84 PART 2 Examining Tools and Processes

Consider the scenario where parents in a community are complaining that a local

factory is emitting pollutants that they believe is resulting in a higher rate of

­leukemia being diagnosed in the community’s youth. To study whether the par-

ents are correct or not, you need to sample members of the population based on

their proximity to the factory. This is where cluster sampling comes in.

Planning to do cluster sampling geographically starts with getting an accurate

map of the area from which you are sampling. In the United States, each state is

divided up into counties, and each county is further subdivided into smaller

regions determined by the U.S. census. Other countries have similar ways their

maps can be divided along official geographic boundaries. In the scenario described

where a factory is thought to be polluting, the factory could be placed on the map

and lines drawn around the locations from which a sample should be drawn.

Different methodologies are used depending upon the specific study, but they

usually involve taking an SRS of regions and from the sampled regions known as

clusters, taking an SRS of community members for study participation.

But cluster sampling is not only done geographically. As another example, clus-

ters of schools may be selected based on school district, rather than geography,

and an SRS drawn from each school. The important takeaway from cluster sam-

pling is that it is a sampling strategy optimized for drawing a representative sam-

ple when studying an exposure known to be uneven across the population.

Sampling at your convenience

If you have read this chapter from the beginning until now, you may be feeling a

little exasperated. And that may be because all the sampling strategies we have

discussed so far  — SRS, stratified sampling, systematic sampling, and cluster

sampling — involve a lot of work for the researcher. In an SRS, you need to have

a list of the population from which to draw, and in stratified sampling, you have

to know the value of the characteristics on which you want to stratify your sample.

Each of these features makes designing your sampling frame more complicated.

Thinking this way, both systematic sampling and cluster sampling also add com-

plexity to your sampling frame. In systematic sampling, whether you use a static

list or you sample in real time, you need to keep track of the details of your sam-

pling process. In cluster sampling, you may be using a map or system of group-

ings from which to sample, and that also involves a lot of recordkeeping. You may

be asking by now, “Isn’t there an easier way?”

Yes! There is an easier and more convenient way: convenience sampling. Conve-

nience sampling is what you probably think it is — taking a sample from a popu-

lation based on convenience. For example, when statistics professors want to