Sample size calculation poses a persistent challenge for researchers in the social sciences, particularly when conducting survey research. The quest for an appropriate sample size often perplexes young scholars and those embarking on research endeavors. In this tutorial, we will explore essential considerations and guidelines to help scholars estimate an optimal sample size for survey research.
Nature of Research and Statistical Analysis
Understanding the nature of your research is paramount. For instance, conducting research on top executives in organizations demands a tailored approach. The sample size will inevitably vary based on the targeted individuals or entities. Moreover, the type of statistical analysis employed plays a crucial role. Exploratory factor analysis requires a minimum of 50 observations, while simple regression analysis necessitates at least 100 samples in most scenarios.
Choice of Analysis Program
The choice of analysis program, such as AMOS, Mplus, LISREL, or SmartPLS, can influence the required sample size. It is imperative to dispel the misconception that certain tools, like PLS SEM, possess magical capabilities for handling small sample sizes. Understanding the nuances of your chosen analysis program is crucial to ensure accurate results.
Supervisory Influence
Research supervisors often advocate for larger sample sizes to enhance generalizability. However, scholars should exercise caution, as excessively large sample sizes may lead to statistically significant yet unreliable results, introducing the risk of type one errors. It is vital to align the sample size with the study’s objectives and analytical techniques.
Existing Rules and Guidelines
Several established rules and guidelines aid in determining sample sizes. The sample-to-item ratio, often recommended for exploratory factor analysis, suggests a ratio not less than 5:1. Additionally, sample-to-variable ratios, Krejcie and Morgan tables, and various online calculators provide valuable insights. Researchers must be mindful of the assumptions inherent in these approaches.
Guidelines for PLS SEM
For Partial Least Squares Structural Equation Modeling (PLS SEM), the conventional “10 times rule” suggests the minimum sample size should be ten times the larger number of formative indicators. However, recent alternatives like the inverse square root method and gamma exponential method offer more nuanced approaches, considering factors like effect size and measurement item reliability.
Additional Recommendations and Considerations
Several other considerations, such as Roscoe’s 1975 guidelines, Klein’s recommendations, multi-level models, and G Power analysis, offer diverse perspectives on sample size determination. Klein suggests that a sample size between 100 and 200 is medium, over 200 is large, and less than 100 is small, with due consideration for model complexity.
Conclusion
In conclusion, determining the appropriate sample size for survey research involves a nuanced consideration of the research’s nature, statistical analysis, analysis program, and existing guidelines. Researchers must navigate through the plethora of recommendations, taking into account the specificities of their study, to ensure a robust and reliable sample size. For a deeper understanding, it is strongly recommended to refer to the detailed paper mentioned in this tutorial.
Reference Paper: Memon, M. A., Ting, H., Cheah, J. H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), 1-20.
