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(pp. 335 353). Washington, DC: American Psychological Association. Heckman, J. J. (1979). Sample selection bias as a speci cation error. Econometrica, 47, 153 161. Hyatt, S. L., & Collins, L. M. (2000). Using Latent Transition Analysis to examine the relationship between parental permissiveness and the onset of substance use. In J. Rose, L. Chassin, C. Presson, & S. Sherman (Eds.), Multivariate applications in substance use research (pp. 259 288). Hillsdale, NJ: Erlbaum. J reskog, K. G., & S rbom, D. (1996). LISREL 8 user s reference guide. Moorseville, IN: Scienti c Software. Little, R. J. A. (1994). A class of pattern-mixture models for normal incomplete data. Biometrika, 81, 471 483. Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112 1121. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley. Little, R. J. A., & Schenker, N. (1995). Missing Data. In: G. Arminger, C. C. Clogg, & M. E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 39 75). New York: Plenum Press. McArdle, J. J. (1994). Structural factor analysis experiments with incomplete data. Multivariate Behavioral Research, 29(4), 409 454. McArdle, J. J., & Hamagami, F. (1991). Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. In L. M. Collins & J. C. Horn (Eds.), Best methods for the analysis of change (pp. 276 304). Washington, DC: American Psychological Association. Muth n, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables. In L. M. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 291 322). Washington, DC: American Psychological Association. Muth n, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431 462.
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Most Web-based software is optimized for a specific screen resolution. If the user s screen resolution is lower than the software s optimal resolution, scrolling is required to see the full application screen. If the user s screen resolution is higher than the software s optimal resolution, some areas of the user screen may be blank. It is therefore important to ascertain the optimal screen resolution for the dashboard software and to compare this resolution level against the organization-wide standard. If the software s optimal resolution is lower than that of the organization-at-large, it may be okay, but the opposite case is risky. If the software s optimization resolution is higher than the organization standard, then many users may miss important information displays because they may overlook the scrolling required to see the full span of the dashboard. As of the writing of this book, an optimal screen resolution of 1024 768 would suffice for a significant percentage of the user base.
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FIGURE 12-14: The completed Wi-Fi Roomba
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orthogonal factors of a prior study in a new sample, only to nd the factors correlating .6 to .8. Highly correlated data will not be denied. It is best to be forewarned about this situation by leaving the rotation unrestricted. If the factors are correlated, then those correlations can be factored (just as the original variable correlations were factored). The factors from the variables themselves are called the primary factors, whereas those extracted from the primary factors are called secondary factors; third-order factors would be factors from the second-order factors, and so forth. All factors after the primary factors are referred to as higher-order factors. Conceptually, the primary factors are more speci c than are the secondary factors and so should predict more speci c variables better than do the secondary factors. With more general variables, the secondary factors should predict better. Using the results of a higher-order factor analysis and the desired dependent variables, it is possible to show (Gorsuch, 1984) and even test (Mershon & Gorsuch, 1988) when the primary or second-order factors are more useful. An example of higher order factoring is the WAIS-III. The primary factors are in Table 6.4. The four primary factors were correlated, and a general second-order factor was extracted. This factor, the last column of Table 6.4, represents the classical g, or general ability factor (IQ). The correlations of the individual scales with g were computed by extension analysis (discussed later in this chapter). It is g that has a long history of relating to many areas of achievement.
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In general, an information source generates any of a set of M different symbols, which are considered as representatives of a discrete random variable X that adopts any value in the range A = {x1 , x2 , . . . , x M }. Each symbol xi has the probability Pi of being emitted and contains information Ii . The symbol probabilities must be in agreement with the fact that at least one of them will be emitted, so
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Load Distribution at a per User Level Granularity. Figures 5.10 and 5.11 show the total number of accesses and total amount of data requested by different clients, respectively. Users with invalid identi ers were discarded. As we can see, there is a signi cant variation in the load placed by different users on the Web
Psychologists do not engage in sexual relationships with students or supervisees who are in their department, agency, or training center or over whom psychologists have or are likely to have evaluative authority. (See also Standard 3.05, Multiple Relationships.)
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