How Do The Sampling Methods And Sample Sizes Vary For The Di

How Do The Sampling Methods And Sample Sizes Vary For The Different Ty

How do the sampling methods and sample sizes vary for the different types of quantitative and qualitative research? Hypothetically, what sampling method would you use to conduct a study of your selected evidence-based practice problem/topic? Why? How would you determine the sample type and size? Responses need to address all components of the question, demonstrate critical thinking and analysis, and include peer reviewed journal evidence to support the student’s position. Please be sure to validate your opinions and ideas with citations and references in APA format. The initial discussion response should be in the range of words.

Paper For Above instruction

The methodologies used in research studies are significantly influenced by the type of research design—be it quantitative or qualitative—as well as the specific objectives, population characteristics, and resource availability. Sampling methods and sample sizes are critical components that determine the validity, reliability, and generalizability of research findings. Understanding how these elements vary across different research paradigms is essential for conducting rigorous research, particularly when exploring evidence-based practices in healthcare.

Differences Between Quantitative and Qualitative Sampling Methods and Sample Sizes

Quantitative research aims to test hypotheses, measure variables, and generalize findings to larger populations (Creswell & Creswell, 2017). Such studies typically employ probability sampling methods like simple random sampling, stratified sampling, or cluster sampling. These techniques facilitate the selection of representative samples, allowing for statistical inferences. Sample sizes in quantitative studies are generally larger, determined by statistical power analysis to ensure adequate sensitivity to detect meaningful effects (Punch, 2014). Larger samples reduce sampling error and enhance the external validity of the findings, often ranging from hundreds to thousands of participants depending on the population size and research design.

Conversely, qualitative research seeks to understand phenomena in depth, often exploring perceptions, experiences, or complex social processes (Merriam & Tisdell, 2015). Non-probability sampling methods such as purposive, convenience, or snowball sampling are common, focusing on selecting information-rich cases that can provide detailed insights. Sample sizes are usually smaller, often ranging from 10 to 50 participants, dictated by the concept of data saturation—the point at which no new themes emerge (Guest, Bunce, & Johnson, 2006). The emphasis here is on depth rather than breadth, and the sample size is flexible, adjusted as the data collection progresses.

Hypothetical Sampling Method for a Selected Evidence-Based Practice Problem

Suppose I aim to study nurses' perceptions of implementing a new pain management protocol in a hospital setting—a healthcare improvement project. Given the nature of this inquiry, a purposive sampling method would be appropriate. This approach allows the researcher to select nurses with direct experience or specific knowledge about the protocol, ensuring rich, relevant data (Palinkas et al., 2015). Purposive sampling aligns with qualitative research principles, focusing on depth and understanding within a specific context.

Determining Sample Type and Size

The sample size would depend on reaching data saturation—when additional interviews no longer yield new insights. Based on prior qualitative research in healthcare settings, a sample of 15-20 nurses may suffice (Madill, Jordan, & Shirley, 2000). To enhance trustworthiness, I would ensure diversity in nursing roles, experience levels, and shifts to capture varied perspectives. Recruitment would continue until thematic redundancy is achieved, balancing resource constraints and research rigor.

Critical Thinking and Evidence-Based Justifications

Selecting purposive sampling allows for targeted exploration of nurses’ perceptions, which is critical in understanding barriers and facilitators to protocol implementation (Polit & Beck, 2017). This method ensures data richness, which is essential for developing tailored interventions. Determining sample size based on saturation rather than predetermined numbers aligns with qualitative standards, optimizing resource use and ethical considerations (Fusch & Ness, 2015). Employing peer-reviewed evidence to justify sampling choices enhances the study's credibility and methodological soundness.

References

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Fusch, P. I., & Ness, L. R. (2015). Are we there yet? Data saturation in qualitative research. The Qualitative Report, 20(9), 1408–1416.

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82.

Madill, A., Jordan, A., & Shirley, C. (2000). Objectivity and reliability in qualitative analysis: Realist, contextualist, and radical constructionist epistemologies. Psychology and Health, 15(3), 387–405.

Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544.

Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Wolters Kluwer.

Punch, K. F. (2014). Introduction to social research: Quantitative and qualitative approaches (3rd ed.). SAGE Publications.