What are the distinguishing features of simple random sampling? A sampling frame must be compiled in which each element has a unique identification number. Each element in the population has a known and equal probability of selection.

Systematic random sampling

When random sampling is used, each element in the population has an equal chance of being selected (simple random sampling) or a known probability of being selected (stratified random sampling).

1. STEP ONE: Define the population.
2. STEP TWO: Choose your sample size.
3. STEP THREE: List the population.
4. STEP FOUR: Assign numbers to the units.
5. STEP FIVE: Find random numbers.
6. STEP SIX: Select your sample.

Simple random sample advantages include ease of use and accuracy of representation. No easier method exists to extract a research sample from a larger population than simple random sampling.

Simply put, a random sample is a subset of individuals randomly selected by researchers to represent an entire group as a whole. The goal is to get a sample of people that is representative of the larger population.

Simple Random Sample: An Overview These disadvantages include the time needed to gather the full list of a specific population, the capital necessary to retrieve and contact that list, and the bias that could occur when the sample set is not large enough to adequately represent the full population.

Advantages of non-probability sampling Getting responses using non-probability sampling is faster and more cost-effective than probability sampling because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.

Advantages and disadvantages A major advantage with non-probability sampling is that—compared to probability sampling—it’s very cost- and time-effective. It’s also easy to use and can also be used when it’s impossible to conduct probability sampling (e.g. when you have a very small population to work with).

In the most basic form of probability sampling (i.e., a simple random sample), every member of the population has an equal chance of being selected into the study. Non-probability sampling, on the other hand, does not involve “random” processes for selecting participants.

A quota is a type of trade restriction where a government imposes a limit on the number or the value of a product that another country can import. For example, a government may place a quota limiting a neighboring nation to importing no more than 10 tons of grain. Each ton of grain after the 10th incurs a 10% tax.

How to get quota sampling right

1. Divide the sample population into subgroups. These should be mutually exclusive.
2. Figure out the weightages of subgroups. The weightage is how much of your sample a given subgroup will be.
3. Select an appropriate sample size.
4. Survey while adhering to the subgroup population proportions.

Disadvantages of Quota Sampling Quota sampling does not allow random selection of participants of the research. Quota sampling increases the risk of researcher bias as a researcher might include people in research who he finds to easy to approach or have co-operative nature.

This type of sampling is actually employed by both qualitative and quantitative researchers, but because it is a nonprobability method, we’ll discuss it in this section. When conducting quota sampling, a researcher identifies categories that are important to the study and for which there is likely to be some variation.

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.

In sociology and statistics research, snowball sampling (or chain sampling, chain-referral sampling, referral sampling) is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group is said to grow like a rolling snowball.

Purposive sample– when a researcher seeks out participants with specific characteristics. Quota sample– when a researcher selects cases from within several different subgroups. Snowball sample– when a researcher relies on participant referrals to recruit new participants.

In sociology, “snowball sampling” refers to a non-probability sampling technique (which includes purposive sampling) in which a researcher begins with a small population of known individuals and expands the sample by asking those initial participants to identify others that should participate in the study.

Purposive and snowball sampling. Purposive sampling: A non random selection of participants on purpose. The variables to which the sample is drawn up are linked to the research question. Snowball sampling: A type of purpose sampling where existing participants recruit future subjects from among their acquaintances.