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Sampling (statistics)

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## Probability vs. Non-Probability Samples

❶This ensures that the statistical conclusions will be valid.
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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. This is the best overall group of methods to use as you can subsequently use the most powerful statistical analyses on the results.

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. 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.

A public opinion pollster wants to know the percentage of voters that favor a flat-rate income tax. The actual percentage of all the voters is a population parameter.

The estimate of that percentage, based on sample data, is a sample statistic. The quality of a sample statistic i. Non-probability sampling methods offer two potential advantages - convenience and cost. The main disadvantage is that non-probability sampling methods do not allow you to estimate the extent to which sample statistics are likely to differ from population parameters. Only probability sampling methods permit that kind of analysis.

Two of the main types of non-probability sampling methods are voluntary samples and convenience samples. Suppose, for example, that a news show asks viewers to participate in an on-line poll. 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.

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.

How to do sampling for qual and quant research designs. 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.

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. Covers survey sampling methods. Describes probability and non-probability samples, from convenience samples to multistage random samples. Includes free video.