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Sampling (statistics)

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❶Sampling is often clustered by geography, or by time periods.
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There are no strict rules to follow, and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study.

Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population.

Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn. Sampling methods are classified as either probability or nonprobability. In probability samples, each member of the population has a known non-zero probability of being selected.

Probability methods include random sampling, systematic sampling, and stratified sampling. In nonprobability sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling.

The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error.

In nonprobability sampling, the degree to which the sample differs from the population remains unknown. Such results only provide a snapshot at that moment under certain conditions. The concept of repeating procedures over different conditions and times leads to more valuable and durable results.

Within this section of the Gallup article, there is also an error: In 5 of those surveys, the confidence interval would not contain the population percent. Eberly College of Science. Printer-friendly version Sampling Methods can be classified into one of two categories: 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 Probability Sampling In probability sampling it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected.

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. With stratified sampling one should: With cluster sampling one should divide the population into groups clusters. Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone.

For example the percentage of people watching a live sporting event on television might be highly affected by the time zone they are in.

Cluster sampling really works best when there are a reasonable number of clusters relative to the entire population. In this case, selecting 2 clusters from 4 possible clusters really does not provide much advantage over simple random sampling.

Either stratified sampling or cluster sampling could be used. It would depend on what questions are being asked. For instance, consider the question "Do you agree or disagree that you receive adequate attention from the team of doctors at the Sports Medicine Clinic when injured?

In contrast, if the question of interest is "Do you agree or disagree that weather affects your performance during an athletic event? Consequently, stratified sampling would be preferred.

Cluster sampling would probably be better than stratified sampling if each individual elementary school appropriately represents the entire population as in aschool district where students from throughout the district can attend any school. Stratified sampling could be used if the elementary schools had very different locations and served only their local neighborhood i.

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. Click on the following link for a desciption of types of purposeful sampling: Types of Purposeful Sampling.

Quota Sampling is a sampling technique whereby participant quotas are preset prior to sampling. Typically, the researcher is attempting to gather data from a certain number of participants that meet certain characteristics that may include things such as age, sex, class, marital status, HIV status, etc. Click here for more information on this type of sampling: Snowball Sampling is also known as chain referral sampling.

In this method, the participants refer the researcher to others who may be able to potentially contribute or participate in the study.

This method often helps researchers find and recruit participants that may otherwise be hard to reach. For more information, click here: Collecting Qualitative Data from highness Qualitative Sampling Methods by ProProfs.

Resource Links Qualitative Research Methods - A Data Collectors Field Guide - This comprehensive, detailed guide describes various types of sampling techniques and provides examples of each, as well as pros and cons. Page Options Share Email Link.

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.

Sampling Methods. Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives.

In probability sampling it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected. The following sampling methods are examples of probability sampling: Of the five methods listed above, students have the most trouble. This type of research is called a census study because data is gathered on every member of the population. Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. Sampling methods are classified as either probability or.

Qualitative Research Methods - A Data Collectors Field Guide - This comprehensive, detailed guide describes various types of sampling techniques and provides examples of each, as well as pros and cons. Sampling Let's begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of the statistical terms used in sampling.