Sampling and Survey Methods

Sampling and Survey Methods
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Institutional Affiliation
Sampling and Survey Methods
Part 1: Review
Population
A research population is usually a large group of people or items, which are the primary focus of systematic explorations. Researchers typically undertake studies for the benefit of the society. At times, research populations comprise of a distinct collection of items or objects characterized by familiar facets. For individuals or subjects to qualify as a research population, they must have shared or binding traits. Populations are usually of large sizes and researchers cannot examine every person as the process can be time-consuming and expensive. It is for this reason that they rely on sampling techniques to identify the most appropriate population.
Random Assignment
It is an experimental approach commonly applied in research psychology to study human behavior. Research psychologists rely on this technique to allocate subjects to different clusters in a test. Assigning participants randomly plays a critical role in the eradication of confounding variables that could cause a variation in the dependent variable.
Simple Random Sampling
The selection strategy that is at times denoted as random sampling forms part of the purest and most upfront probability sampling method. It is popularly applied in the selection of a sample within a specific population for an extensive variety of tenacities (Alvi, 2016). With this strategy, the likelihood of choice is quite high for each member of the population.

The main advantages associated with this method is its capability to eliminate bias and result in representative samples.
Cluster Sampling
As opposed to choosing all subjects from the entire population, researchers undertake a myriad of steps in gathering the sample population. The strategy usually encompasses the identification of a group of participants who are representative of the research population. Cluster sampling is quite popular in marketing explorations. Its main benefits revolve around cost reduction and increased levels of effectiveness (Alvi, 2016). In cluster sampling, researchers perceive identified groups as sampling units.
Stratified Random Sampling
When analyzing a group of entities with similar features, researchers are often confronted with population sizes that are too large. For them to save money and time, they may apply a more feasible strategy by choosing a small group from the entire population. For instance, they may divide a population into smaller groups in an approach commonly dented as stratified random sampling. In this method that is at times referred to as stratification, researchers select participants based on shared attributes.
Convenience and Haphazard Sampling (Non-Probability Sampling)
It is a non-probability selection technique whereby researchers choose subjects based on their proximity and convenient accessibility. As opposed to selecting participants that are illustrative of the whole population, researchers choose participants due to their ease of recruitment (Alvi, 2016). It would be epitome to examine the entire population in all forms of research. Nonetheless, this is impractical since the population is just too large. It is for this reason that they rely on techniques such as convenience sampling that are not only fast but also efficient and economical.
Basic Research
This form of research often lacks clear goals and experimenter undertake it to satisfy their curiosity. Its purpose is to gather data regarding the combination of nature and people. Although knowledge of this form of research applies to real matters, it is usually associated with improving human understanding of certain aspects.
Applied Research
Contrary to basic research that satisfies inquisitiveness, applied research purposes of discovering an answer to immediate challenges facing the community or organization. It is usually scientific and involves the development of innovative technologies. It is usually considered as a non-systemic inquiry launched by specific individuals or agencies to address particular issues.
Internal Validity
It is the extent to which researchers can confirm that only the variables under study produced the results of a research process. The drive for most research is to identify the impact of the independent variable on it dependent counterparts. Internal validity allows researchers to eliminate the possibility that external factors cause a change in the dependent variable.
Part 2: Sampling
Q1
Target Population-Walgreens workers
Applied research question
Survey/correlational research design
Simple Random Sampling: Since Walgreens employees are a large population, simple random sampling is the most appropriate strategy for selecting the participants of this research. The approach is not only simple to use but also guarantees the accuracy of representation. As opposed to dividing the population into sub-groups, the researchers will just pick the number of participants from the larger group at random. It is critical to note that with this strategy, each member of the more significant population has an equal likelihood of selection.
If haphazard sampling procedures were used, there would have been a significant impact on the conclusions drawn from the research due to the selection bias. When compared to other selection strategies, the reliability of haphazard sampling is quite low as it in a non-statistical technique. With this method, the researchers would have failed to include the perception of some influential participants since they will be selecting subjects based on their accessibility and convenience.
Q2
Target population-Working Adults
Applied research question
Observational research design
Cluster sampling: Cluster sampling will allow for the selection of participants from multiple cases since the research population is huge. With this technique, the researchers will be able to choose specific workplaces where they will observe the impact of ergonomic working conditions on productivity. What is more, the method is economical, fast, and will allow for the accumulation of large samples.
If the researchers applied haphazard sampling procedures, in this case, there would be problems for both internal and external validity due to discriminatory selection of participants. As opposed to choosing subjects randomly, researchers would have picked participants whom they can access easily. The results would not have been a representative of the entire population. Importantly, apart from the independent variable, other external factors would have changed the dependent variable introducing bias as a result.
Q3
Target population-adults
Applied research question
Survey/correctional research design
Stratified random sampling: Since it involves the division of a population into subgroups and the use of random selection methods to create a test group, it is the most suitable method for this research. It will not only result in the selection of participants’ symbolic of the entire population under investigation but will also minimize selection bias. Stratifying population as the whole before applying random selection strategies will ensure that the chosen participants replicate study population. Importantly, the technique will reduce the overrepresentation or underrepresentation of specific population segments.
I would not be confident with the conclusions drawn with the application of observational research design as it would be impractical for the researcher to get accurate observations with such a large crowd. Furthermore, observational research is more practical with the use of focus group whereby researchers are able to discern the conduct of the selected subjects. With such big crowd whose interest is on their favorite musician, it will be impossible for the researcher to get a precise observation of the selected sample.
Q4
Target population-people with depression
Applied research question
Experimental research design
Quota sampling: This selection procedure is characteristically based on the judgment of the researcher. It is commonly applied when gathering representative information from a particular group. In this case, people with depression is the specific group under exploration. Quota sampling will ensure that selected subjects represent specific features of the population being studied. Mainly, the researchers should apply controlled quota sampling to limit their choice of samples. This selection strategy is not only cost-effective but also highly efficient.
Haphazard sampling procedures would have substantial consequences on the conclusion drawn from the research as they will introduce issues of selection bias besides compromising the validity of the results. In this technique, researchers choose select participants based on their judgment. If the researchers applied haphazard sampling techniques, the validity of the results would be questionable due to issues related to discriminatory sampling and selection biases.
Part 3: Discussion Questions
The Need for Sampling Procedures: Applied versus Basic Research Settings
Population selection techniques are more imperative in applied research settings than in basic exploration as they involve the investigation of individual aspects to find practical solutions to specific situations. Typically, applied research must encompass the participation of subjects affected by the phenomena being investigated. Therefore, for researchers to get conclusive results, they must involve the community. Since engaging the community as a whole in exploration is impractical, sampling enables researchers to select participants who are representative of the entire population. On the contrary, researchers undertake basic research to satisfy their curiosity and add more knowledge to their actual understanding (Bentley, Gulbrandsen, & Kyvik, 2015). It is highly theoretical, and researchers can obtain the required information with techniques such as observation, which do not require sampling.
The development of new ideas and products to help the community is the principal purpose of applied research. It provides vital benefits to the economy of states. Despite the varied objectives of this form of research, its scope is for the present and not the future (Bentley, Gulbrandsen, & Kyvik, 2015). Since it is aimed at helping the community at present, researchers tend to encompass study subjects when conducting applied research. Sampling allows investigators undertaking applied studies to select the most appropriate subjects for the study as it is impossible to involve the entire population.
The Need for Sampling Procedures: Experimental versus non-experimental Contexts
Experimental research is typically associated with sciences such as psychology, medicine, sociology, chemistry, biology, and physics. As opposed to the application of single research design, experimental studies encompass a collection of strategies that utilize both controlled testing and manipulation to understand underlying courses. Typically, researchers manipulate one or more variables to establish their impact on the dependent variable. On the other hand, researchers can neither control nor manipulate the predictor variable. They usually rely on observations, interactions, or interpretations to draw their conclusions. Non-experimental researchers typically depend on case studies, correlations, or surveys. Therefore, they cannot establish an actual cause-and-effect connection.
Since non-experimental research can easily be generalized to a larger population, the need for sampling procedures is low. In an experimental study, the investigators require subjects to establish a cause-and-effect relationship. Typically, this form of research cannot occur without participants. Investigators usually divide participants into two clusters namely the experimental and control groups. Sampling procedures are more important in this case since researchers require techniques that will enable them to most the most appropriate samples that are symbolic of the entire population.

References
Alvi, M. (2016). A manual for selecting sampling techniques in research. Munich Personal RePEc Archive. Retrieved from https://mpra.ub.uni-muenchen.de/70218/1/MPRA_paper_70218.pdfBentley, P., Gulbrandsen, M., & Kyvik, S. (2015). The relationship between basic and applied research in universities. Higher Education, 70(4), 689-709.