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Is cluster sampling is a type of stratified random sampling?
No. Cluster sampling and stratifed random sampling are different, though often confused. (They may, however, be used in conjunction in some sampling designs.) Both are types of random sampling.STRATIFIED sampling involves identifying a variable that will break up your population into separate homogeneous groups (homogeneous in terms of the variable you are interested in). For example, suppose you want to know about the attitudes of kids about their future. Perhaps you have reason to believe this will change with time. If you collected a sample from high schools, you could stratify by grade, giving you 4 relatively homogeneous groups: freshmen, sophomores, juniors, seniors. Then, a common approach is to sample a similar number from each group.Sometimes, though, separating the groups isn't so clear cut. Perhaps you want to stratify based on religion. You can't tell this from looking at a person. So perhaps you collect sample data and apply strata after the fact! This can be useful, but there are some statistical techniques that require equal (or nearly) sample sizes for the strata.CLUSTER sampling involves breaking your population into fairly similarly sized groups called clusters (try googling MSE for an example). But now you want each cluster to contain a heterogeneous mix of individuals. Then, you take a random selection of these clusters and completely enumerate inside of those selected clusters. The problem with cluster sampling is that the cluster has now become your sample unit, instead of individuals which is what you probably hoped. This can be used for counting species, or just for contacting certain populations like apartment dwellers, nursing home residents, etc. The clusters could be apartment buildings in a city. So instead of taking a random sample of apartment dwellers, you would actually randomly select a few of the buildings and talk to everyone inside! Often, this is much more cost efficient. :)
A sampling error could occur from?
a poorly designed hypothesis
What problems could prevent truly random sampling?
YES
How do you solve simple random sampling?
Put up a no trespassing sign. Could you be (a lot) more specific?
When is non probability sampling appropriate?
In some situations stratified random sampling may be more appropriate. You may have a population which can be divided up into a number of subsets (strata) such that the difference between units in different strata is much greater than the difference between units within each stratum. A probability sample may not have enough units from some of the smaller strata. A stratified random sample will ensure that each stratum is represented proportionally. In other situations, cluster sampling may be more appropriate. Suppose you wish to visit a sample 1% of all schools in the country. If you were to choose the schools by probability sampling they would be all over the country and you would require a huge amount of time and money to visit them all. What you could do, instead, is to divide up the country into 1000 regions. Select 10 of these regions (1%) and then visit every school in the selected regions. Far less running around!
When is sampling neccessary?
Sampling is necessary in a few places. It could be when eating, painting and building.
Could the value of the sampling error be zero?
Yes, it could.
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.
Is cluster sampling is a type of stratified random sampling?
No. Cluster sampling and stratifed random sampling are different, though often confused. (They may, however, be used in conjunction in some sampling designs.) Both are types of random sampling.STRATIFIED sampling involves identifying a variable that will break up your population into separate homogeneous groups (homogeneous in terms of the variable you are interested in). For example, suppose you want to know about the attitudes of kids about their future. Perhaps you have reason to believe this will change with time. If you collected a sample from high schools, you could stratify by grade, giving you 4 relatively homogeneous groups: freshmen, sophomores, juniors, seniors. Then, a common approach is to sample a similar number from each group.Sometimes, though, separating the groups isn't so clear cut. Perhaps you want to stratify based on religion. You can't tell this from looking at a person. So perhaps you collect sample data and apply strata after the fact! This can be useful, but there are some statistical techniques that require equal (or nearly) sample sizes for the strata.CLUSTER sampling involves breaking your population into fairly similarly sized groups called clusters (try googling MSE for an example). But now you want each cluster to contain a heterogeneous mix of individuals. Then, you take a random selection of these clusters and completely enumerate inside of those selected clusters. The problem with cluster sampling is that the cluster has now become your sample unit, instead of individuals which is what you probably hoped. This can be used for counting species, or just for contacting certain populations like apartment dwellers, nursing home residents, etc. The clusters could be apartment buildings in a city. So instead of taking a random sample of apartment dwellers, you would actually randomly select a few of the buildings and talk to everyone inside! Often, this is much more cost efficient. :)
A sampling error could occur from?
a poorly designed hypothesis
What problems could prevent truly random sampling?
YES
What is the meant by snowball sampling cluster sampling?
Snowball sampling is often used when interviewing. Instead randomly asking people about a particular topic, you would interview initially a person thought to be knowledgable about a subject and then ask them to identify additional people who could serve as usefull interviewees. You then interview those people and ask them to suggest even more people. Thus, your pool of interviewees increases over time, something akin to making a big snowball where it slowly groes as you add more snow.
How do you combine probability to non probability sampling?
There are many methods: stratified random sampling and cluster sampling are two examples. Suppose you have a school with 1000 pupil: 400 in the Junior school and 600 in the Senior school. You may wish to keep the sampling proportions in the two parts of the school the same. So if you wanted a sample of 5% = 50 pupils, you would take a probability or random sample of 5% = 20 from the Juniors and 5% = 30 from the Seniors. For the second example, imagine you want to sample 5% of all schools in the country. This could result in you spending lots of time and money travelling from place to place. Instead, you divide up the country into 100 regions - each containing the same number of schools. You then take a probability sample of 5% of these regions. In each of the regions you visit every school.
Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with a criminal justice e?
Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with an information technology example.
What negative effects could occur if you used a quantitative sampling method for a qualitative study and vice versa?
Using a quantitative sampling method in a qualitative study could result in a lack of in-depth understanding of participants' experiences and perspectives. On the other hand, using a qualitative sampling method in a quantitative study could introduce bias and limit the generalizability of the findings.
Difference between restricted and unrestricted sampling?
Difference between restricted sampling and unresticted sampling
What is the difference between sampling frame and sampling uinit?
Samplig frame is the source material from which the sample is drawn. If you have a 'list' of names of all inviduals from which you could draw a sample, the list is a sampling frame. A samplig unit is the sample being chosen.
