The design of complex samples induces correlations between element values. In stratification negative correlation reduces the variance; but that gain is less for subclass means, and even less for their differences and for complex statistics. Clustering induces larger and positive correlations between element values. The resulting increase in variance is measured by the ratio deff, and is often severe. This is reduced but persists for subclass means, their differences, and for analytical statistics. Three methods for computing variances are compared in a large empirical study. The results are encouraging and useful.
Using data from a 1996/1997 survey of undocumented Latino immigrants in four sites, we examine reasons for coming to the United States, use of health care services, and participation in government programs. We find that undocumented Latinos come to this country primarily for jobs. Their ambulatory health care use is low compared with that of all Latinos and all persons nationally, and their rates of hospitalization are comparable except for hospitalization for childbirth. Almost half of married undocumented Latinos have a child who is a U.S. citizen. Excluding undocumented immigrants from receiving government-funded health care services is unlikely to reduce the level of immigration and likely to affect the well-being of children who are U.S. citizens living in immigrant households.
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