Introduction

Statistical analysis lies at the heart of scientific research and the quantitative evaluation of data. Through this process, it becomes possible to interpret information derived from questionnaires, experiments, and measurements, in order to draw conclusions with scientific validity. In every such endeavor, the concept of reliability plays a decisive role, as it reflects the level of consistency and stability of the measurements. Reliability is intrinsically linked with the validity of research because without stable results it is impossible to derive useful and objective conclusions. The development of measurement scales that accurately capture the variables under study is therefore a necessary condition for the advancement of scientific knowledge.

Measurement Scales of Variables

Statistical science recognizes different types of measurement scales, which are used to categorize and describe variables. These scales are not merely technical tools; they largely determine the type of analysis that can be applied and the way results should be interpreted.

The nominal scale is the simplest form of measurement. It is used to assign labels to categories without any hierarchy or mathematical relationship between them. A typical example is the categorization of gender or religion. Although it lacks quantitative elements, it is highly useful when the goal is to distinguish and identify groups within a population.

The ordinal scale introduces the element of order. In this case, the categories have a logical hierarchy, although the distance between them cannot be measured precisely. For instance, recording the degree of satisfaction of an individual, where responses range from “not at all satisfied” to “very satisfied,” illustrates a clear order, but it is not known whether the difference between the intermediate choices is equal or not.

The interval scale goes a step further by introducing the concept of equal distances between values. The values of such a scale have numerical meaning, since the difference between them can be measured and compared. However, there is no absolute zero that represents a complete absence of the characteristic. A typical example is temperature in Celsius, where differences can be calculated, but zero does not indicate a total lack of heat.

The ratio scale is the most complete form of measurement. It has all the characteristics of the interval scale, but it also includes an absolute zero, making it possible to compare not only differences but also ratios. Measurements of weight or income fall into this category. The existence of an absolute zero allows statements such as “one individual earns twice as much as another,” which is impossible to express with other types of scales.

Estimation of Reliability

Beyond selecting the appropriate measurement scale, research must ensure the reliability of its results. The reliability index expresses the proportion of response variance that can be attributed to real differences among participants compared to the total observed variance. The higher this index, the more reliable the questionnaire is considered.

The reliability of a questionnaire is directly related to the number of questions it contains. As the number of items increases, so does the likelihood of consistently capturing the concept under study. The sum of participants’ responses forms the composite variable, which reflects the overall score of each respondent. To estimate the reliability of this variable, different methods are employed, with the most widely used being Cronbach’s alpha and the split-half method.

Cronbach’s alpha is the most well-known indicator of internal consistency. It evaluates the extent to which the items that make up a scale are consistent with one another and measure the same theoretical construct. A high coefficient indicates that the items are not randomly selected but jointly contribute to measuring the same concept.

The split-half method is based on the principle that a reliable questionnaire should yield similar results even if divided into two equal parts. The items are split into two groups, responses are compared, and the correlation between the two sets is then estimated using the Spearman-Brown coefficient. The higher the correlation, the stronger the reliability is considered to be.

Importance of Reliability

Reliability is not merely a statistical indicator but the foundation on which the validity of research rests. A measurement that is not reliable cannot be valid, even if it presents seemingly interesting findings. Reliability ensures that differences observed among participants reflect true distinctions and not random measurement errors. At the same time, it allows for replication of the study and comparison of results with future research, thereby strengthening the scientific credibility of the findings.

Conclusion

The statistical analysis of measurement scales and the estimation of their reliability form an integral part of every scientific investigation. From nominal categories to complex ratio scales, each type of measurement has its place and significance. Choosing the appropriate scale is the first step toward correct analysis, while ensuring high reliability through indices such as Cronbach’s alpha and the Spearman-Brown coefficient makes results both valid and reproducible. Reliability is therefore not simply a technical statistical requirement but a fundamental prerequisite for producing knowledge that can be applied in practice. Without it, research risks leading to arbitrary conclusions, while with it, scientific studies can substantially contribute to the progress of knowledge.