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Difference between Cluster and Quota Sampling:-
Transcript of Difference between Cluster and Quota Sampling:-
What is Sampling ?
In many experiments, sampling an entire population as part of a research experiment is impossible, due to the time, expense and sheer number of subjects.
Imagine, for example, an experiment to test the effects of a new education technique on schoolchildren. It would be impossible to select the entire school age population of a country, divide them into groups and perform research.
A research group sampling the diversity of flowers in the Indian subcontinent could not count every single flower, because it would take many years.
This is where sampling comes in, the idea of trying to take a representative section of the population or flora, perform the experiment and extrapolate it back to the population as a whole.
The Advantages of Sampling:-
It involves a smaller amount of subjects, which reduces investment in time and money.
Sampling can actually be more accurate than studying an entire population, because it affords researchers a lot more control over the subjects. Large studies can bury interesting correlations amongst the ‘noise.'
Statistical manipulations are much easier with smaller data sets, and it is easier to avoid human error when inputting and analyzing the data.
The Disadvantages of Sampling:-
There is room for potential bias in the selection of suitable subjects for the research. This may be because the researcher selects subjects that are more likely to give the desired results, or that the subjects tend to select themselves.
Sampling requires a knowledge of statistics, and the entire design of the experiment depends upon the exact sampling method required.
, instead of selecting all the subjects from the entire population right off, the researcher takes several steps in gathering his sample population.
First, the researcher selects groups or clusters, and then from each cluster, the researcher selects the individual subjects by either simple random or systematic random sampling. The researcher can even opt to include the entire cluster and not just a subset from it.
The most common cluster used in research is a
, a researcher wants to survey academic performance of high school students in India.
He can divide the entire population (population of India) into different clusters (cities).
Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling.
Then, from the selected clusters (randomly selected cities) the researcher can either include all the secondary students as subjects or he can select a number of subjects from each cluster through simple or systematic random sampling.
The important thing to remember about this sampling technique is to give all the clusters equal chances of being selected.
Cluster Sampling (continued)...
Types of Cluster Sample
One-Stage Cluster Sample
Recall the example given in the previous slide; one-stage cluster sample occurs when the researcher includes all the secondary students from all the randomly selected clusters as sample.
Two-Stage Cluster Sample
From the same example MENTIONED BEFORE, two-stage cluster sample is obtained when the researcher only selects a number of students from each cluster by using simple or systematic random sampling.
This sampling technique is cheap, quick and easy. Instead of sampling an entire country when using simple random sampling, the researcher can allocate his limited resources to the few randomly selected clusters or areas when using cluster samples.
Related to the first advantage, the researcher can also increase his sample size with this technique. Considering that the researcher will only have to take the sample from a number of areas or clusters, he can then select more subjects since they are more accessible.
From all the different type of probability sampling, this technique is the least representative of the population. The tendency of individuals within a cluster is to have similar characteristics and with a cluster sample, there is a chance that the researcher can have an overrepresented or underrepresented cluster which can skew the results of the study.
This is also a probability sampling technique with a possibility of high sampling error. This is brought by the limited clusters included in the sample leaving off a significant proportion of the population unsampled.
Cluster Sampling (continued)...
is a non-probability sampling technique wherein the assembled sample has the same proportions of individuals as the entire population
with respect to known characteristics, traits or focused phenomenon. In addition to this, the researcher must make sure that the composition of the final sample
to be used in the study meets the research's quota criteria.
Step-by-step Quota Sampling
The first step in
quota sampling is to divide the population into exclusive subgroups.
Then, the researcher must identify the proportions of these subgroups in the population; this same proportion will be applied in the sampling process.
Finally, the researcher selects subjects from the various subgroups while taking into consideration the proportions noted in the previous step.
The final step ensures that the sample is representative of the entire population. It also allows the researcher to study traits and characteristics that are noted for each subgroup.
Example of Quota Sampling
In a study wherein the researcher likes to compare the academic performance of the different secondary class levels, its relationship with gender and socioeconomic status, the researcher first identifies the subgroups.
Usually, the subgroups are the characteristics or variables of the study. The researcher divides the entire population into class levels, intersected with gender and socioeconomic status. Then, he takes note of the proportions of these subgroups in the entire population and then samples each subgroup accordingly.
QUOTA Sampling (continued)...
When to Use Quota Sampling ?
The main reason why researchers choose quota samples is that it allows the researchers to sample a subgroup that is of great interest to the study. If a study aims to investigate a trait or a characteristic of a certain subgroup, this type of sampling is the ideal technique.
Quota sampling also allows the researchers to observe relationships between subgroups. In some studies, traits of a certain subgroup interact with other traits of another subgroup. In such cases, it is also necessary for the researcher to use this type of sampling technique.
Disadvantages of Quota Sampling :-
It may appear that this type of sampling technique is totally representative of the population. In some cases it is not. Keep in mind that only the selected traits of the population were taken into account in forming the subgroups.
In the process of sampling these subgroups, other traits in the sample may be overrepresented. In a study that considers gender, socioeconomic status and religion as the basis of the subgroups, the final sample may have skewed representation of age, race, educational attainment, marital status and a lot more.
QUOTA Sampling (continued)...
• You have a complete sampling frame. You have contact information for the entire population.
• You can select a random sample from your population. Since all persons (or “units”) have an equal chance of being selected for your survey, you can randomly select participants without missing entire portions of your audience.
• You can generalize your results from a random sample. With this data collection method and a decent response rate, you can extrapolate your results to the entire population.
• Can be more expensive and time-consuming than convenience or purposive sampling.
• Used when there isn’t an exhaustive population list available. Some units are unable to be selected, therefore you have no way of knowing the size and effect of sampling error (missed persons, unequal representation, etc.).
• In quota sampling, the selection of the sample is Not random.
• Can be effective when trying to generate ideas and getting feedback, but you cannot generalize your results to an entire population with a high level of confidence. Quota samples (males and females, etc.) are an example.
•More convenient and less costly, but doesn’t hold up to expectations of probability theory.