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Splitting a population into groups with similar characteristics before sampling is called?
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A population is divided into non-overlapping similar groups from which to be sampled what type of sampling method is this?
Stratified Random Sampling. Google it. .
If it has similar characteristics as the population the sample is termed?
It is called a representative sample.
What is consecutive sampling?
Consecutive sampling is very similar to convenience sampling except that it seeks to include ALL accessible subjects as part of the sample. This non-probability sampling technique can be considered as the best of all non-probability samples because it includes all subjects that are available that makes the sample a better representation of the entire population.
When do you need non-probability sampling?
There are at least two situations. Consider the situation where the population consists of a number of sub-populations (strata) such that units within a sub-population are similar to one another but there are much larger differences between units from different sub-populations. In order to ensure that the sample is representative, it may be sensible to use stratified random sampling. The sampling proportion may be a constant proportion or may even be such that the variance in each stratum is similar. The situation may also arise if the population is widely scattered geographically. Rather than expend time and money travelling all over the place, you could employ cluster sampling. Select a number of clusters of the population and then, within each cluster, carry out a census.
Which sampliing method subdivides the population into categories sharing similar characteristics and then selects a sample from each subdivision?
Stratified
A population is divided into non-overlapping similar groups from which to be sampled what type of sampling method is this?
Stratified Random Sampling. Google it. .
If it has similar characteristics as the population the sample is termed?
It is called a representative sample.
What is consecutive sampling?
Consecutive sampling is very similar to convenience sampling except that it seeks to include ALL accessible subjects as part of the sample. This non-probability sampling technique can be considered as the best of all non-probability samples because it includes all subjects that are available that makes the sample a better representation of the entire population.
What is the difference between practical generalizability and statistical generalizability?
Statistical: must have random sampling, allows you to generalize to the population from which you randomly selected. Practical: do the results hold for similar individuals? allows you to generalize to similar individuals
When do you need non-probability sampling?
There are at least two situations. Consider the situation where the population consists of a number of sub-populations (strata) such that units within a sub-population are similar to one another but there are much larger differences between units from different sub-populations. In order to ensure that the sample is representative, it may be sensible to use stratified random sampling. The sampling proportion may be a constant proportion or may even be such that the variance in each stratum is similar. The situation may also arise if the population is widely scattered geographically. Rather than expend time and money travelling all over the place, you could employ cluster sampling. Select a number of clusters of the population and then, within each cluster, carry out a census.
Which sampliing method subdivides the population into categories sharing similar characteristics and then selects a sample from each subdivision?
Stratified
What is difference between sample and representative sample?
A sample is any subset of the total population. A representative sample is one that is chosen so that its characteristics are similar to that of the population.
What is a stratified random sample?
Stratified random sampling is a sampling scheme which is used when the population comprises a number of strata, or subsets, which are similar within the strata but differ from one stratum to another. One example is school children stratified according to classes, or salaries stratified by departments.A simple random sample may not have enough representatives from each stratum and the solution is to use stratified random sampling. Under this scheme, the overall sampling proportion (sample size/population size) is determined and a sample is drawn from each stratum which represents the same proportion.
What is meant by sampling in computer graphics?
Sampling is the key technique used to digitize analog information. For example, music CDs are produced by sampling live sound at frequent intervals and then digitizing each sample. The term sampling is also used to describe a similar process in digital photography.
What do you learn from identifications of individuals and population?
The identification of people and population can help researchers learn about similar characteristics between certain individuals of the same population. Population is defined as a group of people living in an area, interacting with each other constantly.
You are in charge of promoting a new flavor of toothpaste yet to be produced in a toothpaste manufacturing unit which sampling techniques will you use to get the required data from a population?
Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice. In business, sampling is widely used for gathering information about a populationIt is incumbent on the researcher to clearly define the target population. There are no strict rules to follow, and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study.Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population.Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn.Sampling methods are classified as either probability or non-probability. 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 non-probability sampling, members are selected from the population in some non-random manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In non probability sampling, the degree to which the sample differs from the population remains unknown.· Random samplingis the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.· Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.· Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.· Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This non-probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.· Judgment sampling is a common non-probability method. The researcher selects the sample based on judgment. This is usually an extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.· Quota samplingis the non-probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.· Snowball sampling is a special non-probability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.If I were an officer to promote a new flavour of toothpaste yet to be produced, I would use RANDOM SAMPLING METHODØ Random sampling:Random sampling- all members of the population have an equal chance of being selected as part of the sample. You might think this means just standing in the street and asking passers-by to answer your questions. However, there would be many members of the population who would not be in the street at the time you are there; therefore, they do not stand any chance of being part of your sample. To pick a random sample, it is necessary to take all the names on the electoral register (A list of all the people who live in a particular area) and pick out, for example, every fiftieth name. This particular person needs to be interviewed to make the sample truly random. Random sampling is very expensive and time consuming, but gives a true sample of the population.Types of Random sample:A simple random sample is selected so that all samples of the same size have an equal chance of being selected from the population.A self-weighting sample, also known as an EPSEM (Equal Probability of Selection Method) sample, is one in which every individual, or object, in the population of interest has an equal opportunity of being selected for the sample. Simple random samples are self-weighting.Stratified sampling involves selecting independent samples from a number of subpopulations, group or strata within the population. Great gains in efficiency are sometimes possible from judicious stratification.Cluster sampling involves selecting the sample units in groups. For example, a sample of telephone calls may be collected by first taking a collection of telephone lines and collecting all the calls on the sampled lines. The analysis of cluster samples must take into account the intra-cluster correlation which reflects the fact that units in the same cluster are likely to be more similar than two units picked at randomØ Pros and Cons:1. There are lot of bias in Random sampling2. It is feasible and simple as the sampling is done on a random basis.3. Can make sample units in groups.4. Very expensive and time consuming, but gives a true result of the population5. While in toothpaste case, the users can given a sample piece of toothpaste randomly to get the feedback or their opinion from the chosen populationConclusion: Though the Random sampling has couple of de-merits it will help to figure out the result from the chosen population. While all other also may provide the result which may not be best comparing to the sampling method which I have chosen (Random Sampling)
How is a sample is termed if it has similar characteristics to the population being studied.?
what is the type of sample in which each member of the sample set or group has an equal chance of being chosen
