By Dr. Eun Sul Lee, Dr. Ronald N. Forthofer
This ebook examines how one can research advanced surveys, and specializes in the issues of weights and layout results. This re-creation contains fresh perform of studying complicated survey information, introduces the recent analytic strategy for express info research (logistic regression), studies new software program and offers an creation to the model-based research that may be priceless reading well-designed, particularly small-scale social surveys.
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Extra resources for Analyzing Complex Survey Data (Quantitative Applications in the Social Sciences)
For each replicate created (ui ), the parameter estimate is calculated. Then the bootstrap estimate of the variance of the mean of all replicate estimates is given by vðu"Þ = 1 B (u − u")2 : B i=1 i (4:10) This estimator needs to be corrected for bias by multiplying it by (n − 1)/n: When n is small, the bias can be substantial. In our example, there are two PSUs in each stratum, and the estimated variance needs to be halved. An alternative approach to correct the bias is to resample (nh − 1) PSUs in stratum h and multiply the sample weights of the observations in the resampled PSUs by nh /(nh − 1) (Efron, 1982, pp.
Conditions under which the sampling scheme is ignorable for inference have been explored extensively (Nordberg, 1989; Sugden & Smith, 1984). Many survey statisticians have debated these two points of view since the early 1970s (Brewer, 1999; Brewer & Mellor, 1973; Graubard & Korn, 1996, 2002; Hansen, Madow, & Tepping, 1983; Korn & Graubard, 1995a; Pfeffermann, 1996; Royall, 1970; Sarndal, 1978; T. M. F. Smith, 1976, 1983). A good analysis of survey data would require general understanding of both points of view as well as consideration of some practical issues, especially in social surveys.
For this analysis we need to determine if there are a number of PSUs without observations in a particular education-by-gender category. If there are many PSUs with no observation for some education-by-gender category, this calls into question the estimation of the variance–covariance matrix, which is based on the variation in the PSU totals within the strata. The education (3 levels) by gender (2 levels) tabulation by PSU showed that 42 of the 84 PSUs had at least one gender-by-education cell with no observations.