Types of sampling in statistics pdf

A sample cluster is selected using simple random sampling method and then survey is conducted on people of that sample cluster. Multistage sampling - In such case, combination of different sampling methods at different stages. For example, at first stage, cluster sampling can be used to choose clusters from population and then sample random sampling can be used to choose elements from each cluster for the final sample.

Systematic random sampling - In this type of sampling method, a list of every member of population is created and then first sample element is randomly selected from first k elements. Thereafter, every kth element is selected from the list. Non-probability sampling methods are convenient and cost-savvy.

But they do not allow to estimate the extent to which sample statistics are likely to vary from population parameters. Whereas probability sampling methods allows that kind of analysis. Following are the types of non-probability sampling methods:. Voluntary sample - In such sampling methods, interested people are asked to get involved in a voluntary survey.

A good example of voluntary sample in on-line poll of a news show where viewers are asked to participate. In voluntary sample, viewers choose the sample, not the one who conducts survey. Convenience sample - In such sampling methods, surveyor picks people who are easily available to give their inputs. For example, a surveyer chooses a cinema hall to survey movie viewers. There are two major categories in sampling: probability and non-probability sampling.

Sampling methods are classified as either probability or nonprobability. In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. In nonprobability sampling, members are selected from the population in some nonrandom manner. Question 7 7. This is the currently selected item. Samples and surveys. Next tutorial. Types of studies experimental vs.

Sampling methods review. What are sampling methods? In a statistical study, sampling methods refer to how we select members from the population to be in the study. In proportionate sampling, the sample sizes are proportional to the population sizes. For example, if the potential samples are 50 percent male and 50 percent female, the samples chosen are also 50 percent male and 50 percent female. Sampling error is condensed by selecting a large sample and by using proficient sample design and inference approaches.

The sampling should be done in a way that it is within the research budget and should not be too expensive to be replicated. The best bet for researchers is to sense the causes and correct them.

Population is commonly related to the number of people living in a particular country. Taking a subset from chosen sampling frame or entire population is called sampling. Sampling can be used to make inferences about a population or to make generalization in relation to existing theory.

In essence, this depends on the choice of sampling technique. Probability Sampling Probability sampling means that every item in the population has an equal chance of being included in the sample. One way to undertake random sampling would be if researcher was to construct a sampling frame first and then used a random number generation computer program to pick a sample from the sampling frame. Probability or random sampling has the Disadvantages associated with simple random sampling include: A complete frame a list of all units in the whole population is needed; in some studies, such as surveys by personal interviews, the costs of obtaining the sample can be high if the units are geographically widely scattered; The standard errors of estimators can be high.

For example, if surveying a sample of consumers, every fifth consumer may be selected from your sample. The advantage of this sampling technique is its simplicity. A subgroup is a natural set of items. Subgroups might be based on company size, gender or occupation to name but a few. Stratified sampling is often used where there is a great deal of variation within a population.

Subsequently, a random sample is taken from these clusters, all of which are used in the final Cluster sampling is advantageous for those researchers whose subjects are fragmented over large geographical areas as it saves time and money. If, for example, a Malaysian publisher of an automobile magazine were to conduct a survey, it could simply take a random sample of automobile owners within the entire Malaysian population. Obviously, this is both expensive and time consuming.

A cheaper alternative would be to use multi-stage sampling. In essence, this would involve dividing Malaysia into a number of geographical regions. Subsequently, some of these regions are chosen at random, and then subdivisions are made, perhaps based on local authority areas. Next, some of these are again chosen at random and then divided into smaller areas, such as towns or cities. The main purpose of multi-stage sampling is to select samples which are concentrated in a few geographical regions.

Once again, this saves time and money. Non probability Sampling Non probability sampling is often associated with case study research design and qualitative research. With regards to the latter, case studies tend to focus on small samples and are intended to examine a real life phenomenon, not to make statistical inferences in relation to the wider population.

A sample of participants or cases does not need to be representative, or This approach is most applicable in small populations that are difficult to access due to their closed nature, e. Typically, convenience sampling tends to be a favored sampling technique among students as it is inexpensive and an easy option compared to other sampling techniques.

Convenience sampling often helps to overcome many of the limitations associated with research. For example, using friends or family as part sample is easier than targeting unknown individuals. It is where the researcher includes cases or participants in the sample because they believe that they warrant inclusion.

What is adequate depends on several issues which often confuses people doing surveys for the first time. This is because what is important here is not the proportion of the research population that gets sampled, but the absolute size of the sample selected relative to the complexity of the population, the aims of the researcher and the kinds of statistical manipulation that will be used in data analysis.

To put it bluntly, larger sample sizes reduce sampling error but at a decreasing rate. Several statistical formulas are available for determining sample size. There are numerous approaches, incorporating a number of different formulas, for calculating the sample size for categorical data. These cases are taken from the original sample. In reality, most researchers never achieve a percent response rate.

Reasons for this might include refusal to respond, ineligibility to respond, inability to respond, or the respondent has been located but researchers are unable to make contact. In sum, response rate is important because each non response is liable to bias the final sample. Clearly defining sample, employing the right sampling technique and generating a large sample, in some respects can help to reduce the likelihood of sample bias.

These are the sampling principles. These precautions are to be taken at some specific points during the sampling procedure An essential tenet to be kept in mind is that the basic motive behind sampling is analysing the units in the sample and deduce results from the study, which can be generalised to the universe from which the sample was drawn.

Sample is the representative of the universe. Research conducted on the sample is for making inferences about the universe. Sampling techniques should be chosen with care and caution, so as to obtain the most appropriate sample. It is advisable to not see these factors as an impediment to research, but to utilise them in the most efficient way possible.

Even with the advent of technology, care must be taken by the researcher that the selected respondents are source of objective, unbiased answers. It should also be ensured to the maximum possible extent that the potential respondents are not being forced for participation in the research. This can be done by the procedure of elimination.