988; Rost, 1988) for analyzing ordered response formats The ordinal Rasch models (eg, the partial credit and rating scale models) can be integrated into the framework of the ordinal MRM by adding the modelspecific threshold parameters (Rost, 1991).

superiority and certainty fostered by an emphasis on cohesiveness as opposed to rationality would seem to be a breeding ground for bold decisions that go beyond what an individual alone would contemplate The shift toward risky decisions, however, was observed in contexts that didn t involve the intellectual and emotional incest displayed by highly cohesive groups of self-important people Even groups of strangers brought together for a one-shot encounter in a laboratory setting were found to advocate courses of action with less guarantee of success than the recommendations volunteered by the group members prior to their discussion Because this observation ew in the face of conventional wisdom, it cried out for both replication and explanation During the 1960s, neither proved to be in short supply.

Although numerous studies have now applied LCA as well as the MRM (see Rost & Langeheine, 1997; von Davier & Carstensen, in press), this line of probabilistic models has been used only infrequently in sport psychology Strauss (1994) used LCA and the MRM to identify types of sports spectators (N = 2,311 team handball spectators) in terms of their attendance motives However, the main result was the detection of numerous response sets (eg, tendency toward the middle; optimal categorization , Zhu, 2002) among spectators that could not be identified through other nonprobabilistic procedures These response sets led to the conclusion that survey data should.

number to move the VDD wire vertically The lower stretch line algorithm moves the VSS bus downward as cathode ngers are added to the lower p-cell element The vertical stretch line allows the input, VDD , and VSS metal to grow with the length of the diode element For a construction of the p-cell, there are different methods of p-cell de nition within the CadenceTM environment This methodology is referred to as the graphical technique The command structure for p-cell de nition involves Stretch, Conditional Inclusion, Repetition, Parameterized shapes, Repeat along shape, Reference point, Inherited Parameters, Parameterized Layer, Parameterized Label, Parameterized Property, Parameters, and Compile The Stretch function allows Stretch in X, Stretch in Y, Qualify, and Modify The Repetition function allows for Repeat in X, Repeat in Y, and Repeat in X and Y Stretch commands require an algorithm to de ne the design expression for stretch.

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