Analyzing Complex Survey Data by Dr. Eun Sul Lee, Dr. Ronald N. Forthofer

By Dr. Eun Sul Lee, Dr. Ronald N. Forthofer

This ebook examines how one can study complicated surveys, and specializes in the issues of weights and layout results. This new version comprises fresh perform of interpreting advanced survey facts, introduces the hot analytic strategy for specific information research (logistic regression), studies new software program and gives an advent to the model-based research that may be valuable studying well-designed, really small-scale social surveys.

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In a few cases, the choice may depend on the type of statistic to be estimated or the sample design used, as in the paired selection design. 44 The formula-based Taylor series approximation (linearization) is perhaps the most widely used method of variance estimation for complex surveys because it is found in most available software. It may be preferable to the replication-based methods (BRR and JRR) for practical reasons, but, as discussed in Chapter 4, it is not applicable for the median or other percentiles and nonparametric statistics.

51): v(" u) = (ui − u")2 /t(t − 1) (4:1) This estimator can be applied to any sample statistic obtained from independent replicates of any sample design. In applying this variance estimator, 10 replicates are recommended by Deming (1960), and a minimum of 4 by others (Sudman, 1976) for descriptive statistics. An approximate estimate of the standard error can be calculated by dividing the range in the replicate estimates by the number of replicates when the number of replicates is between 3 and 13 (Kish, 1965, p.

One way to conduct a preliminary analysis taking weights into account is to select a subsample of manageable size, with selection probability proportional to the magnitude of the weights, and to explore the subsample using the standard statistical and graphic methods. This procedure will be illustrated in the first section of Chapter 6. Choices of Method for Variance Estimation Incorporating the design features into the analysis requires choosing the method of variance estimation. As discussed in Chapter 4, three methods of variance estimation (BRR, JRR, and Taylor series approximation) are used in practice.

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