they wonder if the variance is unduly inflated as a result. A standard design-based method is still appropriate here, since the fact that the sum of the weights varies appreciably is a reflection of the randomness in the sample for estimating domains. For such quantities as domain means or regression coefficients, the effect of this variability may be less pronounced, compared with the estimation of domain totals. However, compared with a model-based analysis, the variances may appear to be large. This demonstrates more the deficiency in the model-based approach, which does not account for the randomness in the domain sample size, rather than reflecting badly on the design-based approach. This should be explained in the guide, so that researchers encountering this problem will understand the reason.

Another question of interest to researchers is how to integrate data from more than one survey. These questions are more complex, especially if there is a need to use a coordinated jackknife, so some guidelines to users would be very valuable. In this area ERS and NASS may not currently have the necessary expertise. In general, there are two choices for analyzing the data when it is thought that the parameters of interest are similar across surveys: (1) the separate approach, in which the estimates are produced for each survey separately and then combined using a composite weight, and (2) the combined approach, in which the files are combined and analyzed as one file with a possibly adjusted weight variable. Under the separate approach, it is possible to use meta-analytic methods to combine evidence from different surveys (e.g., Zieschang, 1990). If the combined approach is pursued, it is always recommended that the assumptions about equality of the parameters of interest across surveys be subjected to a statistical hypothesis test.

Finally, the guide should also include a list of relevant references for researchers dealing with survey data. Some possible references are Lohr (1999), Pfeffermann (1993), and Korn and Graubard (1999). A gentle introduction to the basic issues of design-based and model-based inference for survey data is provided by Carrington et al. (2000), which is available online at

Recommendation 7.6: ERS and NASS should collaborate on writing a Guide for Researchers for performing multivariable analyses using data from complex surveys, particularly data from ARMS. In areas in which expertise is not available for writing parts of such a guide, expertise should be sought from the statistics and economics community, especially those with experience in the analysis of survey data from complex survey designs.

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