Sampling Case Study

SAMPLING
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[1] Define the following terms in your own words
Population
The population refers to all the datasets of people, events or activities in a study.
Sample
A sample is a subset of a population that is representative of the population, and it should be selected to minimize bias as much as possible. The samples help to make conclusions about a population with random sampling best suited for sampling
Bias
Bias relates to the errors resulting from representing the population parameters, and the lower the bias the more reliable the data and results
Design
A design is a conceptual blueprint that a researcher plans what to do, and this includes defining and identifying the population, sample and research method.
Response bias
These are the biases related to responses that fail to capture relevant information by giving inaccurate responses especially for study participants. The study participants would prefer to respond to what is socially acceptable, while the study questions may not be properly structured.
[2] Define and provide an example for each design method
Simple random sampling
The samples are chosen at random with all of them having an equal chance of occurring. The simple random sampling is preferred as it avoids biases, and it is possible to make inferences about the unknown population parameters (Albright, Winston & Zapp, 2013). For instance, determining a lottery winner from a group of lottery tickets, with no predetermined formula is a form of simple random sampling.
Systematic sampling
Samples can be chosen randomly and then selected if they are the nth data item. In order to ensure there is no bias in systemic sampling, the first element should be chosen at random
(Babbie, 2013). For instance, having already selected students randomly, every 10th students is selected to participate in a study to understand their beliefs on the role of spirituality in healthcare.
Stratified sampling
A population is divided into sub-populations (strata) that are non-overlapping, and the selection of different strata is independent of each other (Babbie, 2013). For instance, if the population of students is first divided into male and female diabetic patients and then selected depending on whether specific racial groups including minorities.
Cluster sampling
The populations are first placed into groups, containing various elements, but the groups need not be the same size. Sampling is carried out for the clusters rather than the elements, but only a few clusters are sampled. For instance, annual surveys on drug use in high schools for all students in the 12 the grade, classes with information collected through anonymous questionnaires. The classes are the clusters in the sampling while the students are the elements.
[3] Choose one design method from the list above. Using your example, make a list of 2-3 advantages and 2-3 disadvantages for using the method.
Systematic sampling
Advantages
It is possible to cover more data compared to random sample
The approach is straightforward and easy to use.
Disadvantages
The sampling does not fully represent all the data items as it follows a specific pattern, and there is likely to be bias.
At times there is a need to map the population while including all the data items in a list.
[4]The name of each student in a class is written on a separate card. The cards are placed in a bag. Three names are picked from the bag.
Identify which type of sampling is used and why
This is simple random sampling since each card has an equal chance of being chosen, the basis of chance distinguishes the simple random sampling from other approaches.
[5] A phone company obtains an alphabetical list of names of homeowners in a city. They select every 25th person from the list until a sample of 100 is obtained. They then call these 100 people to advertise their services. Does this sampling plan result in a random sample? What about a simple random sample? Explain why or why not.
The sampling plan does not result in either the simple random sample or random sample. There is a systematic selection of every 25th person among the population of homeowners. In the case of random and simple random sampling every element has an equal chance of being selected. However, this is not the case in the example, and hence this is systematic sampling.
[6] The manager of a company wants to investigate job satisfaction among its employees. One morning after a meeting, she talks to all 25 employees who attended. Does this sampling plan result in a random sa...