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First, schools were ordered within each jurisdiction using a serpentine sort (by TUDA/urbanicity status, race/ethnicity status, and achievement score or ZIP Code area median income). Then a systematic sample was drawn with probability proportional to the measures of size.
In addition, new and newly eligible schools were assigned measures of size and sampled from the new-school frame. The new schools were ordered by district (i.e., the serpentine sort was not used for the small numbers of these schools) and a systematic sample drawn.
The goal of deeply stratifying the school sample in each jurisdiction is to reflect the population distribution as closely as possible, thus minimizing the sampling error. The success of this approach can be seen by comparing the proportion of races/ethnicities enrolled in schools (CCD values for each school), median income, and type of location (viewed as an interval variable) reported in the original frame against the school sample. In addition, the distribution of state assessment achievement scores for the original frame can be compared with that of the school sample for those jurisdictions for which state assessment achievement data are available.