Question

Distinguish among a quota sample, a cluster sample, and a stratified sample. Give examples of each.

Describe snowball sampling. Give an example of a situation in which you might use this type of sample. What are the dangers associated with this type of sample?

Answer 1

Stratified sampling is a type of probability sampling, in which first of all the population is bifurcated into various mutually exclusive, homogeneous subgroups (strata), after that, a subject is selected randomly from each group (stratum), which are then combined to form a single sample. A stratum is nothing but a homogeneous subset of the population, and when all the stratum are taken together, it is known as strata.

The common factors in which the population is separated are age, gender, income, race, religion, etc. An important point to remember is that strata should be collectively exhaustive so that no individual is left out and also non-overlapping because overlapping stratum may result in the increase in the selection chances of some population elements. The sub-types of stratified sampling are:

• Proportionate Stratified Sampling
• Disproportionate Stratified Sampling.

Example: if one wanted to stratify a sample of individuals in a town by age, one could easily get figures of the age distribution, but if there is no general population list showing the age distribution, prior stratification would not be possible. What might have to be done in this case at the analysis stage is to correct proportional representation? Weighting can easily destroy the assumptions one is able to make when interpreting data gathered from a random sample and so stratification prior to selection is advisable. Random stratified sampling is more precise and more convenient than simple random sampling

Cluster sampling is defined as a sampling technique in which the population is divided into already existing groupings (clusters), and then a sample of the cluster is selected randomly from the population. The term cluster refers to a natural, but heterogeneous, intact grouping of the members of the population.

The most common variables used in the clustering population are the geographical area, buildings, school, etc. Heterogeneity of the cluster is an important feature of an ideal cluster sample design. The types of cluster sampling are given below:

• Single-stage cluster sampling
• Two-stage cluster sampling
• Multistage cluster sampling.

Quota sampling:

This is a method of stratified sampling in which the selection within strata is non-random. Selection is normally left to the discretion of the interviewer and it is this characteristic which destroys any pretensions towards randomness.

example: consider the situation where an interviewer has to survey people about a cosmetic brand. His population is people in a certain city between 35 and 45 years old. The interviewer might decide they want two survey subgroups — one male, and the other female — each with 100 people. After choosing these subgroups, the interviewer has the liberty to rely on his convenience or judgment factors to find people for each subset.

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Snowball Sampling:

Snowball sampling is where research participants recruit other participants for a test or study. It is used where potential participants are hard to find. It’s called snowball sampling because (in theory) once you have the ball rolling, it picks up more “snow” along the way and becomes larger and larger. Snowball sampling is a non-probability sampling method. It doesn’t have the probability involved, with say, simple random sampling (where the odds are the same for any particular participant being chosen). Rather, the researchers used their own judgment to choose participants.

Snowball sampling consists of two steps:

1. Identify potential subjects in the population. Often, only one or two subjects can be found initially.
2. Ask those subjects to recruit other people.

advantages and Disadvantages of Snowball Sampling

Advantages:

• It allows for studies to take place where otherwise it might be impossible to conduct because of a lack of participants.
• Snowball sampling may help you discover characteristics about a population that you weren’t aware existed. For example, the casual illegal downloader vs. the for-profit downloader.

Disadvantages:

• It is usually impossible to determine the sampling error or make inferences about populations based on the obtained sample.