What Kinds Of Sampling Designs Would Be Used For The Followi

What Kinds Of Sampling Designs Would Be Used For The Following Cases A

What kinds of sampling designs would be used for the following cases and why? Please, provide an explanation.

1. A study to get a quick idea of the medical acceptability of a new aspirin substitute which cannot be dispensed over the counter without prescription.

2. A study involving a sample of 325 students in a university where 2,000 students are enrolled (both with and without replacement).

3. An investigation of the career salience of professionals in the fields of medicine, engineering, business, and law.

4. The generalizability of the attitudes of blue-collar workers from a sample of 184 to the total population of 350 blue-collar workers in the entire factory of a particular company.

Paper For Above instruction

Choosing the appropriate sampling design is fundamental in conducting effective research, as it directly influences the representativeness of the sample and the validity of the study's conclusions. Each of the scenarios presented requires a specific sampling approach based on the study's objectives, population characteristics, and resource constraints. This paper explores suitable sampling methods for each case, providing rationale grounded in statistical principles and practical considerations.

Case 1: Quick Assessment of a New Aspirin Substitute

The primary goal here is to acquire a rapid preliminary understanding of the medical acceptability of a new aspirin substitute that cannot be dispensed without a prescription. Given the urgency and the limited scope, a convenience sampling or judgmental sampling approach may be most appropriate. Convenience sampling involves selecting individuals who are readily accessible and willing to participate, such as patients visiting clinics or hospitals, which expedites data collection (Etikan, Musa, & Alkassim, 2016). Judgmental sampling leverages expert judgment to choose participants likely to provide relevant insights, especially when the population with the specific medical condition or access to the drug is limited or difficult to enumerate.

Alternatively, if an initial pilot study is needed to rapidly gauge acceptability, a purposive sampling approach targeting specific subgroups—such as patients with particular health profiles—may be utilized. These non-probability sampling methods are justified because the main aim is a quick, exploratory insight rather than generalizability. Once preliminary data is obtained, more rigorous probabilistic sampling can be adopted in subsequent phases.

Case 2: Study of a Student Sample from a University

In this scenario, researchers are working with a sampling frame of 2,000 students and selecting a sample of 325. Both with and without replacement options are considered. A simple random sampling (SRS) approach is suitable here, as it allows each student an equal chance of selection, minimizing bias and ensuring that the sample is representative of the student population (Creswell & Creswell, 2017). Whether sampling with or without replacement, SRS ensures the statistical validity needed for inferential analysis, especially if the sample size is sufficiently large relative to the population.

If the aim is to improve representativeness across different demographic or academic groups within the student body, a stratified random sampling could be implemented. For example, students can be stratified by year, major, or gender before randomly sampling within each subgroup. This approach enhances the precision of estimates and allows subgroup-specific insights (Lohr, 2010). Since the population is finite, sampling without replacement avoids duplicate selections, while sampling with replacement can be used in cases of small subsamples or to facilitate theoretical analysis.

Case 3: Career Salience of Professionals in Different Fields

This investigation involves professionals from multiple fields—medicine, engineering, business, and law—and aims to compare their perceptions or attitudes regarding career importance. A suitable sampling design here is a stratified random sampling approach, where professionals are divided into strata based on their fields (Kalton, 1983). This ensures representation from each professional category, facilitating comparisons across groups. Within each stratum, random sampling can be employed to select participants, thus enabling accurate inferences about each group's views and the overall population.

Given the diversity and possible differences in population sizes across these fields, stratified sampling enhances efficiency and accuracy by reducing sampling variability (Lohr, 2010). If the population sizes are unknown or difficult to access, a cluster sampling approach—sampling entire groups or organizations and then surveying all members—may be considered, but stratified sampling is more precise for comparative purposes.

Case 4: Attitudes of Blue-Collar Workers in a Factory

Assessing whether a sample of 184 blue-collar workers can generalize to the entire group of 350 workers involves a probability sampling strategy. A simple random sampling or systematic sampling approach ensures that each worker has an equal chance of selection, thus providing a representative subset (Cochran, 1977). This aids in making valid inferences about the entire factory workforce regarding their attitudes.

Alternatively, a stratified sampling method can be applied if specific worker subgroups—such as shifts, departments, or experience levels—are believed to influence attitudes. Stratification ensures these subgroups are proportionally represented, improving the accuracy and generalizability of results (Lohr, 2010). Given that the total population and the sample size are known, probability sampling ensures the findings are statistically valid and applicable to the entire factory workforce.

Conclusion

Each research scenario warrants a tailored sampling method to optimize data quality and representativeness. Quick exploratory studies benefit from non-probability approaches like convenience or judgmental sampling, suitable for preliminary insights. In contrast, studies aiming for representativeness and valid inferences, especially with finite populations, should utilize probability sampling techniques such as simple random, stratified, or systematic sampling. The choice depends on research objectives, population structure, resource availability, and the level of precision required.

References

• Cochran, W. G. (1977). Sampling Techniques. 3rd edition. John Wiley & Sons.
• Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage publications.
• Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4.
• Kalton, G. (1983). Principles of Stratified Sampling. Journal of the Royal Statistical Society. Series A (General), 146(2), 170–183.
• Lohr, S. L. (2010). Sampling: Design and Analysis. Cengage Learning.
• Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.