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Types of Sampling Methods and Techniques in Research

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❶For example, a researcher might study the success rate of a new 'quit smoking' program on a test group of patients, in order to predict the effects of the program if it were made available nationwide.

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In your textbook, the two types of non-probability samples listed above are called "sampling disasters. The article provides great insight into how major polls are conducted.

When you are finished reading this article you may want to go to the Gallup Poll Web site, https: It is important to be mindful of margin or error as discussed in this article. We all need to remember that public opinion on a given topic cannot be appropriately measured with one question that is only asked on one poll.

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. Again, the questions of interest would affect which sampling method should be used.

Non-probability Sampling The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided: Welcome to STAT !

Benefits, Risks, and Measurements Lesson 3: Lesson 3 - Have Fun With It! Getting the Big Picture and Summaries Lesson 5: In systematic probability random sampling the researcher selects every kth element in the population. The value of k can be taken by dividing the number of units in the population by the number of units in the sample. In household surveys this technique is most commonly used for sampling. In stratified random sampling the researcher divides the population in strata but in cluster sampling the researcher identifies clusters or groups in the population.

Units are selected from each cluster and taken in the sample. These clusters or groups are usually naturally found in the population and the researcher does not divide the population himself. Non-random sampling designs are also known as non-probability sampling designs.

These samples are not taken on the principles of probability. It does not mean, however, that these samples are not representable, valid or generalizable to the whole population. There are times when the researcher cannot take random sample from the population and he is forced to select a non-random sample. Taking a random sample is difficult as not all the units or members of that population will be ready to share their views or fill questionnaires.

The researcher has to ask them and if they will be willing he can only then take observations or interviews. In psychology, social sciences and behavioral sciences there is always a posed risk of not getting a random sample and hence non-random sampling techniques has to be used.

There are different types of sampling designs in this type of sampling:. In quota sampling the investigator or the researcher decides about the number of samples to be taken and then he can freely choose any sample from the population.

There is no distinction and he can choose any unit. The investigator in this type of sampling selects the units from the population according to his own judgement. The reason might be that the investigator thinks certain elements in the population to be more fit for the survey than other.

In snowball sampling the investigator selects one element in the population randomly or non randomly to ask questions. The investigator asks that individual to identify another element of the population that can be taken in the sample. In convenience sampling the researcher or the investigator selects samples according to his convenience.

He selects sample that are easily available or more easy to ask questions. These are only few of the sampling types in research and there can be many more depending on the type of study. Tags research sample sample samples sampling sampling in research sampling type sampling types. The APA in-text citation follows the author-date system of citation. This means that the researcher ….

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A stratified sample is a mini-reproduction of the population. Before sampling, the population is divided into characteristics of importance for the research. For example, .

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Ultimately, though, the sampling technique you choose should be the one that best allows you to respond to your particular research question. Let's review four kinds of probability sampling techniques.

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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. 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.

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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. Types of Probability Sampling Methods. Simple Random Sampling. This is the purest and the clearest probability sampling design and strategy. It is also the most popular way of a selecting a sample because it creates samples that are very highly representative of the population.. Simple random is a fully random technique of selecting subjects.