Practical Considerations in Raking Survey Data

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the IQR, because the raking may increase the weight values of the cases that have been truncated, and thus cause the raking steps to repeat endlessly. The approach described above does not guarantee convergence (i.e., after running the raking several times there could still be weights above the median weight plus six times the IQR plus one), and one could consider adding a larger constant to increase the chances of convergence, but the authors have found in their applications that convergence is often achieved by adding a constant of one. Table 4 shows an example of the use of weight trimming with raking. Before raking there are four cases with “input” weights that exceed the median weight plus six times the IQR plus one of 439.847 (condition). The weights of those cases are truncated to 438.847 (cutoff) and the raking is run for the first time. After the first raking the condition equals 444.490. Only one case has a weight that exceeds this value and that weight is truncated to the cutoff of 443.490. After the second raking no cases have a weight that exceeds the condition and the process is stopped. The weights from the second raking add to the population size and meet the raking control totals. 15. Summary The authors have sought to give some background on how raking works and to discuss the convergence process. They have also sought to give some warnings of conditions that need to be checked before and after raking. Brick et al. (2003) discuss other examples of issues that one should be aware of when using raking. The IHB SAS macro discussed in this paper is available for free from the first author. 24
References Bishop YMM, Fienberg SE, and Holland PW. (1975). Discrete Multivariate Analysis: Theory and Practice. Cambridge, MA: MIT Press. Brick JM, Montaquila J, and Roth S. (2003). Identifying Problems with Raking Estimators. 2003 Proceedings of the Annual Meeting of the American Statistical Association [CD-ROM], Alexandria, VA: American Statistical Association, pp. 710-717. Deming WE. (1943). Statistical Adjustment of Data. New York: Wiley. Frankel MR, Srinath KP, Hoaglin DC, Battaglia MP, Smith PJ, Wright RA, and Khare M. (2003). Adjustments for non-telephone bias in random-digit-dialling surveys. Statistics in Medicine, Volume 22, pp. 1611-1626. Izrael D, Hoaglin, DC, and Battaglia MP. (2000). A SAS Macro for Balancing a Weighted Sample. Proceedings of the Twenty-Fifth Annual SAS Users Group International Conference, Cary, NC: SAS Institute Inc., pp. 1350-1355. Izrael D, Hoaglin DC, and Battaglia MP. (2004). To Rake or Not To Rake Is Not the Question Anymore with the Enhanced Raking Macro. May 2004 SUGI Conference, Montreal, Canada. Kalton G. (1983). Compensating for Missing Survey Data. Survey Research Center, Institute for Social Research, University of Michigan. 25
Page 1 and 2: Practical Considerations in Raking
Page 3 and 4: 1. Introduction A survey sample may
Page 5 and 6: 2. Basic Algorithm The procedure kn
Page 7 and 8: The adjustment factors, (0) Tj+ / m
Page 9 and 10: The authors’ experience indicates
Page 11 and 12: Table 2 illustrates the use of the
Page 13 and 14: Control totals usually do not come
Page 15 and 16: 7. Examining and Diagnosing Slow Co
Page 17 and 18: categories. Taking the meaning of v
Page 19 and 20: median in order to achieve converge
Page 21 and 22: 12. Raking Surveys that Screen for
Page 23: trimming high weight values one gen
Page 27 and 28: Table 1. A 2 x 2 Table for Which Ra
Page 29 and 30: Raking AREA - 1 VARIABLE2, iteratio
Page 31 and 32: Figure 1. Convergence of a Raking P
Page 33 and 34: Figure 3. Convergence of Variables
Page 35 and 36: Figure 5. Prediction of the Number
Page 37: Table 4: Example of Weight Trimming

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