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Sampling (statistics)

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❶This is the best overall group of methods to use as you can subsequently use the most powerful statistical analyses on the results. Simple linear regression Ordinary least squares General linear model Bayesian regression.

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Concerns in Statistical Sampling

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.

This can be accomplished by using randomized statistical sampling techniques or probability sampling like cluster sampling and stratified sampling. 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. For a particular analysis and valid results, you can determine the number of people you need to sample.

In particular when you are studying a number of groups and when sub-groups are small, then you will need equivalent numbers to enable equivalent analysis and conclusions.

Good sampling is time-consuming and expensive. Not all experimenters have the time or funds to use more accurate methods.

There is a price, of course, in the potential limited validity of results. When doing field-based observations, it is often impossible to intrude into the lives of people you are studying.

Samples must thus be surreptitious and may be based more on who is available and willing to participate in any interviews or studies. Please help and share: Method Best when Simple random sampling Whole population is available. Stratified sampling random within target groups There are specific sub-groups to investigate eg. 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.

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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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Techniques > Research > Sampling > Choosing a sampling method Probability | Quota | Selective | Convenience | Ethnographic | See also There are many methods of sampling when doing research.

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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. 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 .

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Video: What is Sampling in Research? - Definition, Methods & Importance - Definition, Methods & Importance The sample of a study can have a profound impact on the outcome of a study. 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.