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What are the different non-probability sampling techniques?
The related web sites give a good idea of the types of non-random sampling. These include snowball, convenience, quota, self-selection, diversity, expert, and others. Non-randon sampling is usually done because it is less expensive, easier, and quicker than random sampling.
Advantages and disadvantages of random sampling?
Advantage -- Less effort, cost, work Disadvantage -- Less accuracy, information, difficulty of establishing true 'randomness" in some samplings.
What happens When a random sampling frame has a systematic pattern in the listing of sampling units rather than a random pattern?
You get a non-random sample and any analysis based on the assumption of randomly distributed variables is no longer valid. In particular, your estimates of any variables are likely to be biased and your error estimates (standard errors or sample variances) will be incorrect. Any inferences based on statistical tests will be less reliable and may be wrong.
What is the ideal sampling frequency?
Not less than double the highest frequency component of the signal you're sampling.
Why is a random sample better than a census?
A random sample is better than a census because it takes less time and costs less.
What are the reasons for choosing random sampling method for ones research work and what is random sampling method anyway?
because it is the simplest sampling technique which requires less time and cost.
Which sampling method is based on probability?
There are many such methods: cluster sampling, stratified random sampling, simple random sampling.Their usefulness depends on the circumstances.
What are the different non-probability sampling techniques?
The related web sites give a good idea of the types of non-random sampling. These include snowball, convenience, quota, self-selection, diversity, expert, and others. Non-randon sampling is usually done because it is less expensive, easier, and quicker than random sampling.
Advantages and disadvantages of random sampling?
Advantage -- Less effort, cost, work Disadvantage -- Less accuracy, information, difficulty of establishing true 'randomness" in some samplings.
What happens When a random sampling frame has a systematic pattern in the listing of sampling units rather than a random pattern?
You get a non-random sample and any analysis based on the assumption of randomly distributed variables is no longer valid. In particular, your estimates of any variables are likely to be biased and your error estimates (standard errors or sample variances) will be incorrect. Any inferences based on statistical tests will be less reliable and may be wrong.
Why do you use sample populations?
e.g. you wanted to conduct a test on teenagers, if you wanted to test an entire population you would have to test every teenager in the world. BY using random sampling or stratisfied random sampling you can get fair results which represents the entire population and takes far less time.
What are the differences between sampling error and sampling bias?
Sampling bias is a known or unknown selection of data to be examined in an audit. There should be no bias if the sample is random. Ex ... look at the first item in the file folder. or examine all files for purchases over $10,000, or examine no files for sales less than $500. Sampling error, is the incorrect selection of files for an audit. Ex ... a random number generator tells you to audit file 1547, but you select 1457. Sampling error is also used to describe the fact that auditing a sample will NOT create the exact same answer as auditing every single file or transaction.
What is the ideal sampling frequency?
Not less than double the highest frequency component of the signal you're sampling.
Why is sampling preferred over census?
Less time and less cost for a sample
What is the difference between flat top sampling and natural sampling?
in flat top sampling the electronic circuit required for sampling are less complicated as compared to the one used in natural sampling, at demodulation of the sample it is very difficult to maintain the natural waveform of the natural sampling so flat top sampling can easily be demodulated.
How can you perform a sample selected in such a way that each member of the population has an equal probability of being included?
The short answer is "random sample," but that, unfortunately, is neither specific nor complete. It is not specific because there are forms of random sampling where selection probabilities are not constant. It is not complete because there are many different ways to conduct random sampling with equal selection probabilities. "Simple random sampling" occurs when you can perform a process that, for all practical purposes, behaves like writing down the identifier of each population member on a piece of paper, putting all the pieces into a box, mixing them thoroughly, and pulling out a few of them one by one (without replacing them in the box). Nowadays we use a computer to do this job, because it's faster and more reliable (it is notoriously difficult to mix pieces of paper perfectly randomly). The computer needs a complete list of all the population members: this is called a <i>sampling frame</i>. Here is an example of random sampling that is not simple but still selects every population member with equal probability. Suppose you want to sample half the students in a classroom of 30. Ask them to line up. Flip a fair coin: if it's heads, pick the first, third, ..., 29th in line. If tails, pick the second, fourth, ..., 30th. Any individual student has a 50% chance of being part of the sample, so each student has an equal probability of being included. However, if you lined up the students boy-girl-boy-girl, etc., the samples themselves wouldn't look very random: they will either be mostly boys or mostly girls. It's still random though, because it's determined by the flip of a coin. The example highlights a subtle but important property of a random sample: in many cases, you want the selection of population members to be <b>independent</b>. This means the probability of selecting one member is not affected by which other members are selected. In simple random sampling, independence holds; in the second example (a form of <i>gridded sampling</i>), there is complete dependence: no student can be chosen along with either of their neighbors in line, for instance. Simple random sampling is ideal for many purposes but often cannot be carried out in practice because it is not feasible (you might not be able to construct a sampling frame) or costs too much. Often, more complicated procedures, such as <i>hierarchical sampling</i>, are carried out to overcome these limitations. (An example of hierarchical sampling is when an epidemiologist selects a city at random, then selects households at random within the city, then selects children at random within each household to study. Doing it this way can require much less travel than selecting children at random from all over the state.) These procedures might or might not select population members with equal probability. Usually the selection is not independent, either. When the probabilities are unequal, they can be figured out and used as <i>weights</i> in statistical analysis of the data. Results can also be adjusted for lack of independence. A good, readable, non-technical introduction to sampling and simple random samples is the textbook <i>Statistics</i> by Freedman, Pisani, and Purves. Any edition is fine. Steven Thompson's book <i>Sampling</i> discusses dozens of different sampling procedures and explains the theory behind each one.
What are four kinds of sampling techniques?
Four sampling techniques are:1) Simple Random SamplingThis is the ideal choice as it is a 'perfect' random method. Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection.This method is ideal, but if it cannot be adopted, one of the following alternatives may be chosen if any shortfall in accuracy.2) Systematic SamplingSystematic sampling is a frequently used variant of simple random sampling. When performing systematic sampling, every kth element from the list is selected (this is referred to as the sample interval) from a randomly selected starting point. For example, if we have a listed population of 6000 members and wish to draw a sample of 2000, we would select every 30th (6000 divided by 200) person from the list. In practice, we would randomly select a number between 1 and 30 to act as our starting point.The one potential problem with this method of sampling concerns the arrangement of elements in the list.? If the list is arranged in any kind of order e.g. if every 30th house is smaller than the others from which the sample is being recruited, there is a possibility that the sample produced could be seriously biased.3) Stratified SamplingStratified sampling is a variant on simple random and systematic methods and is used when there are a number of distinct subgroups, within each of which it is required that there is full representation. A stratified sample is constructed by classifying the population in sub-populations (or strata), base on some well-known characteristics of the population, such as age, gender or socio-economic status. The selection of elements is then made separately from within each strata, usually by random or systematic sampling methods.Stratified sampling methods also come in two types - proportionate and disproportionate.In proportionate sampling, the strata sample sizes are made proportional to the strata population sizes.For example if the first strata is made up of males, then as there are around 50% of males in the UK population, the male strata will need to represent around 50% of the total sample. In disproportionate methods, the strata are not sampled according to the population sizes, but higher proportions are selected from some groups and not others. This technique is typically used in a number of distinct situations:The costs of collecting data may differ from subgroup to subgroup.We might require more cases in some groups if estimations of populations values are likely to be harder to make i.e. the larger the sample size (up to certain limits), the more accurate any estimations are likely to be.We expect different response rates from different groups of people. Therefore, the less co-operative groups might be 'over-sampled' to compensate.4) Cluster or Multi-stage SamplingCluster sampling is a frequently-used, and usually more practical, random sampling method. It is particularly useful in situations for which no list of the elements within a population is available and therefore cannot be selected directly. As this form of sampling is conducted by randomly selecting subgroups of the population, possibly in several stages, it should produce results equivalent to a simple random sample.The sample is generally done by first sampling at the higher level(s) e.g. randomly sampled countries, then sampling from subsequent levels in turn e.g. within the selected countries sample counties, then within these postcodes, the within these households, until the final stage is reached, at which point the sampling is done in a simple random manner e.g. sampling people within the selected households. The 'levels' in question are defined by subgroups into which it is appropriate to subdivide your population.Cluster samples are generally used if:- No list of the population exists.- Well-defined clusters, which will often be geographic areas exist.- A reasonable estimate of the number of elements in each level of clustering can be made.- Often the total sample size must be fairly large to enable cluster sampling to be used effectively.Non-probability Sampling MethodsNon-probability sampling procedures are much less desirable, as they will almost certainly contain sampling biases. Unfortunately, in some circumstances such methods are unavoidable. In a Market Research context, the most frequently-adopted form of non-probability sampling is known as quota sampling.? In some ways this is similar to cluster sampling in that it requires the definition of key subgroups. The main difference lies in the fact that quotas (i.e. the amount of people to be surveyed) within subgroups are set beforehand (e.g. 25% 16-24 yr olds, 30% 25-34 yr olds, 20% 35-55 yr olds, and 25% 56+ yr olds) usually proportions are set to match known population distributions. Interviewers then select respondents according to these criteria rather than at random. The subjective nature of this selection means that only about a proportion of the population has a chance of being selected in a typical quota sampling strategy.If you are forced into using a non-random method, you must be extremely careful when drawing conclusions. You should always be honest about the sampling technique used and that a non-random approach will probably mean that biases are present within the data. In order to convert the sample to be representative of the true population, you may want to use weighting techniques.The importance of sampling should not be underestimated, as it determines to whom the results of your research will be applicable. It is important, therefore to give full consideration to the sampling strategy to be used and to select the most appropriate. Your most important consideration should be whether you could adopt a simple random sample.? If not, could one of the other random methods be used? Only when you have no choice should a non-random method be used.All to often, researchers succumb to the temptation of generalising their results to a much broader range of people than those from whom the data was originally gathered. This is poor practice and you should always aim to adopt an appropriate sampling technique. The key is not to guess, but take some advice.
