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Choosing a sampling method

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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. A population is a group of individual units with some commonality. For example, a researcher may want to study characteristics of female smokers in the United States.

This would be the population being analyzed in the study, but it would be impossible to collect information from all female smokers in the U. Therefore, the researcher would select individuals from which to collect the data. This is called sampling. The group from which the data is drawn is a representative sample of the population the results of the study can be generalized to the population as a whole.

The sample will be representative of the population if the researcher uses a random selection procedure to choose participants. The group of units or individuals who have a legitimate chance of being selected are sometimes referred to as the sampling frame. If a researcher studied developmental milestones of preschool children and target licensed preschools to collect the data, the sampling frame would be all preschool aged children in those preschools. Students in those preschools could then be selected at random through a systematic method to participate in the study.

This does, however, lead to a discussion of biases in research. For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study. Extra care has to be taken to control biases when determining sampling techniques. There are two main types of sampling: The difference between the two types is whether or not the sampling selection involves randomization.

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.

The researcher sets a quota , independent of population characteristics. A purposive sample is a non-representative subset of some larger population, and is constructed to serve a very specific need or purpose. A researcher may have a specific group in mind, such as high level business executives. It may not be possible to specify the population -- they would not all be known, and access will be difficult. The researcher will attempt to zero in on the target group, interviewing whomever is available.


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A sampling frame is the group of people from which you will draw your sample. For example, Brooke might decide that her sampling frame is every student at the university where she works. For example, Brooke might decide that her sampling frame is every student at the university where she works.

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Choosing a sampling method. Techniques > Research > Sampling > Choosing a sampling method. Probability | Quota | Selective | Convenience | Ethnographic | See also. There are many methods of sampling when doing research. This guide can help you choose which method to use.

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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 the population from which they were chosen. SAMPLING IN RESEARCH Sampling In Research Mugo Fridah W. INTRODUCTION This tutorial is a discussion on sampling in research it is mainly designed to eqiup beginners with knowledge on the general issues on sampling that is the purpose of sampling in research, dangers of.

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How to do sampling for qual and quant research designs. Before sampling, the population is divided into characteristics of importance for the research. For example, by gender, social class, education level, religion, etc. Then the population is randomly sampled within each category or stratum.