Editing Sampling (statistics) (section)

===Probability-proportional-to-size sampling===

{{main|Probability-proportional-to-size sampling}}

In some cases the sample designer has access to an "auxiliary variable" or "size measure", believed to be correlated to the variable of interest, for each element in the population. These data can be used to improve accuracy in sample design. One option is to use the auxiliary variable as a basis for stratification, as discussed above.

Another option is probability proportional to size ('PPS') sampling, in which the selection probability for each element is set to be proportional to its size measure, up to a maximum of 1. In a simple PPS design, these selection probabilities can then be used as the basis for [[Poisson sampling]]. However, this has the drawback of variable sample size, and different portions of the population may still be over- or under-represented due to chance variation in selections.

Systematic sampling theory can be used to create a probability proportionate to size sample. This is done by treating each count within the size variable as a single sampling unit. Samples are then identified by selecting at even intervals among these counts within the size variable. This method is sometimes called PPS-sequential or monetary unit sampling in the case of audits or forensic sampling.

<blockquote>
''Example: Suppose we have six schools with populations of 150, 180, 200, 220, 260, and&nbsp;490 students respectively (total 1500 students), and we want to use student population as the basis for a PPS sample of size three. To do this, we could allocate the first school numbers 1&nbsp;to&nbsp;150, the second school 151 to 330&nbsp;(=&nbsp;150&nbsp;+&nbsp;180), the third school 331 to 530, and so on to the last school (1011 to&nbsp;1500). We then generate a random start between 1 and 500 (equal to&nbsp;1500/3) and count through the school populations by multiples of 500. If our random start was 137, we would select the schools which have been allocated numbers 137, 637, and&nbsp;1137, i.e. the first, fourth, and sixth schools.''
</blockquote>

The PPS approach can improve accuracy for a given sample size by concentrating sample on large elements that have the greatest impact on population estimates. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available&nbsp;– for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable. In some cases, an older measurement of the variable of interest can be used as an auxiliary variable when attempting to produce more current estimates.<ref name="MySwedeLohr">
* {{cite book|author=Lohr, Sharon L.|title=Sampling: Design and Analysis}}
* {{cite book|author=Särndal, Carl-Erik |author2=Swensson, Bengt |author3=Wretman, Jan|title=Model Assisted Survey Sampling}}</ref>