So, only a sample is studied when conducting statistical or marketing research. In simple words, probability sampling also known as random sampling or chance sampling utilizes random sampling techniques and principles to create a sample. For example, if we have a population of people, each one of the persons has a chance of 1 out of of being chosen for the sample. Types of Probability Sampling Methods. This is the purest and the clearest probability sampling design and strategy.
It is also the most popular way of a selecting a sample because it creates samples that are very highly representative of the population. Simple random is a fully random technique of selecting subjects. 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.
Before sampling, the population is divided into characteristics of importance for the research. For example, by gender, social class, education level, religion, etc. Then the population is randomly sampled within each category or stratum. How to Construct a probability representative sample. As they are not truly representative, non-probability samples are less desirable than probability samples. However, a researcher may not be able to obtain a random or stratified sample, or it may be too expensive.
A researcher may not care about generalizing to a larger population. The validity of non-probability samples can be increased by trying to approximate random selection, and by eliminating as many sources of bias as possible. A researcher is interested in the attitudes of members of different religions towards the death penalty.
In Iowa a random sample might miss Muslims because there are not many in that state. However, the sample will no longer be representative of the actual proportions in the population. This may limit generalizing to the state population. But the quota will guarantee that the views of Muslims are represented in the survey. A subset of a purposive sample is a snowball sample -- so named because one picks up the sample along the way, analogous to a snowball accumulating snow. A snowball sample is achieved by asking a participant to suggest someone else who might be willing or appropriate for the study.
Snowball samples are particularly useful in hard-to-track populations, such as truants, drug users, etc. Non-probability samples are limited with regard to generalization. Because they do not truly represent a population, we cannot make valid inferences about the larger group from which they are drawn. Validity can be increased by approximating random selection as much as possible, and making every attempt to avoid introducing bias into sample selection.
Examples of nonprobability samples. Using the random numbers table. Two of each species. Random sample The term random has a very precise meaning. The defining characteristic of a quota sample is that the researcher deliberately sets the proportions of levels or strata within the sample.
Techniques > Research > Sampling > Choosing a sampling method Probability | Quota | Selective | Convenience | Ethnographic | See also There are many methods of sampling when doing research.
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.
Sampling Methods. Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives: Define sampling and randomization. Explain probability and non-probability sampling and describes the different types of each. Simple Random Sampling (SRS) Stratified Sampling; Cluster Sampling; Systematic Sampling; 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 stratified sampling and cluster sampling.