Non-random sampling: availability sampling

Written by Carlos Ochoa el 30 de January 2017

This post marks the second part of our series on sampling methods. This second installment will describe non-random sampling methods.

Remember: we talk about nonrandom sampling when we don’t have access to a full list of the individuals who form the population (a sampling frame) and thus don’t know the probability that a given individual will be selected for the sample.

The main consequence of this lack of information is that we can’t generalize the results with statistical precision.


Availability sampling is used quite frequently. It involves selecting a sample from the population because it is accessible. That is to say, individuals are selected for the research not because they meet some statistical criterion, but because they are readily available. This convenience usually translates to easy operation and low sampling costs. The trade-off, of course, is that it is impossible to use the results to make general assertions about the population with any sort of statistical rigor.

Suppose that we want to know Chilean college students’ thoughts on politics. To get a random sample, we would need a list of all the students in all of the universities in Chile so that we could randomly select a group of individuals and interview them. To get an availability sample, on the other hand, we might meander over to the three universities closest to where we live and survey however many individuals agree to participate when we catch them between classes.



Category: availability sampling | non-random sampling | sampling

Random sampling: cluster sampling

Written by Carlos Ochoa el 25 de January 2017

With this post dedicated to cluster sampling, we conclude our first block of posts on random sampling. With our next post, we will launch into nonrandom sampling methods, which are used most commonly in online research.


Cluster sampling is a method that makes the most of groups or clusters in the population that correctly represent the total population in relation to the characteristic that we wish to measure. In other words, all of the variability that exists in a population is contained within the population. When this is the case, we can select just a few of these clusters to conduct our study.

Let’s look at this method from another point of view. In most of the methods we’ve seen so far, the sampling units have coincided with the units to be studied (individuals). With cluster sampling, however, the sampling units are groups of units to be studied, which can be very beneficial when it comes to minimizing the cost of the sampling process. Of course, there’s a trade-off: this technique usually entails less precision, since there is a lack of heterogeneity among the clusters.

Category: random sampling | cluster sampling | sampling

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