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Sampling (statistics)

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❶In the above example, not everybody has the same probability of selection; what makes it a probability sample is the fact that each person's probability is known.

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Probability vs. Non-Probability Samples
Population Parameter vs. Sample Statistic
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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.

This is generally done to insure the inclusion of a particular segment of the population. The proportions may or may not differ dramatically from the actual proportion in the population. This would be a volunteer sample. The sample is chosen by the viewers, not by the survey administrator. Consider the following example.

A pollster interviews shoppers at a local mall. The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population. This ensures that the statistical conclusions will be valid. There are many ways to obtain a simple random sample. One way would be the lottery method.

Each of the N population members is assigned a unique number. The numbers are placed in a bowl and thoroughly mixed.

Then, a blind-folded researcher selects n numbers. Population members having the selected numbers are included in the sample. As a example, suppose we conduct a national survey.

We might divide the population into groups or strata, based on geography - north, east, south, and west. Then, within each stratum, we might randomly select survey respondents. Note the difference between cluster sampling and stratified sampling. Qualitative Research Overview - The following link provides a full overview of qualitative research, but also contains sections discussing types of sampling methods and methods of participant recruitment.

Sampling - This resource provides a brief overview of sampling and sample size with links to descriptions of purposeful sampling strategies. A Guide to Using Qualitative Research Methodology - The file linked below contains a full description of how to conduct qualitative sampling, including a chart that lists the types of sampling techniques and includes examples.

Sampling Designs in Qualitative Research - The following article discusses sampling designs and ways to make the sampling process more public. This pin will expire , on Change. This pin never expires. Select an expiration date. About Us Contact Us. Search Community Search Community. Qualitative Sampling Methods The following module describes common methods for collecting qualitative data.

Describe common types of qualitative sampling methodology. Explain the methods typically used in qualitative data collection. Describe how sample size is determined. Purposeful Sampling is the most common sampling strategy.

In this type of sampling, participants are selected or sought after based on pre-selected criteria based on the research question. For example, the study may be attempting to collect data from lymphoma patients in a particular city or county.

The sample size may be predetermined or based on theoretical saturation, which is the point at which the newly collected no longer provides additional insights.


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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.

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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.

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Social research is a scientific method to understand human behavior which is done by sending out surveys to a targeted sample. There are two basic types of sampling for social research, Probability, and Non-probability sampling. These sampling types are divided on the basis of the selection of members and are implemented in different situations. How to do sampling for qual and quant research designs Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website.

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Non-probability Sampling: Sample does not have known probability of being selected as in convenience or voluntary response surveys; Probability Sampling. In probability sampling it is possible to both determine which sampling units belong to which sample and . Snowball sampling isn’t one of the common types of sampling methods but still valuable in certain cases. 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.