NR 449 Week 4 Discussion: Sampling

NR 449 Week 4 Discussion: Sampling

Student Name

Chamberlain University

NR-449 Evidence-Based Practice

Prof. Name:

Date

NR 449 Unit 4: Sampling

Implications of Using Convenience Sampling in Research

Convenience sampling, a widely used non-probability sampling technique, involves selecting participants who are readily accessible to the researcher. While this method offers benefits such as ease of recruitment and cost-effectiveness, it also presents significant drawbacks. One of the primary concerns is the potential for bias. Since the sample is not randomly selected, it may not accurately reflect the broader population, thereby limiting the generalizability of findings (Houser, 2018).

For instance, when convenience sampling is applied in healthcare settings, the results might only be relevant to specific institutions or demographic groups. This could lead to misleading interpretations if the findings are extended to other populations without caution. Additionally, the presence of selection bias—where researchers may consciously or unconsciously choose subjects who confirm their hypotheses—further threatens the validity of the research outcomes (Emerson, 2015).

Practical Example and Limitations

A notable example of convenience sampling’s limitations is found in a study assessing nurses’ perceptions of family violence (FV) screening processes. The research was conducted in a rural healthcare system and involved nurses who were readily available and had completed an online training module. Although the findings suggested that nurses recognized the value of FV screening tools, the study’s convenience sample limited the applicability of its conclusions to more diverse or urban settings (Durham-Pressley, 2018).

This example underscores the importance of recognizing the limitations inherent in convenience sampling and the need for caution when interpreting and applying such research findings.

Considerations for Ethical and Methodological Soundness

Despite its drawbacks, convenience sampling may be appropriate for pilot studies or exploratory research, where resources and time are limited. In such cases, researchers are advised to randomly assign participants to groups after recruitment to minimize bias. For example, in a pilot study evaluating a virtual nursing intervention, convenience sampling was employed due to the limited scope and preliminary nature of the research. Although this approach was justifiable, the researchers acknowledged the inability to generalize the results and called for further research using more representative sampling techniques (Cote et al., 2018).

Ethical considerations are equally crucial. As emphasized by Paavilainen et al. (2014), conducting research involving human subjects demands strict adherence to ethical guidelines, including informed consent, respect for privacy, and minimizing potential harm. When using convenience samples, researchers must ensure these ethical standards are upheld to maintain the integrity of the study and protect participants’ rights.

Summary of Key Points

Overall, while convenience sampling is a practical option for preliminary investigations, its limitations necessitate cautious interpretation and often call for additional studies employing more rigorous sampling strategies to validate the findings.

Understanding Convenience Sampling in Healthcare Research

Convenience sampling, often referred to as availability sampling, is a non-probability sampling method wherein researchers select subjects who are easiest to reach or most readily accessible. This technique, although widely used due to its cost-efficiency and speed, comes with several limitations, primarily its potential for bias and lack of generalizability.

Practical Example in Sickle Cell Research

A clear illustration of convenience sampling is the use of individuals diagnosed with sickle cell disease when studying the effectiveness of hydroxyurea in managing pain. Including participants who do not have the disease would compromise the validity of the findings. If individuals without pain symptoms receive the drug, their positive responses could falsely suggest the medication’s efficacy. Furthermore, exclusion of diverse racial or ethnic groups can introduce cultural or biological biases, particularly if the research is conducted in regions like sub-Saharan Africa. While findings may be relevant locally, they may lack ecological validity in more diverse populations like the United States (Houser, 2018).

Sampling Bias in Pharmacy Settings

Another application of convenience sampling is observed in pharmacy practices. A pharmacy might survey existing patients about their preference for a 90-day supply of medication to reduce workload and improve efficiency. However, such data may not reflect the broader population due to variables like insurance coverage limitations or increased co-payments for extended prescriptions. During events like Hurricane Harvey, the influx of new patients to less-affected pharmacies may also distort findings, further contributing to sampling bias (Houser, 2018).

Bias and Limitations of Convenience Sampling

Bias in convenience sampling arises from overrepresentation or underrepresentation of certain groups. Factors such as socioeconomic status, ethnicity, and geographical proximity can significantly influence outcomes. In one study, a hospital-based sample was used to estimate childhood obesity rates, but the lack of general population representation rendered the findings unreliable (Gilliland et al., 2015).

Comparison to Other Sampling Methods

Researchers often compare convenience sampling with more rigorous methods such as purposive or random sampling. For instance, in studying the effectiveness of flipped classrooms in nursing education, purposive sampling may provide more accurate insights by specifically selecting nursing students across various institutions. Conversely, if convenience sampling is used by surveying only those available after class sessions, results may be biased and non-replicable (Palinkas et al., 2015).

Snowball Sampling as a Complement

Snowball sampling, another non-probability method, can be used to complement convenience sampling. Researchers may initially recruit accessible subjects and then expand the sample by asking participants to refer others. This method is particularly effective in hard-to-reach populations, such as healthcare professionals reporting medication errors or patients with rare diseases (Emerson, 2015; Sheu et al., 2009).

Final Considerations in Healthcare Research

Despite its shortcomings, convenience sampling remains prevalent in healthcare due to limited resources and urgent data needs. Researchers must acknowledge its limitations, remain cautious about generalizing results, and consider integrating other sampling techniques to enhance reliability and validity. Careful attention to inclusion and exclusion criteria, random assignment within the convenience sample, and triangulation with other data sources can mitigate some of the inherent risks.

Understanding Convenience Sampling and Its Implications on Research Validity

Overview of Sampling Methods and Their Impact

Sampling plays a vital role in research design, significantly affecting the reliability and validity of study outcomes. Among various sampling techniques, convenience sampling is frequently used due to its simplicity, speed, and cost-efficiency. However, this approach raises concerns about generalizability and bias.

Convenience sampling, as described by Houser (2018), is a nonprobability sampling method where participants are selected based on their availability and proximity to the researcher. While it is the most commonly employed sampling technique, its limitations in terms of external validity and representation cannot be overlooked (Chamberlain College of Nursing, 2019).

Advantages and Disadvantages of Convenience Sampling

Researchers often choose this method in early stages of research to test tools or procedures (Houser, 2018). For instance, conducting a preliminary test of a new nursing policy at a nearby hospital can provide initial insights without extensive travel or financial burden.

Real-World Application and Bias Concerns

A case example highlighted the drawbacks of this method. A hospital system with facilities in Houston, New Orleans, and Oklahoma implemented a pilot policy only in its Houston branch due to proximity. Since Houston staff were pro-management, their responses were favorable, leading to biased and potentially misleading conclusions (Balingit, 2019). This emphasizes that while convenient, this method may compromise the study’s credibility.

Melissa Castro (2019) agreed, noting that such samples reflect only a subset of the target population, thus reducing the scope of inference. Similarly, Joanne Mae Yabut (2019) cited Emerson (2015) who warned that homogeneity among participants could skew results.

Usefulness in Pilot Studies and Small-Scale Research

Convenience sampling remains appropriate in pilot or feasibility studies. Kimberly Iglesias (2019) and Professor Hobbs (2019) emphasized that its use is acceptable to refine methodologies before conducting large-scale randomized studies. The objective in such cases is to assess the research process rather than generate widely applicable results.

Chona Balingit (2019) later recognized this nuance, revising her initial stance against convenience sampling. She acknowledged its role in early-stage research, especially in evaluating procedures and identifying potential hurdles.

Expert Insight and Criticism

Despite its benefits, scholars caution against overreliance on convenience sampling. According to Bornstein et al. (2017), it introduces selection bias and undermines the study’s credibility and reproducibility. Nonprobability methods such as this fail to capture diverse participant characteristics, reducing external validity.

Olukayode Ogunbanwo (2019) echoed this, stating that data derived from one institution cannot represent an entire state or nation. Such limitations challenge the applicability of the findings across broader contexts.

Contextual Appropriateness and Ethical Considerations

Etikan (2016) compared convenience with purposive sampling, noting that while convenient, the former lacks strategic selection criteria, which could compromise data quality. In clinical settings, researchers might rely on convenience sampling due to urgent timelines or limited access to subjects. However, ethical and methodological transparency is critical.

Felicia Campbell (2019) used convenience samples in studies involving elderly patients in hospitals and nursing homes. These provided concentrated data but required careful interpretation due to location-specific biases.

Summary Table: Perspectives on Convenience Sampling

References

Bornstein, M. H., Jager, J., & Putnick, D. L. (2017). Sampling in developmental science: Situations, shortcomings, solutions, and standards. Developmental Review, 33(4), 357–370.

Chamberlain College of Nursing. (2019). Week 4: Lesson – Considerations for Human Subject Samples. Retrieved from https://chamberlain.instructure.com

Emerson, R. W. (2015). Convenience sampling, random sampling, and snowball sampling: How does sampling affect the validity of research? Journal of Visual Impairment & Blindness, 109(2), 164–168.

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. https://doi.org/10.11648/j.ajtas.20160501.11

NR 449 Week 4 Discussion: Sampling

Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Burlington, MA: Jones & Bartlett Learning.

El-Hneiti, M., Shaheen, A. M., Bani Salameh, A., Al-Hussami, M., & Ahmad, M. (2019). Predictors of nurses’ stress working with older people admitted to acute care setting. International Journal of Older People Nursing, e12222. https://doi.org/10.1111/opn.12222

Emerson, R. W. (2015). Convenience sampling, random sampling, and snowball sampling: How does sampling affect the validity of research? Journal of Visual Impairment & Blindness, 109(2), 164-168. https://doi.org/10.1177/0145482X1510900215

Gilliland, J., Clark, A. F., Kobrzynski, M., & Filler, G. (2015). Convenience Sampling of Children Presenting to Hospital-Based Outpatient Clinics. American Journal of Public Health, 105(7), 1332-1335.

Houser, J. (2018). Nursing research: Reading, using, & creating evidence (4th ed.). Burlington, MA: Jones & Bartlett Learning.

NR 449 Week 4 Discussion: Sampling

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 in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544.

Sheu, S., Wei, I., Chen, C., Yu, S., & Tang, F. (2009). Using snowball sampling method with nurses to understand medication administration errors. Journal of Clinical Nursing, 18(4), 559-569. https://doi.org/10.1111/j.1365-2702.2007.02048.x

Cote, J., Fortin, M., Auger, P., Rouleau, G., Dubois, S., Boudreau, N., Vaillant, I., & Gelinas-Lemay, E. (2018). Web-based tailored intervention to support optimal medication adherence among kidney transplant recipients: Pilot parallel-group randomized controlled trial. JMIR Formative Research. https://doi.org/10.2196/formative.9707

Durham-Pressley, C. (2018). Nurse perceptions of the family violence screening process and education program in a rural healthcare system. Nursing, 48(1), 56. https://doi.org/10.1097/01.NURSE.0000527617.52655.2f

Emerson, R. W. (2015). Convenience Sampling, Random Sampling, and Snowball Sampling: How Does Sampling Affect the Validity of Research? Journal of Visual Impairment & Blindness, 109(2), 164–168.

NR 449 Week 4 Discussion: Sampling

Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Burlington, MA: Jones & Bartlett Learning.

Paavilainen, E., Lepistö, S., & Flinck, A. (2014). Ethical issues in family violence research in healthcare settings. Nursing Ethics, 21(1), 43–52. https://doi.org/10.1177/0969733013486794