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Level of measurement for quantitative research
In quantitative research, the rat
Level of measurement for quantitative research
In quantitative research, the ratio level of measurement is often preferred due to its comprehensiveness and flexibility. The ratio level includes all the properties of the other levels of measurement (nominal, ordinal, and interval) and adds a true zero point, which allows for the meaningful comparison of absolute magnitudes.
Below I am going to put the key advantages of measuring the level of relationship. True Zero Point: Unlike interval data, ratio data has a true zero point that signifies the absence of the quantity being measured. This makes it possible to say that one value is twice as much as another, which is a meaningful comparison in many research contexts (Trochim, 2020). All Mathematical Operations: Ratio data supports all types of mathematical operations, including addition, subtraction, multiplication, and division. This capability makes ratio data the most versatile and powerful for statistical analysis (Stevens, 1946). Wide Applicability: Many real-world phenomena that researchers are interested in measuring (e.g., height, weight, time, and money) are naturally at the ratio level, making this level of measurement both practical and essential for a wide range of studies (Heiman, 2014).
Where there are also examples like Physical Measurements: Height and weight are classic examples of ratio data, as they have a true zero point and allow for a full range of mathematical operations. Economic Data: Income and expenditure data are also ratio measures, enabling complex economic analyses that require the calculation of growth rates, averages, and other key metrics.
Scholarly Support According to Trochim (2020), the ratio scale is paramount in research requiring precise measurement and comparison because it includes a non-arbitrary zero point, allowing for meaningful ratio comparisons. Similarly, Heiman (2014) underscores that the ability to perform all mathematical operations with ratio data enhances the scope and depth of statistical analysis, which is crucial for robust research findings.
In conclusion, the ratio level of measurement is preferred in quantitative research because it provides the greatest flexibility and precision, enabling a broad range of statistical techniques and ensuring that comparisons between measurements are meaningful and accurate.
References
Heiman, G. W. (2014). Basic Statistics for the Behavioral Sciences. Cengage Learning.
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677-680.
Trochim, W. M. (2020). Research Methods: The Essential Knowledge Base. Cengage Learning.
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