Here it is blind chance alone that determines whether one item or the other is selected. The results obtained from probability or random sampling can be assured in terms of probability i. Random sampling ensures the law of Statistical Regularity which states that if on an average the sample chosen is a random one, the sample will have the same composition and characteristics as the universe.
This is the reason why random sampling is considered as the best technique of selecting a representative sample.
In such a design, personal element has a great chance of entering into the selection of the sample. The investigator may select a sample which shall yield results favourable to his point of view and if that happens, the entire inquiry may get vitiated.
Thus, there is always the danger of bias entering into this type of sampling technique. But in the investigators are impartial, work without bias and have the necessary experience so as to take sound judgement, the results obtained from an analysis of deliberately selected sample may be tolerably reliable. However, in such a sampling, there is no assurance that every element has some specifiable chance of being included. Sampling error in this type of sampling cannot be estimated and the element of bias, great or small, is always there.
As such this sampling design in rarely adopted in large inquires of importance. However, in small inquiries and researches by individuals, this design may be adopted because of the relative advantage of time and money inherent in this method of sampling.
Quota sampling is also an example of non-probability sampling. Under quota sampling the interviewers are simply given quotas to be filled from the different strata, with some restrictions on how they are to be filled. This type of sampling is very convenient and is relatively inexpensive. But the samples so selected certainly do not possess the characteristic of random samples.
Quota samples are essentially judgement samples and inferences drawn on their basis are not amenable to statistical treatment in a formal way. A sample design is made up of two elements. Random sampling from a finite population refers to that method of sample selection which gives each possible sample combination an equal probability of being picked up and each item in the entire population to have an equal chance of being included in the sample.
This applies to sampling without replacement i. In such a situation the same element could appear twice in the same sample before the second element is chosen. In brief, the implications of random sampling or simple random sampling are:. To make it more clear we take a certain finite population consisting of six elements say a , b , c , d , e , f i. Measurement And Scaling Techniques.
Analysis Of Variance And Co-variance. Interpretation And Report Writing. Its Role In Research. Research Methodology Interview Questions. Research Methodology Practice Tests. Jobs in Meghalaya Jobs in Shillong. Making a great Resume: How to design your resume? Have you ever lie on your resume? All you need to do as a researcher is ensure that all the individuals of the population are on the list and after that randomly select the needed number of subjects. This process provides very reasonable judgment as you exclude the units coming consecutively.
Simple random sampling avoids the issue of consecutive data to occur simultaneously. Then the researcher randomly selects the final items proportionally from the different strata. It means the stratified sampling method is very appropriate when the population is heterogeneous. Stratified sampling is a valuable type of sampling methods because it captures key population characteristics in the sample.
In addition, stratified sampling design leads to increased statistical efficiency. Thus, with the same size of the sample, greater accuracy can be obtained. This method is appropriate if we have a complete list of sampling subjects arranged in some systematic order such as geographical and alphabetical order. The process of systematic sampling design generally includes first selecting a starting point in the population and then performing subsequent observations by using a constant interval between samples taken.
This interval, known as the sampling interval, is calculated by dividing the entire population size by the desired sample size. For example, if you as a researcher want to create a systematic sample of workers at a corporation with a population of , you would choose every 10th individual from the list of all workers.
This is one of the popular types of sampling methods that randomly select members from a list which is too large. A typical example is when a researcher wants to choose individuals from the entire population of the U.
It is impossible to get the complete list of every individual. So, the researcher randomly selects areas such as cities and randomly selects from within those boundaries. Cluster sampling design is used when natural groups occur in a population.
The entire population is subdivided into clusters groups and random samples are then gathered from each group. Cluster sampling is a very typical method for market research. The cluster sampling requires heterogeneity in the clusters and homogeneity between them. Each cluster must be a small representation of the whole population.
The key difference between non-probability and probability sampling is that the first one does not include random selection. Non-probability sampling is a group of sampling techniques where the samples are collected in a way that does not give all the units in the population equal chances of being selected. Probability sampling does not involve random selection at all.
Most commonly, the units in a non-probability sample are selected on the basis of their accessibility. They can be also selected by the purposive personal judgment of you as a researcher.
Types of Non-Probability Sampling Methods. There are many types of non-probability sampling techniques and designs, but here we will list some of the most popular.
As the name suggests, this method involves collecting units that are the easiest to access: It forms an accidental sample. It is generally known as an unsystematic and careless sampling method.
For example, people intercepted on the street, Facebook fans of a brand and etc. This technique is known as one of the easiest, cheapest and least time-consuming types of sampling methods. Quota sampling methodology aims to create a sample where the groups e. The population is divided into groups also called strata and the samples are gathered from each group to meet a quota.
Judgmental sampling is a sampling methodology where the researcher selects the units of the sample based on their knowledge. This type of sampling methods is also famous as purposive sampling or authoritative sampling. In this method, units are selected for the sample on the basis of a professional judgment that the units have the required characteristics to be representatives of the population.
Judgmental sampling design is used mainly when a restricted number of people possess the characteristics of interest. It is a common method of gathering information from a very specific group of individuals. It is a methodology where researcher recruits other individuals for the study. This method is used only when the population is very hard-to-reach. For example, these include populations such as working prostitutes, current heroin users, people with drug addicts, and etc.
The key downside of a snowball sample is that it is not very representative of the population. Sampling can be a confusing activity for marketing managers carrying out research projects. By knowing and understanding some basic information about the different types of sampling methods and designs, you can be aware of their advantages and disadvantages.
The two main sampling methods probability sampling and non-probability sampling has their specific place in the research industry. In the real research world, the official marketing and statistical agencies prefer probability-based samples.
Multistage Sampling (in which some of the methods above are combined in stages) Of the five methods listed above, students have the most trouble distinguishing between .
Types of sampling design in Research Methodology There are different types of sample designs based on two factors viz., the representation basis and the element selection technique. On the representation basis, the sample may be probability sampling or it may be non-probability sampling.
Sampling Methods. Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives. Probability sampling is a technique wherein the samples are gathered in a process that gives all the individuals in the population equal chance of being selected. Many consider this to be the more methodologically rigorous approach to sampling because it eliminates social biases that could shape the research sample.
RESEARCH METHOD - SAMPLING 1. Sampling Techniques & Samples Types 2. Outlines Sample definition Purpose of sampling Stages in the selection of a sample Types of sampling in quantitative researches Types of sampling in qualitative researches Ethical Considerations in Data Collection. There are many methods of sampling when doing research. This guide can help you choose which method to use. Simple random sampling is the ideal, but researchers seldom have the luxury of time or money to access the whole population, so many compromises often have to be made.