Most common individual-level analyses have lower design factors than those listed in Table 2 because researchers tend to choose population subgroups in such a way as to effectively minimize the impact of clustering. For example, fertility studies most frequently focus on married women ages 15 to 49. Since the great majority of sample clusters contain only one such individual, the impact of clustering is trivial. Thus, fertility studies almost inevitably have design factors at or below 1.0, which yield conservative estimates of statistical significance. Likewise, investigations of such topics as the living arrangements of elderly women, the occupational status of young men, or the education of never-married adult women should generally yield precision at least as high as would be obtained from a simple random sample of the same size.
Researchers can usually set up their analyses to avoid high design factors.
But if we restrict the analysis by age and sex and look at the school attendance of girls ages 10 to 14, the design factor improves to 1.08 because most clusters include only one girl in this age group. Finally, if we randomly select one school-age child from each household, the design factor falls below 1.0. In general, if studies are designed so that they rarely or never examine more than one individual per household, the effects of clustering can be altogether avoided. Especially when doing analyses of children, boarders, and other groups likely to appear multiple times in the same household, researchers should develop strategies to eliminate the redundant cases. Instead of assessing the characteristics of all children, for example, one can look at eldest children, or youngest children, or children of a particular age, or a randomly selected child from each household.