Introduction

Sample size is one of the most decisive factors for the validity and reliability of a research study. By the term sample size, we refer to the number of observations, participants, or respondents selected from a population with the aim of drawing conclusions about it. Choosing the correct size is not a simple process, as it is influenced by multiple factors such as the nature of the study, the variables under investigation, the expected effect size, available time, and resources. An insufficient sample can lead to misleading results, while an excessively large sample may cause unnecessary waste of resources. The balance between adequacy and efficiency is fundamental to research quality.

Sample-to-Item Ratio

One of the basic approaches for determining sample size is the sample-to-item ratio. This rule suggests that the sample size should be at least five times the number of items included in a study. For example, in a survey with thirty questions, at least one hundred and fifty participants would be required. However, many researchers propose stricter ratios, such as twenty-to-one, which in the same case increases the number of required participants to six hundred. This stricter approach strengthens statistical power and reliability, reduces the probability of errors, and increases the certainty of conclusions.

Sample-to-Variable Ratio

Another common approach is the sample-to-variable ratio. In this case, the number of independent variables entered into a model is taken into account. Although the minimum suggested ratio is five participants per variable, larger ratios such as fifteen-to-one or twenty-to-one are often preferred. A model with five independent variables could, according to the minimum rule, rely on only twenty-five participants, something considered insufficient for the majority of inferential analyses. The adoption of higher ratios ensures stability and persuasiveness in the results and is particularly important in studies using multiple regression analyses, where data requirements are greater.

A-priori Sample Size

Beyond empirical rules, modern studies often rely on the methodology of a-priori sample size, which is especially used in structural equation models. Through online power analysis applications, the minimum required number of participants is determined based on the number of latent and observed variables, the expected effect size, the level of statistical significance, and the desired statistical power. The advantage of this method is that it provides a customized and precise estimate, in contrast to general rules that may not reflect the specific characteristics of each study. Thus, a-priori sample size is considered a superior tool, as it increases validity and reduces the risk of inadequate research design.

Validation of Sample Size

The process of validating the sample size is a critical step before data collection. Its purpose is to ensure that the chosen sample size corresponds to the goals of the study and the requirements of the research design. Validation is usually carried out with the contribution of experts who have proven experience in the specific field and knowledge of quantitative methods. Researchers must present detailed information about the object of the study, the target population, the unit of analysis, and the availability of a sampling frame, so that experts have all necessary data to evaluate sample adequacy. This process must be documented and supported by recorded correspondence and recommendations, ensuring transparency and clarity during the final evaluation.

Conclusions

In conclusion, sample size is not a mere numerical decision but a multidimensional factor that influences the quality and reliability of research. From basic ratios such as sample-to-item or sample-to-variable, to more advanced approaches like a-priori sample size, every methodological choice aims to ensure statistical power and validity of findings. Validation of sample size by experts provides an additional guarantee of reliability. Careful selection and thorough documentation of sample size ultimately form a cornerstone for the success of any research project.