Why is sampling so important to the success of sociological research? Why is it so important to get the size of a sample as close as possible to what is "correct"? Compare and contrast Probility and Non-probability samples. Discuss the primary strengths and weaknesses of the following samples:

a. Simple Random Sample

b. Systematic Sample

c. Stratified Random Sample

d. Cluster Sample

e. Quota Sample

f. Convenient Sample

g. Purposive Sample (Judgmental sample)

h. Snowball Sample

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In sociology, sampling is defined as the process of choosing a subset of the population that is being studied, in order to gain statistical data on which the research will be based on. It is a way to collect information about a certain demographic without having to analyze and study the entire population. Sampling is a widely used method among sociologists, mainly because it is a rather simple, convenient and cheap research technique.

The population that is being studied is called a target audience, while the population within the sample is known as the sampling frame. The researchers must make sure that the characteristics of the population from which the sampling frame is taken must be representative of the target audience; in other words, the people in the sample must follow the same rules and norms and must have the same beliefs and opinions as the society in general.

There are two main types of sampling techniques—those that are based on probability or probability sampling techniques, and those that are purposive or non-probability sampling techniques.

A probability sampling method defines the selection of people that is done on a random basis. This way, a specified sampling frame is usually not chosen and all individuals have an equal opportunity to be a part of a sample(s). One advantage of the probability sampling method is the fact that it often removes all social biases. However, it is a bit slower and more expensive than purposeful techniques. There are four types of probability sampling:

  1. Simple Random Sample—This is a random sample in which each individual has an equal chance of being chosen from a sampling frame. It is the equivalent of ‘picking a name out of a hat’, as each individual is given a number and a computer chooses the number which will be included in the sample. This method is usually used when researching a similar demographic, as it might create a based sample.
  2. Systematic Sample—This is a sample in which all the individuals are chosen on a fixed interval. For instance, every 15th person is chosen to be a part of the sample. Systematic sampling has the same problems as simple random sampling.
  3. Stratified Random Sample—Here the individuals are separated into smaller groups or strata based on criteria such as age, gender, ethnicity and so on. Then, the final sample is formed at random.
  4. Cluster Sample—Cluster sampling is occasionally used when the population is naturally divided in clusters. For instance, the researchers might find their sample by researching a cluster of random employees from a random organization of a random business. Cluster sampling is easier and cheaper than the previous three sampling types, but it is also less accurate.

A non-probability sampling method defines the selection of samples that is not done randomly and the individuals are not given an equal opportunity to be a part of the sample(s). Even though non-probability sampling is often considered unreliable, as it results with various social biases and doesn’t fully represent the target audience, it can still prove to be the best way to find a sample for some researchers. There are four types of non-probability sampling:

  1. Quota Sample—This is a sample that fits a certain quota. For instance, out of 80 students, the researches must find 25 students with a 4.4 GPA.
  2. Convenient Sample—This is a sample that relies more on the proximity between the researcher and the individuals, instead on the way the target audience is represented. This method is very risky, as the researchers don’t have control over the sample. For instance, a sample could be found by stopping people on the street or in a crowded public place.
  3. Purposive Sample (Judgmental sample)— Here, the sample is based on the purpose of the study. For instance, to determine whether or not mothers would want to have a third child, researchers will find a sample from a fixed group of women between the ages of 25-35.
  4. Snowball Sample—This method is usually used in researches where finding a sample is not that easy. With this method, the participants are asked to find other participants which might help with the research. For instance, researches done on drug use or criminal activity.
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Sampling is important in social science research because it helps you to generalize to the population of interest and ensure high external validity. Since it is often impossible and not practical to enroll the entire population in your study researchers select a sample. Choosing a 'correct' sample means making sure that your sample is large enough and representative of the population.  

One way to make sure that your sample is able to be confidently generalized to the population is to use a sampling model using either non-probability or probability sampling. Generally there are no right or wrong approaches to sampling but it is important to understand the strengths and limitations of each approach. 

Probability sampling is the best way to ensure your sample is similar to your population since you are randomly selecting participants and all participants have an equal chance of being selected. Probability sampling includes Simple Random sample; Systematic sample; Stratified Sample; and Cluster Random sample. Probability sampling has some limitations because it can be time consuming, costly, and often not practical to be able to recruit participants this way. 

Simple Random Sample – get a list of all eligible in your population; generate random numbers, and select subjects based on numbers generated (usually will use a random number calculator or chart).

Systematic Random Sample – same as simple random sample but here you will pick your own number. (Ex: Pick every 4th person who enters the cafeteria.)

Stratified Random Sample – the population is separated (stratified or strata) into groups that are randomly assigned (ex: undergrads and grad student).

Cluster Random sample - the population is separated into clusters and then participants are randomly assigned (ex: geographic regions).

Non-probability sampling is non-random sampling. Non-probability sampling's strength is the fact that it is usually easier and faster to collect your sample using these methods as compared to probability sampling. One of the main limitations in non-probability sampling is the fact that its not randomized and therefore your sample may not be truly representing your population.

Quota sample - sampling based on pre-determined quotas such as 60% women and 40% men.

Convenient sample - enroll people that are easy to find or 'convenient.'

Purposive sample (judgmental sample) - sampling of pre-determined groups such as white males aged 30-45.

Snowball sample - those already enrolled refer others to enroll in your research.

For additional information or to dig deeper into the topic of sampling I have included a link to the Social Research Methods book sampling page.

I hope this helps bring clarity to the ideas of social science research sampling methods and some of the strengths and limitations to each approach.

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