Developing a Plan for Sampling and Data Collection

Plan for Sampling and Data Collection Student’s Name Institution Course Code: Course Name Professor’s Name Submission Date Introduction Workplace issues involving nurses should be assessed and addressed to ensure optimum working conditions and improved health outcomes. Researchers on healthcare issues must employ the best techniques to meet research objectives and positively impact care delivery. Sampling and data collection are key steps in any research project, and all prior preparations are targeted towards it. The context of this research is a community urban hospital whose nurses have reported poor working place conditions that affect their performance. This paper identifies the target population, defends the chosen sampling technique, and explains the necessary steps in executing the sampling procedure for data collection. Step 1. Identify The target population will include licensed and registered practical nurses in a suburban community hospital. These practitioners are essential in healthcare provision and in improving the health outcomes of patients. The participants will be a varied population based on their years of experience and the units they work in, and their shifts will also be considered. Data from these nurses will be retrieved to ascertain how environmental affordances, like the hospital's physical environment, noise, lighting, and equipment placement, relate to their performance in executing their jobs efficiently. In this light, the study will seek to understand how these variables impact their job satisfaction and execution of work, insights that are derived from Boudreau and Rhéaume’s (2024) study. Data will be collected on objective measures such as work efficiency and subjective perceptions. The selected population directly targeted by the physical and organizational design of the hospital is thus one of the targets of this study. Step 2. Defend The probability sampling technique will be most appropriate for this study. Probability sampling will guarantee equal chances of selection for every nurse in the suburban community hospital. Among the various probability sampling methods, stratified sampling is the most relevant in this study. Since there is much variation in the work setting of the nurses, stratified sampling would allow the researcher to ensure proper representation of the critical subgroups among the nursing population. Stratified sampling will also enhance the precision of the research outcome by reducing sampling error, as the samples obtained will be proportionally representative of every subgroup for more valid comparisons (Huo et al., 202...