VARIABLES

Quantitative research focuses on measurement and assumes that the subject of inquiry can be quantified. Its main aim is to provide comprehensive data through measurement, analyse the data for patterns and connections, and verify its accuracy. The scope of quantitative research ranges from easily quantifiable attributes such as height and weight to more intangible elements such as human emotions and thoughts.

The quantitative research approach is highly precise and logical, utilising statistical analysis to its fullest extent. Its ability to test theories through hypothesis formulation and formal statistical analysis sets it apart as a methodology. It is especially useful in measuring variables such as height, weight, attitude, and well-being, differentiating between independent and dependent classifications and capturing the influence of the former on the latter. Multiple hierarchical measurement theories are also employed to acquire diverse measurement types (Tab. 4).

At its most basic level, nominal classification categorises data without quantitative analysis. As we move towards ordinal measurement, we introduce a hierarchical structure to the data, although this method may require more precision. We rely on interval and ratio-level measurements for increased accuracy, although generating a ratio can be challenging when studying social phenomena. Ordinal and interval measurements are the most commonly used techniques in quantitative research.

Regardless of the method of measurement, errors are bound to occur. These errors can stem from various sources, including instrument, human, and random errors.

Although it is possible to reduce instrument and human errors, it is impossible to control random errors. Therefore, it is essential to consider random errors when designing and using any instrument. Instrumental and human errors can manifest in two ways: within the instrument (or within the human operator), which means that the same instrument may produce varying results in different settings, or inter-instrument (or human-to-human), which means that two seemingly identical instruments may yield different results.

Similarly, human errors imply that individuals using the same instrument may obtain divergent results with different advantages. On the other hand, instrument errors imply that two people using the same instrument may obtain different depths simultaneously. While errors cannot be eliminated, they can be minimised.

Effective instruments must be designed to minimise instrument errors. In social research, this means ensuring that observational questionnaires and checklists are easily comprehended and that questions are answered precisely.

When designing instruments, it is crucial to balance "authenticity" and "directivity." An authentic instrument measures as much as possible about a phenomenon but risks becoming indirect. In contrast, a direct instrument focuses only on items directly related to the phenomenon, potentially losing some authenticity (Watson, 2015).