NR 449 Week 4 Discussion: Sampling

Student Name

Chamberlain University

NR-449 Evidence-Based Practice

Prof. Name

Date

Sampling

Implications of Using Convenience Sampling in Research

Convenience sampling is a widely used non-probability sampling technique in which participants are selected based on their availability and ease of access. Researchers frequently use this method because it is cost-effective, time-efficient, and practical in various settings. However, convenience sampling comes with notable limitations that can affect the reliability and validity of research findings.

The primary concern with convenience sampling is bias. Because participants are not randomly selected, the sample often fails to represent the larger target population, limiting the generalizability of results (Houser, 2018). For instance, in healthcare research, collecting data exclusively from one hospital or clinic may produce findings that do not accurately reflect conditions in other healthcare environments.

Another concern is selection bias, which occurs when researchers consciously or unconsciously choose participants who may fit their expectations. This can threaten the internal validity of a study and skew results (Emerson, 2015).

Practical Example and Limitations

A study examining nurses’ perceptions of family violence (FV) screening illustrates these issues. Conducted within a rural healthcare system, researchers selected nurses who were accessible and had completed online training. While the study showed that nurses valued FV screening tools, the findings lacked generalizability due to the limited and non-representative sample (Durham-Pressley, 2018).

Table 1 – Study Example Using Convenience Sampling

This example underscores the importance of recognizing the boundaries of convenience sampling and interpreting results with caution.

Considerations for Ethical and Methodological Soundness

Despite its limitations, convenience sampling is valuable in pilot and exploratory studies where resources and time are constrained. Researchers can mitigate bias by applying random assignment after participant recruitment. For example, a pilot study testing a virtual nursing intervention utilized convenience sampling due to practical constraints but clearly acknowledged the limited generalizability of its findings (Cote et al., 2018).

Ethical considerations remain crucial. Researchers must ensure informed consent, protect participant privacy, and minimize potential harm, even when employing convenience samples (Paavilainen et al., 2014).

Table 2 – Summary of Key Considerations

Understanding Convenience Sampling in Healthcare Research

Also called availability sampling, convenience sampling is prevalent in healthcare due to its practicality. Nevertheless, it is often criticized for introducing bias and limiting external validity.

For example, in hydroxyurea treatment studies for sickle cell disease, only patients diagnosed with the condition should be included. Including participants without the disease or excluding racial/ethnic groups beyond sub-Saharan Africa could introduce biases, compromising the applicability of findings (Houser, 2018).

In pharmacy research, convenience sampling can also introduce bias. A study investigating patient preferences for a 90-day medication supply may yield skewed results due to insurance coverage variations and co-payment structures. Similarly, natural disasters like Hurricane Harvey displaced patients, affecting the representativeness of sampled populations (Houser, 2018).

Table 3 – Limitations of Convenience Sampling

Comparison to Other Sampling Methods

Unlike purposive or random sampling, convenience sampling lacks intentional selection. For instance, studying flipped classrooms in nursing education may involve purposive sampling across multiple institutions. In contrast, convenience sampling might only include students present after class, leading to less representative findings (Palinkas et al., 2015).

Snowball Sampling as a Complement

Snowball sampling, where existing participants recruit additional participants, can complement convenience sampling. This approach is especially effective for accessing hard-to-reach populations, such as patients with rare diseases or healthcare professionals reporting medication errors (Emerson, 2015; Sheu et al., 2009).

Final Considerations in Healthcare Research

While convenience sampling is often necessary due to practical constraints in healthcare research, transparency regarding its limitations is essential. Strategies to improve rigor include:

• Applying strict inclusion/exclusion criteria,

• Implementing random assignment within the convenience group,

• Triangulating data from multiple sources.

Understanding Convenience Sampling and Its Implications on Research Validity

Sampling methods significantly influence a study’s reliability and validity. While convenience sampling is practical, it reduces external validity by failing to capture population diversity (Houser, 2018; Chamberlain College of Nursing, 2019).

Table 4 – Advantages and Disadvantages

For example, a hospital piloting a new policy exclusively in one branch in Houston produced favorable results due to staff cooperation. However, outcomes may differ across other branches with different staff attitudes (Balingit, 2019).

Convenience sampling is particularly useful in early research phases for refining methods before larger-scale trials. Its primary function is evaluating processes rather than producing generalizable outcomes (Iglesias, 2019; Hobbs, 2019).

Experts caution against overreliance on convenience samples due to their homogeneity and poor reproducibility. Findings from a single institution should not be generalized to broader populations (Bornstein et al., 2017; Ogunbanwo, 2019).

Ethical rigor and transparency are essential when applying convenience sampling. Unlike purposive sampling, convenience sampling may compromise data richness, so researchers must clearly communicate limitations and ethical safeguards (Etikan, 2016).

Table 5 – Expert 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

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. https://doi.org/10.1177/0145482X1510900215

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.

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

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