Sampling & Sampling Distribution Series # 3

                                         Sampling & Sampling Distribution 

 Definition:

 Probability sampling is defined as a sampling technique in which the researcher

 chooses samples from a larger population using a method based on the theory of probability.

 For a participant to be considered as a probability sample, he/she must be selected using a

 random selection.

4 types of probability sampling :

• There are four main types of probability sample.
• Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. ...
• Systematic sampling. ...
• Stratified sampling. ...
• Cluster sampling

Advantages of Probability Sampling:

The absence of systematic error and sampling bias.

Higher level of reliability of research findings.

Increased accuracy of sampling error estimation.

The possibility to make inferences about the population.

The characteristics of probability sampling can be summarized as follows:

Random basis of selection.
Fixed, known opportunity of selection.
Used for conclusive research.
Produces an unbiased result.
The method is objective.
Can make statistical inferences.
The hypothesis is tested.

What is non-probability sampling?
Definition: Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection.

 Types of non-probability sampling:

   There are five types of non-probability sampling technique that you may use when doing a

 dissertation at the undergraduate and master's level: quota sampling, convenience sampling, purposive sampling, self-selection sampling and snowball sampling.

 Advantage of non probability sampling

                  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).

Why is probability sampling better than Nonprobability sampling:

  Probability sampling versus non-probability sampling for hotels can be a confusing concept for anyone carrying out survey research projects. ... Probability gives all people a chance of being selected and makes results more likely to accurately reflect the entire population. That is not the case for non-probability.

Major weakness of non-probability sampling:    

 The primary disadvantage of nonprobability sampling is the lack of generalizability. Samples that are more representative of a target population are more generalizable to the target population. Thus, the claims or findings of the study are more

 

      The Methods of Probability Sampling

Simple random sampling is considered the easiest method of probability sampling. To perform simple random sampling, all a researcher must do is ensure that all members of the population are included in a master list, and that subjects are then selected randomly from this master list.While simple random sampling creates samples that are highly representative of the population, it can be time consuming and tedious when creating large samples. 

Stratified Random Sampling

Stratified random sampling is also referred to as proportional random sampling. 

In stratified random sampling, the subjects are initially grouped into different classifications such as gender, level of education, or socioeconomic status. It’s important to note that these classifications should not have any overlapping subjects. 

From here, researchers randomly select the final list of subjects from the different defined categories to ensure a well rounded sample. 

This method of probability sampling is best used when the goal of the research is to study a particular subgroup within a greater population. It also results in more precise statistical outcomes than simple random sampling.

Stratified random sampling creates layers within a sample that are extremely accurate when it comes to representing the layers with the population, but it too can be time consuming and tedious while creating larger samples

.Systematic Random Sampling

• Systematic random sampling is often compared to an arithmetic progression in which the difference between any two consecutive numbers is of the same value. 

  For example, if you are a researcher examining a clinic that has 100 patients, the first step in systematic random sampling is to pick an integer that is less than the total number of the population. This will be the first subject. 

  For the sake of this example let’s pick subject number 4.

  The next step is to choose another integer, which will be the number of individuals between subjects. 

  Let’s say we choose 6 in this example.

  By carrying out the processes above, the subjects for our study would be patients 4, 10, 16, 22, 28, etc. 

  Systematic random sampling allows researchers to create samples without using a random number generator, but the outcomes are not quite as random as they would be if a software program was used instead.

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