A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.
Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient.
This nonprobability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample. Judgment sampling is a common nonprobability method. The researcher selects the sample based on judgment. This is usually and extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities.
When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population. Quota sampling is the nonprobability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. The reason behind representativeness being the primary concern in statistical sampling is that it allows the researcher to draw conclusions for the entire population.
If the sample is not representative of the population, conclusions cannot be drawn since the results that the researcher obtained from the sample will be different from the results if the entire population is to be tested. All these factors contribute to the decisions of the researcher regarding to the study design. There are two types of sampling risks , first is the risk of incorrect acceptance of the research hypothesis and the second is the risk for incorrect rejection.
These risks pertain to the possibility that when a test is conducted to a sample, the results and conclusions may be different from the results and conclusions when the test is conducted to the entire population. The risk of incorrect acceptance pertains to the risk that the sample can yield a conclusion that supports a theory about the population when it is actually not existent in the population. On the other hand, the risk of incorrect rejection pertains to the risk that the sample can yield a conclusion that rejects a theory about the population when in fact, the theory holds true in the population.
Comparing the two types of risks, researchers fear the risk of incorrect rejection more than the risk of incorrect acceptance. Consider this example; an experimental drug was tested for its debilitating side effects. The entire population will then abstain from taking the drug. But with the risk of incorrect rejection, the researcher will conclude that the drug has no negative side effects.
The entire population will then take the drug knowing that it has no side effects but all of them will then suffer the consequences of the mistake of the researcher. Systematic sampling every nth person When a stream of representative people are available eg.
Cluster sampling all in limited groups When population groups are separated and access to all is difficult, eg. Method Best when Quota sampling get only as many as you need You have access to a wide population, including sub-groups Proportionate quota sampling in proportion to population sub-groups You know the population distribution across groups, and when normal sampling may not give enough in minority groups Non-proportionate quota sampling minimum number from each sub-group There is likely to a wide variation in the studied characteristic within minority groups.
Method Best when Purposive sampling based on intent You are studying particular groups Expert sampling seeking 'experts' You want expert opinion Snowball sampling ask for recommendations You seek similar subjects eg.
Method Best when Snowball sampling ask for recommendations You are ethically and socially able to ask and seek similar subjects. Convenience sampling use who's available You cannot proactively seek out subjects. Judgment sampling guess a good-enough sample You are expert and there is no other choice. Method Best when Selective sampling gut feel Focus is needed in particular group, location, subject, etc.
Theoretical sampling testing a theory Theories are emerging and focused sampling may help clarify these. Home Top Menu Quick Links. Probability methods This is the best overall group of methods to use as you can subsequently use the most powerful statistical analyses on the results.
When population groups are separated and access to all is difficult, eg.
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.
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.
How to do sampling for qual and quant research designs. 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 Sampling in Qualitative Research Researchers in.
Sampling Methods can be classified into one of two categories: Probability Sampling: Sample has a known probability of being selected. Non-probability Sampling: Sample does not have known probability of being selected as in convenience or voluntary response surveys. Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to .