Advanced Nursing Inquiry and Evidence Based Practice

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Probability (Random) Sampling Strategies Probability sampling techniques are the most valid choice to produce results that are representative of the target population. There are four commonly-used probability sampling strategies.

In a Simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators, drawing numbers from a hat, or other techniques that are based entirely on chance.

Ex., You want to select a simple random sample of 100 MSN graduates at MRU in 2021. You assign a number to every MSN graduate in the MRU database from 1 to N, and use a random number generator to select 100 numbers.

Systematic sampling is similar to simple random sampling. Every kth member on the list in the sampling frame is selected, where k is calculated based on the number of subjects in the sampling frame and the desired sample size.   Ex., # in sampling frame = 1000; sample size = 100;  k = 1000/100 = 10; therefore, every 10th person on list is

invited to participate in the study. If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the MRU database groups students by age, and MSN graduates are listed in chronological order, there is a risk that your interval might skip over younger graduates, resulting in a sample that is skewed towards older graduates.

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you to draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the list in the sampling frame into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role). Based on the overall proportions in the sampling frame, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above.

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

Non-Probability (Non-Random) Sampling Strategies In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included. There are four commonly used non-probability sampling strategies.

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Convenience sampling simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results.

Snowball sampling is where research subjects recruit other subjects for a test or study. It is used when potential subjects are hard to find. It is 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 other words, subjects are asked to recruit other people who are also then asked to recruit other people, etc., until the needed sample size is obtained. 

Purposive sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very

small and specific. An effective purposive sample must have clear criteria and rationale for inclusion.

In Quota Sampling researchers create a a very tailored sample that is in proportion to some characteristic or trait of a population. For example, you could divide a population by the state they live in, income or education level, or sex. The population is divided into groups (also called strata) and samples are taken from each group to meet a quota. Care is taken to maintain the correct proportions representative of the population. For example, if your population consists of 45% female and 55% male, the sample should reflect those percentages. 

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