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Probability Sampling

The Advantages of Sampling

❶In survey sampling , weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Multistage sampling can substantially reduce sampling costs, where the complete population list would need to be constructed before other sampling methods could be applied.

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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. 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.

With probability sampling , all elements e. With nonprobability sampling , in contrast, population elements are selected on the basis of their availability e. The consequence is that an unknown portion of the population is excluded e.

Recruiting a probability sample is not always a priority for researchers. A scientist can demonstrate that a particular trait occurs in a population by documenting a single instance. For example, the assertion that all lesbians are mentally ill can be refuted by documenting the existence of even one lesbian who is free from psychopathology. Another situation in which a probability sample is not necessary is when a researcher wishes to describe a particular group in an exploratory way.

For example, interviewing 25 people with AIDS PWAs about their experiences with HIV could provide valuable insights about stress and coping, even though it would not yield data about the proportion of PWAs in the general population who share those experiences.

Types of probability samples. Many strategies can be used to create a probability sample. Each starts with a sampling frame , which can be thought of as a list of all elements in the population of interest e. The sampling frame operationally defines the target population from which the sample is drawn and to which the sample data will be generalized.

Probably the most familiar type of probability sample is the simple random sample , for which all elements in the sampling frame have an equal chance of selection, and sampling is done in a single stage with each element selected independently rather than, for example, in clusters. Somewhat more common than simple random samples are systematic samples , which are drawn by starting at a randomly selected element in the sampling frame and then taking every n th element e.

In yet another approach, cluster sampling , a researcher selects the sample in stages, first selecting groups of elements, or clusters e. Suppose some researchers want to find out which of two mayoral candidates is favored by voters. Obtaining a probability sample would involve defining the target population in this case, all eligible voters in the city and using one of many available procedures for selecting a relatively small number probably fewer than 1, of those people for interviewing.

For example, the researchers might create a systematic sample by obtaining the voter registration roster, starting at a randomly selected name, and contacting every th person thereafter. Or, in a more sophisticated procedure, the researchers might use a computer to randomly select telephone numbers from all of those in use in the city, and then interview a registered voter at each telephone number.

This procedure would yield a sample that represents only those people who have a telephone. Several procedures would also be available for recruiting a convenience sample, but none of them would include the entire population as potential respondents. For example, the researchers might ascertain the voting preferences of their own friends and acquaintances.

Or they might interview shoppers at a local mall. Or they might publish two telephone numbers in the local newspaper and ask readers to call either number in order to "vote" for one of the candidates. The important feature of these methods is that they would systematically exclude some members of the population respectively, eligible voters who do not know the researchers, do not go to the shopping mall, and do not read the newspaper.

Consequently, their findings could not be generalized to the population of city voters. Samples are evaluated primarily according to the procedures by which they were selected rather than by their final composition or size.

In the example above, it would be impossible to know if the convenience sample consisting of the researchers' friends or mall shoppers is representative, even if its demographic characteristics closely resembled those of the city electorate e.

And even if several thousand people called the published telephone numbers, the sample would be seriously biased. Of course, results from a probability sample might not be accurate for many reasons.

Using probability sampling procedures is necessary but not sufficient for obtaining results that can be generalized with confidence to the entire population. One of the major concerns about a probability sample is that its response rate is sufficiently high.

Once a sample is selected, an attempt is made to collect data e. Some sample members inevitably are traveling, hospitalized, incarcerated, away at school, or in the military. Others cannot be contacted because of their work schedule, community involvement, or social life.

Others simply refuse to participate in the study, even after the best efforts of the researcher to persuade them otherwise. Each type of nonparticipation biases the final sample, usually in unknown ways. In the General Social Survey GSS , for example, those who refused to be interviewed were later found to be more likely than others to be married, middle-income, and over 30 years of age, whereas those who were excluded from the survey because they were never at home were less likely to be married and more likely to live alone Smith, The importance of intensive efforts at recontacting sample members who are difficult to reach e.

The response rate describes the extent to which the final data set includes all sample members. It is calculated as the number of people with whom interviews are completed "completes" divided by the total number of people or households in the entire sample, including those who refused to participate and those who were not at home. Whether data are collected through face-to-face interviews, telephone interviews, or mail-in surveys, a high response rate is extremely important when results will be generalized to a larger population.

The lower the response rate, the greater the sample bias. Fowler , for example, warned that data from mail-in surveys with return rates of "20 or 30 percent, which are not uncommon for mail surveys that are not followed up effectively, usually look nothing at all like the sampled populations" Fowler, , p.

This is because "people who have a particular interest in the subject matter or the research itself are more likely to return mail questionnaires than those who are less interested" p. Fowler warned that: In such instances, the final sample has little relationship to the original sampling process.

Those responding are essentially self-selected. It is very unlikely that such procedures will provide any credible statistics about the characteristics of the population as a whole" p.

Simple Random Sampling

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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.

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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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What is Sampling? Imagine, for example, an experiment to test the effects of a new education technique on schoolchildren. It would be impossible to select the entire school age population of a country, divide them into groups and perform research.. A research group sampling the diversity of flowers in the African savannah could not count every single flower, because it would take many years. 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.

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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. 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.