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Before we go into each section and the underlying factors in detail, I will explain what each of the column headings mean. If you refer to Exhibit 12.4, you will see that the model s summary report has seven columns. Column 1 (#) highlights the number of each speci c factor. Column 2 (Section/Factors) lists the names for each of the six broad sections and each of the 30 factors. Column 3 (% Breakout Variable) represents each factor s percentage contribution to the score in its respective section. Column 4 (Maximum Points Variable) lists the maximum number of points allotted to each section and individual factor. Column 5 (Manager Score 0 to 5) lists the grade we give to each individual factor, from 0 (low) to 5 (high). Column 6 (Scaled Score). In order to keep the model s maximum total score to 100, we need to scale the individual factors and sections because the sum of all the individual factors when they are unscaled actually exceeds 100 (30 factors with a maximum score of 5 each would result in a possible maximum score of 150. This is done by adding the scores for all the factors in a given section and dividing that gure by the total possible score and then multiplying the result by the maximum points available for the section. For example, the scaled score for the rst section (Investment Professionals) was calculated as follows: Scaled Score = where

When used in conjunction with Fireworks, images can now be optimized from within Dreamweaver. Moreover, it s easier to integrate code generated from Fireworks and you can even specify Dreamweaver-style HTML. You can add a series of interrelated buttons complete with four-state rollovers by using the Navigation Bar object. In the next chapter, you learn how to use hyperlinks in Dreamweaver.

Hampson, S E, Dubanoski, J P, Hamada, W, Marsella, A J, Matsukawa, J, Suarez, E and Goldberg, L R (2001) Where are they now Locating former elementary school students after nearly 40 years for a longitudinal study of personality and health Journal of Research in Personality, 35, 375 387 Hermann, A and Grigorian, K (2005) Looking for Mr Right: effective locating techniques for panel surveys Paper presented at the Annual meeting of the American Association for Public Opinion Research, May, Miami Beach, FL Hill, M S (1991) The Panel Study of Income Dynamics; A User s Guide Thousand Oaks, CA: Sage Koo, M M and Rohan, T E (2000) Use of world wide web based directories for tracing subjects in epidemiological studies American Journal of Epidemiology, 152, 889 894 Landale, N S and Guest, A M (1985).

Constraints, satisfaction and residential mobility: Speare s model reconsidered Demography, 22, 199 222 Laurie, H, Smith, R and Scott, L (1999) Strategies for reducing nonresponse in a longitudinal panel survey Journal of Official Statistics, 15, 269 282 Lepkowski, J M and Couper, M P (2002) Nonresponse in longitudinal household surveys In R M Groves, D Dillman, J Eltinge and R J A Little (Eds), Survey Nonresponse (pp 259 272) New York: John Wiley & Sons, Inc Long, L (1988) Migration and Residential Mobility in the United States New York: Russell Sage Foundation McKenzie, M, Tulsky, J P, Long, H L, Chesney, M and Moss, A (1999) Tracking and follow up of marginalized populations: a review Journal of Health Care for the Poor and Underserved, 10, 409 429 Menendez, E, White, M C and Tulsky, J P (2001).

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Figure 8.5 Infrared spectrum of testosterone (cf. SAQ 8.1). Reprinted from Spectrochim. Acta, 43A, Fletton, R. A., Harris, R. K., Kenwright, A. M., Lancaster, R. W., Pacher, K. J. and Sheppard, N., A comparative Spectroscopic investigation of three pseudopolymorphs of testosterone using solid-state IR and high resolution solid-state NMR , 1111 1120, Copyright (1987), with permission from Elsevier.

Locating study subjects: predictors and successful search strategies with inmates released from a US county jail Controlled Clinical Trials, 22, 238 147 Nagelkerke, N J D (1991) A note on a general definition of the coefficient of determination Biometrika, 78, 691 692 Ribisl, K M, Walton, M A, Mowbray, C T, Luke, D A, Davidson II, W S and Bootsmiller, B J (1996) Minimizing participant attrition in panel studies through the use of effective retention and tracking strategies: review and recommendations Evaluation and Program Planning, 19, 1 25 Rossi, P H (1955) Why Families Move Beverly Hills: Sage Schejbal, J A and Lavrakas, P J (1995) Panel attrition in a dual frame local area telephone survey In Proceedings of the Survey Research Methods Section (pp 1035 1039) Alexandria, VA: American Statistical Association Scott, C K (2004).