Introduction
Probability sampling involves creating a sample from a population that relays on randomization principle. This selection process relays on the precept of randomization, that is, random selection or hazard. It uses random sampling techniques. It bases on the sampling frame. The probability of the sample is non-zero. The sample size must be 30 members and over. But, non-probability sampling involves in non-random method. In this method, everyone has no chance to get selected. It depends on the judgment of the researcher and the convenience of sampling. The probability of this sampling technique is unknown. This technique does not represent the population accurately.
Basic types:
There are two types of probability sampling that is classical and non-classical.
Classical sampling
Classical sampling is the most common since it uses for many purposes. Includes survey data collection and target population selection. It has a lot of advantages, such as being easy to understand and convenient for large populations. It can work for very small samples.
Non-classical sampling
Non-classical sampling requires lots of research effort and time. Sometimes results are even less reliable.
In this article, we will discuss some classical sampling methods. We will compare them with Non-Classical sampling methods.
Main sampling methods
So let’s digress and get into it.
1) Random sampling
It is a type of probability sampling where an individual sample picks from a larger population. In random sampling, each member of the target population has chance to select. It should represent probability distribution. It may give out different possible values. By observing your respondents at a particular time, you can calculate the number of participants in the entire population. An important property of random sampling is that it is not biased in selecting. Selecting the right individuals for the questionnaire may give more information about people’s thoughts.
2) Cluster Sampling
This sampling method involves splitting up the entire population into smaller groups. These small groups name as clusters. The main feature of cluster sampling is that each group member gives value. Yet, their attributes of interest pool together to give greater knowledge. But to do this, each person has an equal chance of getting a particular set of observations. A large amount of literature has conducted on clustering research.
3) Simple Random Sample (SRS)
It uses one representative of the whole population. For example, if you had 100 children in the class, you can select ten children for it.
4) Stratified Sampling
This stratified technique is applicable for class attendance. Each class will comprise only 50%. A researcher should decide on random sample size. He can stratify the classes into the same strata. He/she would be able to find out whether we have the same or different categories, and so forth. For example, if the target population consists of 500 boys and 250 girls, then 25 students must select from each class every year and so on will be taken for observation and finally to make our conclusion as in the case where the target population contains 1000 boys and 250 girls (a lot of data to consider), and so on.
Other method of probability sampling
One Sample Technique
It is when you take a bunch of people in one class of study. For instance, if you had 5000 students in class 10, then only 2000 of them will ask questions to fulfill your questionnaire.
Two Sample Techniques
This is the same as one sample technique, but for two samples. Instead of taking a huge number of people, one big representative sample is selected. After that all members of that sample will go for interviews separately.
Systematic technique
It is dividing your target population into several subsets in the study groups. Then the researcher would know which groups they belong to.
Ensemble Techniques
This is when many techniques like k-means, classification and regression combine to provide a better understanding of the target population.
Multivariate Approach
It is the way of looking at many variables and how the data may explain using by these variables. We can see how each variable can help us know about characteristics or whether it relates to our target.
Generalization Approach
It deals with how well you are doing if you observe a few samples of subjects under your project. It tries to predict the future behavior of the subjects. As you can say for any hypothesis testing, this approach is good because it helps explain your results to someone else.
Multiple Regressions
It involves modeling and predicting new samples. It is same like predictive model that uses for the existing sample.
Cross-Sectional Approach
These are situations where one or more samples should be assessed to give a sense of stability and predictability of the outcome.
Bias-Variance Tradeoff
It is known as cross-validation. It is a method to estimate and visualize the quality of predictive models.
Parametric
It is a statistical tool. It takes care of bias and variance and allows you to use them in analysis and visualization.
Bayesian Network
It is a way of describing the relationships between your dependent and independent variables, including randomness, dependencies and uncertainty.
Confidence Intervals
It is a numerical value of a statistical test, a measure of a parameter, and a parameter itself. There are various kinds of confidence intervals.
Advantages and disadvantages
Probability sampling has various advantages. A cluster sample is the most convenient method. Random sampling represents the whole population. Stratified random sampling makes strata that represent the population. Systematic sampling also represents the population. Probability sampling may be time-consuming. Sometimes, it does not work well due to a lack of homogeneity. All these methods can use for different purposes.
Conclusion
It is an assertion of what is true no matter the source of a claim or test, and thus no matter the method used. The above discussions shows that a combination of several scientific paradigms use for conducting scientific research. Probability sampling is a quantitative research method. It is the best technique to represent the whole population. Different types of probability sampling method may use different purpose in research work.
Author Bio:
Robert Fawl is a professional Content writer & Content Marketer, based in London. Robert is an Author and Blogger with experience in providing services including but not limited to Essay Writing, Dissertation Writing, Coursework Writing Services, Thesis Writing Services and Assignment Writing etc.
