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Transformation is a sensible practice when variables are assessed on scales that are more or less arbitrary anyway, as are many scales in psychology However, interpretation is of the transformed variable and may be bothersome for scores measured on well-known scales or scales with carefully developed psychometric properties If you decide to transform, check that the distribution is improved by transformation If a variable is only moderately positively skewed, for instance, a square root transformation may make the variable moderately negatively skewed so nothing is gained Often, you need to try rst one transformation and then another until you nd the transformation that reduces skewness and kurtosis values to near zero, has the fewest outliers, or produces homogeneity of variance and linearity The type of transformation necessary to improve the t to assumptions also conveys substantive information For ex-.

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Total suspended particulate matter (up to 35 m in diameter) Particulate matter smaller than 10 m in diameter Particulate matter smaller than 2.5 m in diameter Particles smaller than about 1 3 m in diameter; we shall operationally de ne as PM2.5 Particles larger than 1 3 m in diameter; we shall operationally de ne as PM10 minus PM2.5 but it is actually the PM10 contribution to the coarse PM mode Particles smaller than about 100 nm Particles in the range 5 100 nm Particles in the range 100 nm 2 m

[RFC1075] [RFC1583] [RFC1583]

ORDINAL ALTERNATIVES TO MEAN COMPARISON: DOMINANCE ANALYSIS Dominance Analysis for Independent Data The Delta Measure and the d Statistic One of the most important and frequently encountered problems in psychological and behavioral research is the socalled two-sample problem, or location comparison. For example, we are often interested in whether scores from one group tend to be higher than those from the other (e.g., treatment effects). This research question is usually answered by the two-sample t test comparing means of the two groups or the parallel one-way ANOVA. The t test requires (a) interval level of measurement for the variables, and (b) the assumptions of normality and homogeneity of variance. However, as argued earlier in the chapter, many behavioral and social variables have only ordinal justi cation. Although some studies show that Type I error rates and empirical power of the t test are preserved for Likert-scaled data (Hsu & Feldt, 1969; Nanna & Sawilowsky, 1998), ordinal methods can be more powerful than the t test for data of less than interval level. In addition, the parametric assumptions are always violated to a greater or lesser extent in applied research. Nonnormality and heterogeneity of variance can in ate the actual Type I error rate and severely reduce the power of normalbased mean comparison procedures (e.g., Barnard, 1984; Cressie & Whitford, 1986; Gronow, 1953; Pearson & Please, 1975; Ramsey, 1980; Tan, 1982; Wilcox, 1990, 1991, 1992). The t test and the corresponding CI for a mean difference are highly quanti ed procedures. However, as previously discussed, the question of the researcher is often formulated in a

The Impact of Evolution 118 Animal Psychology and the Coming of Behaviorism 119 Behaviorism: The Golden Age of Learning Theory 120 THE MODERN SCIENTIFIC PERIOD 125 The Three Key Ideas of Computing 125 The Fruits of Computation: Cognitive Science 127 Cognitive Psychology Today 131 REFERENCES 131

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Linear Gradient creates a gradient that shades from the starting point to the end point in a Radial Gradient creates a gradient that shades from the starting point to the end point in a

Assembling a partially wired network is easier than it may seem. Just follow these steps:

Hiding in an attachment Hiding in a transmission Hiding in network headers Hiding in an overt protocol

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