What is Probability Sampling?
Probability Sampling is a method of sampling (sampling is, studying or analyzing from smaller groups instead of a large population) that utilizes forms of random selection methods.
The most important requirement of probability sampling is that everyone in your population has a known and an equal chance of getting selected. For example, if you have a population of 100 people every person would have odds of 1 in 100 for getting selected. Probability sampling gives you the best chance to create a sample that is truly representative of the population.
Probability sampling uses statistical theory to select randomly, a small group of people (sample) from an existing large population and then predict that all their responses together will match the overall population.
For example, the population of the US alone is 330 million, it is practically impossible to send a survey to every individual to gather information but you can use probability sampling to get data which is as good even if it is collected from a smaller population.
Types of Probability Sampling
Simple random sampling as the name suggests is a completely random method of selecting the sample. This sampling method is as easy as assigning numbers to the individuals (sample) and then randomly choosing from those numbers through an automated process. Finally, the numbers that are chosen are the members that are included in the sample.
There are two ways in which the samples are chosen in this method of sampling: Lottery system and using number generating software/ random number table. This sampling technique usually works around large population and has its fair share of advantages and disadvantages.
Stratified Random sampling involves a method where a larger population can be divided into smaller groups, that usually don’t overlap but represent the entire population together. While sampling these groups can be organized and then draw a sample from each group separately.
A common method is to arrange or classify by sex, age, ethnicity and similar ways. Splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.
Members in each of these groups should be distinct so that every member of all groups get equal opportunity to be selected using simple probability. This sampling method is also called “random quota sampling” Cluster random sampling is a way to randomly select participants when they are geographically spread out. For example, if you wanted to choose 100 participants from the entire population of the U.S., it is likely impossible to get a complete list of everyone. Instead, the researcher randomly selects areas (i.e. cities or counties) and randomly selects from within those boundaries.
Cluster sampling usually analyzes a particular population in which the sample consists of more than a few elements, for example, city, family, university etc. The clusters are then selected by dividing the greater population into various smaller sections.
Systematic Sampling is when you choose every “nth” individual to be a part of the sample. For example, you can choose every 5th person to be in the sample. Systematic sampling is an extended implementation of the same old probability technique in which each member of the group is selected at regular periods to form a sample. There’s an equal opportunity for every member of a population to be selected using this sampling technique.
What are the steps involved in Probability Sampling?
1. Choose your population of interest carefully: Carefully think and choose from the population, people you think whose opinions should be collected and then include them in the sample.
2. Determine a suitable sample frame: Your frame should include a sample from your population of interest and no one from outside in order to collect accurate data.
3. Select your sample and start your survey: It can sometimes be challenging to find the right sample and determine a suitable sample frame. Even if all factors are in your favor, there still might be unforeseen issues like cost factor, quality of respondents and quickness to respond. Getting a sample to respond to true probability survey might be difficult but not impossible.
But, in most cases, drawing a probability sample will save you time, money, and a lot of frustration. You probably can’t send surveys to everyone but you can always give everyone a chance to participate, this is what probability sample is all about.
When to use Probability Sampling
1. When the sampling bias has to be reduced: This sampling method is used when the bias has to be minimum. The selection of the sample largely determines the quality of the research’s inference. How researchers select their sample largely determines the quality of a researcher’s findings. Probability sampling leads to higher quality findings because it provides an unbiased representation of the population.
2. When the population is usually diverse: When your population size is large and diverse this sampling method is usually used extensively as probability sampling helps researchers create samples that fully represent the population. Say we want to find out how many people prefer medical tourism over getting treated in their own country, this sampling method will help pick samples from various socio-economic strata, background etc to represent the bigger population.
3. To create an accurate sample: Probability sampling help researchers create an accurate sample of their population. Researchers can use proven statistical methods to draw accurate sample size to obtained well-defined data.
Advantages of Probability Sampling
1. It’s Cost-effective: This process is both cost and time effective and a larger sample can also be chosen based on numbers assigned to the samples and then choosing random numbers from the bigger sample. Work here is done.
2. It’s simple and easy: Probability sampling is an easy way of sampling as it does not involve a complicated process. Its quick and saves time. The time saved can thus be used to analyze the data and draw conclusions.
3.It non-technical: This method of sampling doesn’t require any technical knowledge because of the simplicity with which this can be done. This method doesn’t require complex knowledge and its not at all lengthy.