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Non-random sampling: snowball sampling

Written by Carlos Ochoa el 06 de February 2017

With this post, we'll conclude our series on sampling techniques. Today we’re going to talk about the snowball sampling method.

Snowball sampling is a nonrandom sampling method in which the individuals selected to be studied recruit new participants from among their circle of acquaintances. The word “snowball” comes from just that idea: in the same way that a snowball becomes bigger and bigger as it rolls down a hill, this method enables the sample size to grow as the individuals selected to participate invite people they know to join. 

 

 

Snowball sampling is often used to access low-incidence populations and individuals who are difficult for researchers to connect with. For studies that hope to study a very specific group, such as stamp collectors, obtaining a sample through collectors’ networks of friends and acquaintances can prove much more effective than selecting individuals in a strictly random fashion, since, in the latter case, the overwhelming majority of candidates will be dismissed. In theory, it is highly likely that a stamp collector will know other stamp collectors, which makes this an effective method of sampling a group that a researcher would otherwise have difficulty accessing.

Category: snowball sampling | sampling

Non-random sampling: quota sampling

Written by Carlos Ochoa el 01 de February 2017

At last, our series of posts on sampling, has reached the all-star of non-random sampling: quota sampling. This method is most often used in online research conducted through panels. We could look at quota sampling as the nonrandom version of stratified sampling. It consists of three phases:

 

1. Segmentation

To start with, we divide the population we hope to study into mutually exclusive groups, in such a way that every individual belongs to one and only one group (no one is left out), just as when we define the strata for stratified sampling. This segmentation is normally achieved by using a sociodemographic variable such as sex, age, region or social class.

2. Setting the size of the quotas

Next, we set the target number of individuals to be surveyed for each of these groups. We normally define these targets in proportion to the group’s size within the population. For example, if we defined several segments by gender in a population that is 60% women and 40% men, and we want a 1,000-person sample, we would define a target of 600 women and 400 men. These targets are known as quotas. In this example, we would have a gender quota of 600 women and 400 men. Sometimes we set quotas that are not proportional to the population; such quotas might be used to conduct in-depth analysis of a specific group.

Category: quota sampling | non-random sampling | sampling

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