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watermarking, and digital signatures using public key infrastructure (PKI), are deployed to combine semantic learning and security for semantic authentication. For multimedia applications in which the content may undergo various transformations that maintain the semantic meaning of the information, but modify the bit representation, it is imperative to tie authentication to the semantics of the data; otherwise, routine processing of the content may interfere with DRM protection. 11.3.3 Summary

Figure 4-11

Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage. Aiken, L. S., West, S. G., Sechrest, L., & Reno, R. R. (1990). Graduate training in statistics, methodology, and measurement in psychology: A review of Ph.D. programs in North America. American Psychologist, 45, 721 734. Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17 21. Asimov, D. (1985). The grand tour: A tool for viewing multidimensional data. SIAM Journal of Statistical Computing, 6, 138 143. Bauer, M. I., & Johnson-Laird, P. N. (1993). How diagrams can improve reasoning. Psychological Science, 4(6), pp. 372 378. Becker, R. A., Chambers, J. M., & Wilks, A. (1988). The new S language: A programming environment for data analysis and graphics. New York: CRC Press. Becker, R. A., Cleveland, W. S., & Shyu, M. J. (1996). The visual design and control of Trellis Display. Journal of Computational and Statistical Graphics, 5, 123 155. Behrens, J. T. (1997a). Principles and procedures of exploratory data analysis. Psychological Methods, 2, 131 160. Behrens, J. T. (1997b). Does the white racial identity attitude scale measure racial identity Journal of Counseling Psychology, 44, 3 12. Behrens, J. T. (2000). Exploratory data analysis. In A. E. Kazdin (Ed.), Encyclopedia of psychology (Vol. 3, pp. 303 305). New York: Oxford University Press. Behrens, J. T., Bauer, M., & Mislevy, R. (2001, April). Future prospects for assessment in the on-line world. Paper presented at the Annual Meeting of the American Educational Research Association, Seattle, WA. Behrens, J. T., & Dugan, J. G. (1996). A graphical tour of the White Racial Identity Attitude Scale data in hypertext and VRML. Retrieved from http://research.ed.asu.edu/reports/wrias. graphical.tour/graphtour.html. Behrens, J. T., Dugan, J. T., & Franz, S. (1997, August). Improving the reporting of research results using the World Wide Web. Paper presented at the 106th Annual Convention of the American Psychological Association, Chicago, IL. Behrens, J. T., & Rowe, W. (1997). Measuring White racial identity: A reply to Helms (1997). Journal of Counseling Psychology, 44, 17 19. Behrens, J. T., & Smith, M. L. (1996). Data and data analysis. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 949 989). New York: Macmillan.

bedevil us: Do the results represent reality Or are they just a gment of our imagination Will we be able to defend our procedure to the reviewers If by some miracle our article is published, will we nd out later that the results were not valid Not everyone in psychology faces the problem of missing data, but we do. We knew that every time we embarked on an analysis, we could look forward to the sequence of frustrating setbacks, and this knowledge always made us sad. Four separate pieces were published in 1987 that would forever change the way researchers looked at data analysis with missing data. Two papers were published describing a procedure for analyzing missing data using standard structural equation modeling (SEM) software (Allison, 1987; Muth n, Kaplan, & Hollis, 1987). Although somewhat unwieldy, and extremely error prone for most real-life applications, this procedure provided researchers with the rst truly accessible and statistically sound tool for dealing with missing data. Of course, this procedure assumed that one knew how to use the SEM programs to begin with. Little and Rubin s (1987) highly in uential book on analysis with missing data also appeared in 1987. In this book, Little and Rubin, following Dempster, Laird, and Rubin (1977), laid the groundwork for development of the expectation maximization (EM) algorithm for numerous missing data applications. In addition, Rubin (1987) published the rst book on multiple imputation in 1987. Although practical applications of multiple imputation would not appear for another 10 years, this was the beginning of what would be the most general approach to handling missing data. Since 1987, numerous software products have become available that address the issue of missing data. Many of the best of these are free. For the ones that are not free, the cost is more than offset by their usefulness. Although we need to continue to move forward in this area, we have made tremendous progress in making missing data analysis accessible to researchers all over the world. In fact, we stand at the beginning of an era in which useful and accessible missing data procedures are an integral part of mainstream statistical packages.