How Will You Have Access To The Population To Be Sampled?

How Will You Have Access To The Population To Be Sampled Or Interviewe

How will you have access to the population to be sampled or interviewed? The population that we will be sampling will be a group of current employees at the Ritz-Carlton. Sampling is used because of the cost factor; there are greater accuracy of results, greater speed of data collection, and the availability of population elements. Steps in Sampling Design include identifying the target population, parameters of interest, sampling frame, appropriate sampling method, and size of the sample needed.

We will be preparing questions to discuss during a focus group session, a survey, and then conducting individual interviews. Our sampling method will be non-probability sampling, purposive sampling. We will choose specific employees at the Ritz-Carlton that conform to certain criteria. Not every member of the population will have a chance of being included, because we will select members based on their function within the Ritz-Carlton.

The sampling frame defines the members of the population who are eligible for the sample, in this case, current Ritz-Carlton employees involved in the new process, such as hiring managers, recruiters, or HR personnel who have worked for Ritz-Carlton for at least two years. The sample size will be influenced by principles of variation, desired precision, error margin, confidence level, and subgroup representation. Since our research has limited objectives, we don't need the sample to reflect the entire population, and we aim for quick results without the need for probability sampling.

Qualitative methods will include analysis of focus group discussions, recruiting and hiring reports provided by Ritz-Carlton, and individual interviews, chosen because current employees possess valuable insights into attracting quality candidates aligned with the company's culture.

To analyze the data, we will use cross-tabulation to explore relationships between variables. Statistical testing will primarily involve non-parametric tests such as chi-square (_2 ) tests for nominal data and other appropriate significance tests, considering the measurement scales and data independence.

Results and insights will be displayed through case studies, frequency tables, bar charts, and pie charts, allowing clear visualization of findings. When testing hypotheses, the decision rule is to reject the null hypothesis if statistical significance is found, or to fail to reject it if no significance is observed. We recognize that a null hypothesis can never be conclusively proved; therefore, conclusions will be based on whether we reject or fail to reject the null hypothesis, understanding the associated risks of Type I and Type II errors.

Paper For Above instruction

The process of accessing the population for sampling or interviews is a critical initial step in research design, particularly when investigating specific organizational phenomena such as hiring practices at a luxury hotel chain like the Ritz-Carlton. To ensure effective data collection, researchers must consider how to access the target population, which in this context comprises current employees involved in aspects of recruitment, HR, and management related to the new hiring process. This access can be facilitated through organizational cooperation, internal communication channels, and formal permissions, which are essential for ethical and efficient data collection.

Sampling strategies significantly influence the quality and applicability of research findings. In this case, a purposive sampling method will be adopted, selecting employees based on their roles, experience, and relevance to the research objectives. This non-probability sampling approach is justified due to the need for specific insights from particular subsets of employees, such as hiring managers or HR personnel with at least two years of tenure, who are directly involved in the relevant processes. Such criteria help ensure the collected data is rich and pertinent to the study.

The sampling frame constitutes the operational boundary for selecting participants. Here, it includes all current Ritz-Carlton employees who are engaged in the hiring process, have a minimum of two years’ experience, and can provide valuable qualitative insights. The frame ensures a focused and meaningful sample, streamlining recruitment and data collection efforts. Because the research is exploratory and aims for rapid insights rather than statistically representative results, the sample size will be relatively small, guided by principles balancing variability, desired accuracy, and constraints such as time and resources.

Determining the appropriate sample size requires consideration of several factors: the population variance, the required confidence level, the acceptable margin of error, and the scope of subgroup analyses. Given the qualitative emphasis of this study, a smaller but purposively diverse sample will suffice. The sample size will be dictated less by statistical formulae and more by practical thresholds—enough participants to reach data saturation and yield meaningful insights.

Qualitative methods are central to this research, given their capacity to extract nuanced understanding. Focus groups will facilitate collective discussion on recruitment challenges and perceptions, while individual interviews will provide depth into personal experiences and opinions. Additionally, analysis of recruitment and hiring reports will supply contextual data, supporting triangulation and enhancing validity. These methods are appropriate because they leverage internal knowledge from employees familiar with organizational processes, capturing rich, detailed information difficult to quantify.

Analytical procedures will primarily involve cross-tabulation, allowing researchers to examine relationships between categorical variables such as employee roles, perceptions, and attitudes toward recruitment practices. Statistical tests suited for nominal and ordinal data—such as chi-square (_2 ) tests—will be employed to evaluate the significance of associations. The choice of tests depends on the data’s measurement scales, independence, and distribution characteristics. For example, chi-square tests are widely used for nominal data and help determine whether observed distributions differ from expected distributions under the null hypothesis.

Results and insights will be communicated through visual representations such as case studies, frequency tables, bar charts, and pie charts. These clear and accessible formats facilitate understanding among stakeholders and support evidence-based decision-making. Interpretation of statistical tests will follow the standard significance testing protocol. If the null hypothesis is rejected, it suggests a statistically significant relationship exists between variables, warranting further investigation or action. Conversely, failing to reject the null indicates insufficient evidence to support an association, but does not prove independence or the absence of relationship.

Finally, the conclusions drawn from hypothesis testing must acknowledge the inherent limitations of statistical inference. Rejecting or failing to reject the null hypothesis depends on predefined significance levels and the sample data. Errors—Type I (false positive) or Type II (false negative)—are always possible, and researchers should interpret results within the context of these potential inaccuracies. Ultimately, the aim is to derive actionable insights that inform recruitment strategies and organizational practices at the Ritz-Carlton, grounded in robust qualitative and quantitative evidence.

References

• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
• Fowler, F. J. (2013). Survey Research Methods. Sage Publications.
• Hair, J. F., Black, W. C., Babin, B., & Andersen, R. E. (2010). Multivariate Data Analysis. Pearson Education.
• Kelley, K., Clark, B., Brown, V., & Sitzia, J. (2003). Good practice in the conduct of marketing research: issues, strategies, and methods. Marketing Intelligence & Planning, 21(4), 174–184.
• Patton, M. Q. (2002). Qualitative Research & Evaluation Methods. Sage Publications.
• Salant, P., & Dillman, D. A. (1994). How to Conduct Your Own Survey. Wiley.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson Education.
• Yin, R. K. (2014). Case Study Research: Design and Methods. Sage Publications.
• Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2010). Business Research Methods. Cengage Learning.