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analytic assumptions (e.g., Reynolds & Temple, 1995; Wortman, Reichardt, & St. Pierre, 1981). Related analysis issues will be addressed later. In addition to the possible selection-by-maturation problem, the pretest-posttest nonequivalent-groups design is susceptible to other threats that also are called interactions with selection. Selection by history refers to the possibility that the two groups are subjected to different historical forces, so that history may cause a larger effect in one than in the other. For instance, if we were evaluating the effect of a freshman orientation program that is implemented at one college and used a different college as a nonequivalent control group, selection by history might operate if there was a death by alcohol poisoning that was highly publicized locally at one school at about the same time as the freshman orientation was introduced. These and other interactions with selection (Cook & Campbell, 1979; Shadish et al., 2002) may be plausible in the pretest-posttest nonequivalent design. Some Conclusions About Quasi-experiments In summarizing and expanding the preceding selective review of quasi-experimental designs, several conclusions can be highlighted. First is that modi cations in research design can render speci c validity threats implausible. For example, the addition of time series to a simple pretest-posttest design can allow an evaluator to assess the plausibility of maturation and to control for (linear) maturation. Second, evaluators should be careful to assess the plausibility of validity threats in context. As Eckert (2000) illustrated in the context of World Bank training programs, just because a validity threat can apply to a speci c design in general does not mean that it is operating in a particular evaluation using that design (or operating powerfully enough to obscure the true treatment effect). Third, by considering the possible effect of plausible validity threats, one can try to develop more elaborate designs that will better approximate the ideal counterfactual. In the case of the possible selection-by-history problem in the freshman orientation evaluation, for example, a treatment effect hypothesis would predict large treatment effects among freshman but no effects for upperclassmen who did not experience the program. In contrast, a history effect based on a publicized alcohol-related death would presumably lead to the prediction of comparable effects across all grade levels. Careful consideration of validity threats can lead to the development of quasi-experimental designs especially suited to how that threat might operate in that particular evaluation. Fourth, attention should be given not simply to the potential presence of a validity threat, such as selection or history, but to the likely magnitude of its effect. Past writings often seem to suggest a
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For the ESD RF design, there are two fundamental metrics (in current or power formulations) that exist:  Johnson Limit formulation (e.g., current frequency and power frequency relations).  Wunsch Bell formulation (e.g., current-to-failure pulse width and power-to-failure pulse width relations). The two independent relationships one addressing the power speed functional inverse relationship and the other is the power-to-failure pulse width inverse relationship form a fundamental set of relations for understanding of both the power performance tradeoffs and power-to-failure versus pulse width tradeoffs. As a result, these serve as key metrics for the understanding of RF ESD design.
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prerequisite features achieve their effects. This is problematic because political and socioeconomic circumstances (e.g., real or perceived competitive, zero-sum outcomes) often preclude introducing these features (e.g., cooperative interdependence, equal status) into many contact settings. Despite substantial documentation that intergroup cooperative interaction reduces bias (Allport, 1954; Aronson, Blaney, Stephan, Sikes, & Snapp, 1978; Cook, 1985; Deutsch, 1973; Johnson et al., 1983; Sherif et al., 1961; Slavin, 1985; Worchel, 1979), it is not clear how cooperation achieves this effect. One basic issue involves the psychological processes that mediate this change. The classic functional relations perspective by Sherif et al. (1961) views cooperative interdependence as a direct mediator of attitudinal and behavioral changes. However, recent approaches have extended research on the contact hypothesis by attempting to understand the potential common processes and mechanisms that these diverse factors engage to reduce bias. Several additional explanations have been proposed (see Brewer & Miller, 1984; Miller & Davidson-Podgorny, 1987; Worchel, 1979, 1986). For example, cooperation may induce greater intergroup acceptance as a result of dissonance reduction serving to justify this type of interaction with the other group (Miller & Brewer, 1986). It is also possible that cooperation can have positive, reinforcing outcomes. When intergroup contact is favorable and has successful consequences, psychological processes that restore cognitive balance or reduce dissonance produce more favorable attitudes toward members of the other group and toward the group as a whole to be consistent with the positive nature of the interaction. In addition, the rewarding properties of achieving success may become associated with members of other groups (Lott & Lott, 1965), thereby increasing attraction (S. Gaertner et al., 1999). Also, cooperative experiences can reduce intergroup anxiety (Stephan & Stephan, 1984). Intergroup contact can also in uence how interactants conceive of the groups and how the members are socially categorized. Cooperative learning and jigsaw classroom interventions (Aronson & Patnoe, 1997), which are designed to increase interdependence between members of different groups and to enhance appreciation for the resources they bring to the task, may reduce bias in part by altering how interactants conceive of the group boundaries and memberships. In the next section we consider how the effects of intergroup contact can be mediated by changes in personal and collective identity. Contact, Categorization, and Identity From the social categorization perspective, the issue to be addressed is how intergroup contact can be structured to alter
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cases. It may be more appropriate to base the parallel analyses on matrices that also match the observed data in skew and kurtosis as well. All roots from the factors of the study that are larger than the same numbered averaged random root are considered valid roots. For example, for the psychological problem with six variables and N 147, the closest tabled values give the rst parallel roots as 1.2, 1.1, and 1.0. The rst observed root of Table 6.8 is larger than 1.2 and the second is larger than 1.1, but the third is less than 1.0. Therefore, parallel analysis indicates that two factors should be extracted because there are only two roots that exceed their randomly based equivalent. For the box problem, it gives one factor instead of three. The number of WAIS factors is also underestimated, giving two instead of four. It has serious problems with small but replicable factors. Simulation studies have found parallel analysis to be a prime candidate for the best procedure for estimating the number of exploratory factors. Scree Test The scree test has a somewhat different logic for use of the roots. It is assumed that the variables cover a domain of interest and have at least moderately strong correlations. That means the factors of interest should be noticeably stronger than the factors of little interest, including random correlations. So when the roots are plotted in order of size, the factors of interest will appear rst and be obviously larger than the trivial and error roots. The number of factors is that point at which the line formed by plotting the roots from largest to smallest stops dropping and levels out. The name is from an analogy. Scree refers to the rubble at the bottom of a cliff. The cliff itself is identi ed because it drops sharply. The last part of the cliff that can be seen is where it disappears into the scree, which has a much more gradual slope. Note that the cliff is still seen at the top of the rubble; in the same way the number of factors includes the last factor associated with the drop. Following the suggested use of the scree test gives three factors for the psychological variables and four for the boxes. That is one more than are assumed to exist in these two data sets. For the WAIS, the scree gives three factors, a number that does not lead to replicable factors (Gorsuch, 2000). The suggestion to de ne the number of factors as the rst factor among the trivial roots is what gives three factors for the psychological variables instead of two. This has been controversial in what some would see as extracting one too many factors. That leads to the question of whether extracting too many or too few factors would be more harmful. The
Appendix E Cascading Style Sheets Reference
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