# Probability Sampling

Sampling techniques can be divided into two categories: probability and non-probability. In probability sampling, each population member has a known, non-zero chance of participating in the study. Randomization or chance is the core of probability sampling technique. Probability sampling methods use some form of random selection. Therefore, application of this method offers the highest chance of creating a sample that is truly representative of the population.

In non-probability sampling, on the other hand, sample group members are selected non-randomly; therefore, in non-probability sampling only certain members of the population has a chance to participate in the study.

The table below illustrates the main differences between probability and non-probability sampling methods.

 Probability sampling Non-probability sampling Randomly selected samples Subjective judgement of researchers are used to select samples Equal chance for each member of population to get selected Not everyone has equal chance to get selected Used to reduce a sampling bias Researcher is not overly concerned with sampling bias Effective to collect data from diverse population Useful in specific environment with sampling group members sharing similar characteristics Useful in obtaining accurate representation of population Does not help in representing the population accurately Finding correct audience is difficult Finding correct audience is simple

Probability sampling comprises the following sampling techniques:

### Application of Probability Sampling: an Example

Let’s suppose, your dissertation topic is ‘A study into employee motivation of ABC Company and the ways of increasing it’. You chose survey primary data collection method to achieve research objectives.

Sampling process comprises the following four stages:

1. Identifying an appropriate sampling frame based on your research question(s) and objectives. ABC Company has 400 employees and accordingly, your sampling frame would be 400.

2. Determining a suitable sample size. You may decide that the sample size of 60 employees should be sufficient for the purposes of this research.

3. Choosing the most appropriate sampling technique and selecting the samples. In this case, simple random sampling, the most basic form of probability sampling technique can be applied through using a table of randomly generated numbers.  Websites such asGenerate Data, Graph Pad,Mockaroo and many others can be used to do this task easily and quickly.

Now, all you have to do is to choose a starting point in the table (a row and column number) and look at the random numbers that appear there. In this case, since the data run into three digits, the random numbers would need to contain three digits as well. You need to ignore all the random numbers after 400, since your target population has only 400 members. Also, choose a specific number only once and if a number recurs, simply skip it and move to the next number. In this way, the first 60 different numbers between 001 and 400 that represent 60 employees of ABC Company constitute your sample group.

4. Checking if the sample is representative of the population.

• 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. Effective to collect choose samples from broad population base
• Cost-effectiveness
• Simple and straightforward in application

• Higher complexity compared to non-probability sampling
• More time-consuming
• Usually more expensive than non-probability sampling

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of sampling methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words.

John Dudovskiy []