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Sampling (statistics)

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❶There are many methods of sampling when doing research. After sampling, a review should be held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis.

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This is when a list containing all of the population is created and used to obtain participants by random selection. This random selection guarantees that each individual has an independent and equal chance of being selected. This method is very fair, unbiased and easy to carry out. However, with simple random sampling there is no assurance of complete representativeness of the sample.

Another example of simple random sampling is cluster sampling. This is when the sample is gained by the random selection of clusters pre-existing groups of individuals from a list containing all of the clusters existing within a population. This method is easy for obtaining a large and relatively random selection of participants, however, the selections lack independence.

Convenience sampling is a method of nonprobability sampling. With convenience sampling, the sample is made up of individual participants who are easy to get.

The participants were individuals who had volunteered by responding to a newspaper article. Convenience sampling is easy to carry out, but one large disadvantage is that the sample is likely to be biased. Finally, quota sampling is another method of nonprobability sampling. This is when different subgroups are identified and participants are selected through convenience from each different subgroup. For example, say a researcher wanted to select a sample of students to participate in a study using a convenience sample but wanted to ensure that an equal number of boys and girls were selected — quota sampling would be the best method for them to use.

This type of sampling can help to control a convenience sample but may results in a biased sample, which would not be a good representative of the wider population. As I mentioned earlier, the goal of research is to study a sample of participants and then generalise the results to the larger population.

How far we can extend such results to generalise to a population is dependant on how closely the sample resembles the population — the representativeness. The main threat to representativeness is bias.

A biased sample is one which contains characteristics that are different from those of the population. This bias may happen by chance, but usually is down to selection bias. Selection bias is when participants are selected in a way that increases the probability of acquiring a biased sample. For example, if a researcher recruits participants from a gym, they are more likely to be healthier and fitter than the rest of the general public.

I can definitely say that the selection of participants is a very vital part of planning research. Without carefully planning and choosing an appropriate method for sampling it is very easy to obtain a biased sample that does not represent the population. When this happens, it is difficult to extend findings to a wider population and the validity of the experiment decreases. In order to produce influential and meaningful results, researchers must ensure that they have chosen an appropriate sampling method to select a representative sample of participants.

Behavioral study of obedience. Journal of Abnormal and Social Psychology, 72, This is a very interesting blog. I have written in one of my blogs about generalisation, and have never mentioned or considered sampling methods.

Like you have mentioned convenience sampling is a cheap and easy way to recruit participants, and can lead to biases, which is something that I get really angry about, I know that it is impossible to be perfect on representing the entire population, but I just feel like some methods that are used shut off a large amount of people. It is unlikely that research will ever represent the whole population, but it is good that there are a number of methods so that representation can be increased, and at the same time reduce biases.

Homework for my TA. I have a question related to the sampling techniques described here. I will be grateful if you respond my queries please. Hi, suraiya khatoon, this was every intresting type of convenience sampling. The turn up was good hence less bias.

I got what you intend, thankyou for putting up. Woh I am lucky to find this website through google. Being intelligent is not a felony, but most societies evaluate it as at least a misdemeanor. I want to know what is the basis? Hai I am Dr. Remya,I was searching sampling techniques as a part of my study,i found your blog useful,easily comprehensible.

Reblogged this on innocenttauzen. Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling.

Stratified Sampling is possible when it makes sense to partition the population into groups based on a factor that may influence the variable that is being measured. These groups are then called strata. An individual group is called a stratum. With stratified sampling one should:. Stratified sampling works best when a heterogeneous population is split into fairly homogeneous groups.

Under these conditions, stratification generally produces more precise estimates of the population percents than estimates that would be found from a simple random sample. Cluster Sampling is very different from Stratified Sampling.

With cluster sampling one should. It is important to note that, unlike with the strata in stratified sampling, the clusters should be microcosms, rather than subsections, of the population. Each cluster should be heterogeneous. Additionally, the statistical analysis used with cluster sampling is not only different, but also more complicated than that used with stratified sampling. Each of the three examples that are found in Tables 3.

However, there are obviously times when one sampling method is preferred over the other. The following explanations add some clarification about when to use which method. The most common method of carrying out a poll today is using Random Digit Dialing in which a machine random dials phone numbers.

Some polls go even farther and have a machine conduct the interview itself rather than just dialing the number! Such " robo call polls " can be very biased because they have extremely low response rates most people don't like speaking to a machine and because federal law prevents such calls to cell phones. Since the people who have landline phone service tend to be older than people who have cell phone service only, another potential source of bias is introduced.

National polling organizations that use random digit dialing in conducting interviewer based polls are very careful to match the number of landline versus cell phones to the population they are trying to survey. The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided:. Since such non-probability sampling methods are based on human choice rather than random selection, statistical theory cannot explain how they might behave and potential sources of bias are rampant.

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:

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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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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.

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This can be accomplished by using randomized statistical sampling techniques or probability sampling like cluster sampling and stratified sampling. 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. 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.

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Feb 19,  · Research studies are distinct events that involve a particular group of participants. However, researchers usually intend on answering a general question about a larger population of individuals rather than a small select group. Therefore, the main aim of psychological research is to be able to make valid generalisations and extend their . This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitat.