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affects personality. Moreover, behaviorist theories were once the models of what theory could be in psychology. But certain features militate against behaviorism s signi cance for the eld of personality. Those features spring from the traditional behaviorist mission. Traditional Behaviorism and Personality One feature is behaviorism s search for general laws. That is ingrained in the approach, as we can see from its strategy of discovering learning-behavior principles with rats, pigeons, dogs, and cats for the major behaviorists in the rst and second generation were animal psychologists who assumed that those learning-behavior principles would constitute a complete theory for dealing with any and all types of human behavior. John Watson, in behaviorism s rst generation, showed this, as B. F. Skinner did later. Clark Hull (1943) was quite succinct in stating unequivocally about his theory that all behavior, individual and social, moral and immoral, normal and psychopathic, is generated from the same primary laws (p. v). Even Edward Tolman s goal, which he later
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Figure 1.9c A higher-order or multiple star network. Note the direct route between 2B1 and 2B2 . There is another direct route between 3A5 and 3A6 .
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load or by the dissipation of the motivation required for its activity (being a relatively effortful, controlled process), this in turn can lead to rebound effects. Additional ironic implications of stereotype suppression were uncovered in subsequent research. For example, trying not to think stereotypical thoughts about an elderly target resulted in better memory for the most stereotypical characteristics displayed by the target (Macrae, Bodenhausen, Milne, & Wheeler, 1996). Moreover, these effects are not limited to situations in which an overt, external requirement for thought suppression is imposed; even when suppression motivation was self-generated in a relatively spontaneous manner, ironic effects were observed to result (Macrae, Bodenhausen, & Milne, 1998). Other research suggests that rebound effects of this sort are more likely to emerge in high-prejudice persons (Monteith, Spicer, & Toomen, 1998) and in situations in which the perceiver is unlikely to have chronically high levels of suppression motivation (Wyer, Sherman, & Stroessner, 2000). These quali cations are quite consistent with general idea that even the process of mental control itself is subject to some degree of automation. With practice, the initial effortfulness of stereotype suppression may be replaced by relative ef ciency. Another form of controlled processing that has received considerable attention from social cognition researchers is judgmental correction. When perceivers suspect that their judgments have been contaminated by unwanted or inappropriate biases, they may take steps to adjust their judgments in a manner that will remove the unwanted in uence (e.g., Wilson & Brekke, 1994). Whereas the initial processes that produced the bias are likely to be automatic ones, the processes involved in correcting for them are usually considered to be effortful. Hence, they require perceiver motivation and processing capacity for their deployment. One particularly noteworthy domain in which such hypotheses have been investigated is research on person perception. In particular, it has long been established that people are susceptible to a correspondence bias, in which they tend to perceive the behavior of others to be a re ection of corresponding internal dispositions even when there are clear and unambiguous situational constraints on the behavior (e.g., Jones & Harris, 1967; Gilbert & Malone, 1995). The previously described research on spontaneous trait inference is consistent with the idea that people often immediately assume that behavior re ects the actor s dispositions. In an in uential theoretical assessment of this bias, Gilbert (e.g., 1998) proposed that dispositional inferences involve three distinct stages. In the categorization stage, the observed behavior is construed in terms of its trait implications (e.g., Hannah shared her dessert with her brother could be categorized as kind). Then
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Institutional Bonds, Acceptance of Risky Behavior, and Resistance to Peer Pressure) as well as two D.A.R.E. curriculum knowledge questions ( Changing the subject is a good way to say no and Taking a deep breath is a good way to relax ) were employed in this model. The primary hypothesis of interest in this model is whether participation in D.A.R.E. predicts differences in these concepts and curriculum questions. Note that although we conceptualize these components as constructs, due to small sample sizes in some of the classrooms, in this model D.A.R.E. core concepts were treated as measured variables. D.A.R.E. is given to classrooms of children (groups); therefore, D.A.R.E. participation is considered a level 2 (classroom level) predictor. Additionally, other classroom predictors such as (a) size of class and (b) percentage of minority students in the class could also be hypothesized to predict D.A.R.E. effectiveness. These all are hypotheses involving prediction of average student-level outcomes from class-level predictors. It is also hypothesized that variables measured at the child level also predict changes in these components. At the child level, the four core concepts and the two D.A.R.E. curriculum questions were predicted from (a) ethnicity of child (White, non-White), (b) gender of child, and (c) expected grades for child. Model Estimation and Evaluation We use data from 4,578 children in 144 classrooms. Further details about these data and the D.A.R.E. program evaluations can be found in Ullman, Stein, and Dukes (2000). First, a model was estimated with only the child-level data, the four D.A.R.E. core concepts, and two curriculum variables predicted from gender, ethnicity, and grades. The data for children were nonnormally distributed (Mardia s standardized coef cient for multivariate kurtosis = 92.76, p < .001); therefore, maximum likelihood estimation and the SatorraBentler (S-B) Scaled chi-square were used to evaluate the model (Bentler & Yuan, 1999; Satorra & Bentler, 1994). There was evidence that the model t the data: 2 ( N = 4578, 11) = 2.90, p = .99. Although the model t the data well, none of the regression paths signi cantly predicted the D.A.R.E. core concepts/curriculum after adjustment to the standard errors for nonnormality. This pattern of nonsigni cance is typical of research on the D.A.R.E. program (Dukes, Ullman, & Stein, 1995). Fundamental to a D.A.R.E. evaluation is whether D.A.R.E. is effective in increasing Self-Esteem, Institutional Bonds, and Resistance to Peer Pressure and in reducing Acceptance of Risky Behaviors. D.A.R.E. is implemented at the classroom level; therefore, participation is a level 2
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