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A Population is made up of groups that have wide variations within the groups and less variations from group to group Which is the appropriate type of sampling method?
The question appears to have some confusion. The groups within a populations are not always fixed. A population may be divided in different groups in several alternate ways. It is for the researcher to decide if there is a need to identify these groups separately, and if so, what criteria is to be used for grouping.
However, assuming that there is a need to divide the population in groups, it should be done in such a way that the variation within the group is minimized while that between the group may be high. The question assumes the reverse regarding variation within and between the group.
Having clarified this issue, I will get down to answering the question of appropriate method of sampling for different groups within a population. There are two basic method of sampling - random and systematic. What method is appropriate in a given situation is not really dependent on the nature of groups within a population. Only requirement is that the method should be common for all the groups within the population.
Another aspect of sample selection whether the total sample is to be drawn from the complete population without differentiating between group, or to draw sample from each group separately. The First method is appropriate when differences between the groups is not high, or when it is not necessary to analyse data group wise.
It is best to draw sample form each group separately when, it is important to to do detailed analysis of each group separately, or when there is high variation between groups. When this is the selected method, then the additional question of sample size for each group need to be answered. There are three possible methods for this. First alternative is to take equal number of samples from each group. This method is appropriate when the size of each group is large and variation within groups is comparable, and sample size is decided based on desired level of accuracy and confidence.
Second method is to decide sample size separately for each group based on variation within each group - higher the variation, larger the group. This method is appropriate when the variation within groups differ from group to group. Third method is to have have sample size proportional to the group size. This method is appropriate when size of each group is not very large and variations within groups is not is comparable for different groups.
What else can I help you with?
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. .
Splitting a population into groups with similar characteristics before sampling is called?
stratificatin
Is obtained by dividing the population into groups and selecting all individuals from within a random sample of the groups?
Cluster Sampling
Which sampling is obtained by dividing the population into groups and selecting all individuals from within a random sample of the groups?
It is called one-stage cluster sampling. If random samples are taken within the selected clusters then it is two-stage cluster sampling.
What is primary sampling?
Primary sampling is a research method used by various companies for many different reasons. The primary sampling unit arises in sampling surveys where population elements are grouped, and those groups becomes units in the sample selection.
What type of sampling uses a fair representation of the population?
Stratified sampling is a type of sampling that uses a fair representation of the population by dividing the population into different subgroups or strata and then selecting samples from each stratum in proportion to their size in the population. This method helps ensure that all groups in the population are adequately represented in the final sample.
How many types of random sampling?
There are several types of random sampling, with the most common being simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Simple random sampling gives each member of the population an equal chance of being selected. Stratified sampling involves dividing the population into subgroups and sampling from each subgroup. Cluster sampling selects entire groups or clusters, while systematic sampling involves selecting members at regular intervals from a randomly ordered list.
What is stratified sampling under research methodology?
Stratified sampling is a sampling method in research where the population is divided into subgroups or strata based on certain characteristics. Samples are then selected from each stratum in proportion to the population, to ensure representation of all groups. This method helps to reduce sampling errors and improves the accuracy of the research findings.
What are the advantages of systematic sampling as compared to stratified?
You can't conduct startified sampling if there are no difinative groups, thus systematic sampling is more efficient if your data has no groups.
What are the advantage and disadvantage of sampling techniques?
Sampling techniques in research allow researchers to gather data efficiently and cost-effectively, providing a snapshot of a larger population. This can save time and resources compared to collecting data from an entire population. However, sampling techniques may introduce sampling bias, where certain groups are overrepresented or underrepresented in the sample, leading to results that may not accurately reflect the entire population. It is crucial for researchers to carefully select and implement sampling techniques to minimize bias and ensure the validity and generalizability of their findings.
What is obtained by dividing the population into groups and selecting all individuals from within a random sample of the groups?
This method is known as cluster sampling. In cluster sampling, the entire population is divided into clusters, often based on geographical areas or other natural groupings. Then, a random selection of these clusters is made, and all individuals within the selected clusters are included in the sample. This approach can be more practical and cost-effective than other sampling methods, especially when the population is large and dispersed.
What are the examples of sampling?
1. Simple Random Sampling (SRS) - For SRS, every element has an equal probability of being chosen. In fact, any pair, triplet, and so on of elements have an equal chance of random selection. Sometimes, SRS can have problems because the randomness of the sample does not represent the population. For example, a SRS of one hundred people will likely produce about fifty men and fifty women, but it's also possible that there will only be ten men and ninety women selected due to natural sampling variation. 2. Systematic Sampling - For this type of sampling, every nth element is sampled. For example, if names were to be sampled through systematic sampling, every tenth name would be picked from the telephone book. However, this type of sampling may result in an unrepresentative sample of the population. 3. Stratified Sampling - When a population has certain categories, samples can be purposely collected from each strata (category). For example, there may be different strata for age groups if the person sampling is interested in variations between differences in age. One problem with stratified sampling is that it requires a more expensive cost than simple random sampling or systematic sampling. 4. Convenience Sampling - This type of sampling involves drawing the easiest samples to reach from the population. This may include surveying customers outside of a grocery store. Because the sample is limited to a certain time/day, it is unrepresentative of the entire population.
