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Importing Fireworks PNG files You can import Fireworks PNG files into Flash as flattened images or as editable objects. When you import a PNG file as a flattened image, the entire file (including any vector artwork) is rasterized, or converted to a bitmap image. When you import a PNG file as editable objects, vector artwork in the file is preserved in vector format. You can choose to preserve placed bitmaps, text, and guides in the PNG file when you import it as editable objects. If you import the PNG file as a flattened image, you can launch Fireworks from within Flash and edit the original PNG file (with vector data). See Editing bitmaps in an external editor on page 98. When you import multiple PNG files in a batch, you choose import settings one time. Flash MX uses the same settings for all files in the batch.

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Highly parametric methods, such as structural equation modeling (SEM), assume linear relations between manifest and latent variables If IRT is taken seriously, analyzing raw scores with methods such as SEM must lead to a misspeci cation of the manifest-latent relationships This contradiction has led some observers to suggest that the results of highly parametric methods can be spoiled when raw scores are analyzed (Embretson, 1996) A dilemma arises when IRT is applied inconsistently with highly parametric methods Because IRT proposes that manifest (raw) scores be monotonically transformed to estimate latent scores, the results of parametric analyses using latent scores are not comparable to analyses with manifest scores Ordinal methods have an advantage in this context Because of the monotonic relation, the ordinal information of the manifest variable is identical to the information of the latent construct.

Ordinal results will be identical regardless of whether manifest or latent scores are the basis of the analysis This means that the misspeci cation problem with the analysis of raw scores is avoided, and results of ordinal analyses can be compared regardless of whether manifest scores or latent scores are used Robustness Under optimal conditions, parametric statistics generally have larger absolute values than their ordinal counterparts, and parametric inferential methods perform better than ordinal inferential methods, usually in terms of higher power (but see a contrary example in the following discussion) Higher power under optimal conditions is probably the justi cation for many applied researchers using parametric methods rather than ordinal methods (Zimmerman & Zumbo, 1993) However, the differences in power can be very small, and when conditions are not optimal, ordinal methods can have many superior properties, including higher power.

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Because there are reasons to believe that optimal conditions rarely hold in applied research (Micceri, 1989; Sawilowsky & Blair, 1992; Wilcox, 1990), ordinal methods may be a better choice than are parametric methods for general use The ordinal methods presented in this chapter tend to be less in uenced than are parametric methods by changes in the characteristics of the data such as nonnormality, outliers, and monotonic nonlinearity In this sense, the ordinal methods are said to be more robust than their parametric counterparts Distributional variations in the data can considerably distort values of parametric statistics, test values, and con dence intervals For example, monotonic transformation away from bivariate normality can drastically reduce the value of Pearson s r and its t test (Long & Cliff, 2001) In contrast, the values of ordinal statistics and their inferential methods are.

comparison, as we shall see later In addition, psychologists will generally be more attracted to evaluation projects that focus on underlying mechanisms because it allows for theory testing as well as for valuable applied work (Yeh, 2000) Moreover, a view of causation that does not emphasize underlying generative mechanisms simply seems de cient as a representation of causal forces in the world: Cause and effect relations happen for reasons If an orientation program reduces drinking in college freshman, there is some psychological process that mediates the effect, such as changes in perceived norms (Schroeder & Prentice, 1998) In short, the counterfactualist view of causal effects needs to be supplemented, and this supplementation not only enriches our view of causation but also helps guide evaluation design in such a way as to enhance evaluation practice.

Figure 33.1 Item characteristic curve (ICC) of one item ( represents the person parameter).