Practical Considerations in Raking Survey Data

More documents

Recommendations

Info

variable interaction to the data. For example, one is planning to rake on Variables A, B, C, and D. However, control totals for Variable C crossed with Variable D are available and exhibit a strong interaction (e.g., persons aged 0-17 years are more likely to be Hispanic than persons aged 65+ years). If the cell counts in the C x D margin of the sample are large enough to support fitting a C x D interaction, one would rake on three margins: A, B, and C x D. It is not necessary also to rake on separate margins for Variables C and D. If, however, the C x D raking margin involved collapsing one could consider adding one-variable margins to the raking for Variables C and D without any collapsing of their categories. 9. Forming Control Totals for Quantity Variables In a specialized raking situation one is planning on raking a sample of persons on some categorical variables (e.g., age, sex, and race), but the source of the control totals also has a quantity variable related, to say, the total number of glasses of milk consumed in a week. The survey has also measured this same quantity variable; but the survey response rate is, let us assume, only 50%. One may want to ensure that the weighted total number of glasses of milk consumed per week from the sample agrees closely with the control total. This can be accomplished by dividing the sample into groups; each group will have a mean number of glasses of milk consumed in a week and a sum of weights. In the raking process one can modify the sum of the weights in each group so that the sum of the weights times the mean, summed over all the groups, adds to the control value of total glasses of milk consumed in a week. In the simplest application one can divide the sample into two groups: below versus above the median number of glasses of milk consumed in a week based on the control total data. For each group one can use the control data to obtain the total number of glasses of milk consumed in a week. This two-category margin is then added to the raking. Convergence may not occur making it necessary to shift the group boundary point away from the 18
median in order to achieve convergence. Once convergence is achieved the weighted total number of glasses of milk consumed in a week will be in close agreement with the control total value. This procedure may be extended to modify not only the total over the entire sample, but for various subpopulations as well. 10. Raking at the State Level in a Large National Survey Some large surveys stratify by state and are designed to yield state estimates. The resulting total national sample is usually very large. The survey statisticians seek to provide national estimates as well as state estimates. Often one sets up raking control totals at the state level and carries out 51 individual rakings. Assume those rakings use Variables A, B, and C; but the number of categories of each variable is limited because of the state sample sizes. For example, one might collapse Variables A, B, and C differently by state. If Variable A were race/ethnicity, one might be able to use Hispanic as a separate race/ethnicity category in California, but not in Vermont because of the small sample size. After the 51 rakings one might compare weighted distribution of Variables A, B, and C with national control totals and observe some differences that are caused by the state-level collapsing of categories. If having precise weighted distributions at the national level is important for analytic or “face validity” reasons, one can use the IHB raking macro in the following manner. Set up a single raking that includes margins for State x A, State x B, and State x C (i.e., combine the 51 individual state rakings into a single raking). Then add detailed national margins for Variables A, B, and C. Another, similar example would involve adding Variable D as a national raking margin because its control total is available only at the national level (e.g., household income). This strategy needs to be implemented carefully. Checks should be made for raking variables that contain small sample sizes. The coefficient of variation of the weights prior to raking 19
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: categories. Taking the meaning of v
Page 21 and 22: 12. Raking Surveys that Screen for
Page 23 and 24: trimming high weight values one gen
Page 25 and 26: References Bishop YMM, Fienberg SE,
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

Practical Considerations in Raking Survey Data

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?