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

ISBN-10: 0761930388

ISBN-13: 9780761930389

This ebook examines how you can examine complicated surveys, and specializes in the issues of weights and layout results. This re-creation contains contemporary perform of interpreting complicated survey info, introduces the recent analytic method for specific facts research (logistic regression), reports new software program and gives an creation to the model-based research that may be helpful interpreting well-designed, fairly small-scale social surveys.

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**Additional info for Analyzing Complex Survey Data **

**Example text**

3! 4! 37 This is a specific application of the following general formula expanded at a: f (x) = f (a) + f (a)(x − a) + f (a)(x − a)2 f (a)(x − a)3 + +... 2! 3! In statistics, the Taylor series is used to obtain an approximation to some nonlinear function, and then the variance of the function is based on the Taylor series approximation to the function. Often, the approximation provides a reasonable estimate to the function, and sometimes the approximation is even a linear function. This idea of variance estimation has several names in the literature, including the linearization method, the delta method (Kalton, 1983, p.

These evaluation studies tend to show that none of the three methods consistently performs better or worse, and that the choice may depend in most cases on the availability of and familiarity with the software. 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.

The merits and demerits of using sample weights will be further discussed in the last section of Chapter 6. 4. STRATEGIES FOR VARIANCE ESTIMATION The estimation of the variance of a survey statistic is complicated not only by the complexity of the sample design, as seen in the previous chapters, but also by the form of the statistic. Even with an SRS design, the variance estimation of some statistics requires nonstandard estimating techniques. For example, the variance of the median is conspicuously absent in the standard texts, and the sampling error of a ratio estimator (refer again to Note 1) is complicated because both the numerator and denominator are random variables.

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

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