**Ratio variables are quantitative variables that have a clear definition of zero and have consistent intervals between each category, allowing for calculations of magnitude (Katz, 2006a; Katz, 2006b)**

For instance, height in centimeters and weight in pounds are ratio variables. For each, zero denotes the absence of a quantity. As a result, researchers are able to declare mathematical comparisons between items on the scale, such as: “Person A weighs twice as much as Person B” (Babbie, Halley & Zaino, 2007).

The advantage of ratio variables is that they can facilitate the comparison of ratios, detection of proportions, and calculation of various statistical parameters such as mean, median, and standard deviation.

**An Academic Definition:**“A

*ratio variable*has equal intervals between values and a meaningful zero point .”(source: Norman & Streiner, 2008, p. 4)

**Contents**show

## Ratio Variables Examples

**1. Age**

This well-known ratio variable example works as follows: Person A may be twice as old as person B, exemplifying the characteristics of ratio variables.

**2. Height**

Measurement of height, in units such as centimeters or inches, is a ratio variable. Zero height indicates no height, building on the definition of ratio variables.

**3. Weight**

Similar to height, weight is another prime example of a ratio variable. It can be determined accurately in kilograms or pounds, and zero weight signifies the absence of mass.

**4. Income**

An individual’s income, calculated on a yearly, monthly, or hourly basis, is a typical ratio variable. With income, we understand that the zero point signifies an absence of income.

**5. Number of Employees in a Company**

The total number of employees in an organization is a perfect instance of a ratio variable. Zero employees would mean there are no workers in the organization.

**6. Amount of Rainfall**

The amount of rainfall (e.g., in millimeters or inches) represents a ratio variable, as zero rainfall implies no precipitation.

**7. Internet Speed**

Internet speed, usually gauged in megabits per second (Mbps), pertains to ratio variables category. Zero Mbps points to no internet speed.

**8. Number of Items Sold**

The total count of products sold during a given period serves as a typical ratio variable example. Zero would signal that no merchandise was sold.

**9. Temperature (Kelvin)**

While temperatures measured in Celsius or Fahrenheit are interval variables, temperature measured in Kelvin is a ratio variable because it has a clear definition of zero.

**10. Distance**

Distances (for instance, between cities or within a race), calculated in meters or miles, fall under the category of ratio variables. Zero distance signifies that there’s no separation between two points.

**11. Volume**

The volume of a liquid or a gas, expresed in liters, gallons, or cubic meters, is a ratio variable because having zero volume means the absence of any quantity.

**12. Density**

Density, represented as mass per unit volume (kilograms per cubic meter or grams per cubic centimeter), is an example of a ratio variable. A density value of zero would imply no mass in a given volume.

**13. Speed**

Speed, determined as distance per unit time (kilometers per hour or meters per second), is a fitting example of a ratio variable. A speed of zero implies no motion.

**14. Consumption**

Consumption quantities, such as the number of candies consumed in a week or power consumption in kWh, depict ratio variables. Zero consumption points toward no usage or intake.

**15. Test Scores**

A student’s total points scored in an exam, stretching from zero to a maximum achievable score, is a common example of ratio variables. A score of zero suggests no correct answers were given.

**16. Bank Balance**

This indicates the amount of money in someone’s account and is a clear-cut example of a ratio variable. A zero balance means there’s no money in the account.

**17. Time**

Time, regardless of it being expressed in seconds, minutes, or hours, is a ratio variable. Zero time implies the absence of any time duration.

**18. Body Mass Index**

BMI, calculated as weight (kg) divided by height (m) squared, is a classic ratio variable example. A BMI of zero would signal no body mass, which is an impossibility, affirming the significance of zero for ratio variables.

**19. Number of Births**

The number of births in a specific area over a certain time frame is a ratio variable. Zero births would indicate no new births during that period.

**20. Sales Revenue**

The generated sales revenue of a product or a company is a prime example of a ratio variable. Zero sales revenue indicates no sales during a given period.

**21. Physical Activity Level**

Physical activity level is a ratio variable, measured by the number of steps taken or calories burned. Zero steps or calories burned means no physical activity has occurred.

**22. Battery Life**

Battery life, conveyed in minutes or hours before the battery drains, falls into the category of ratio variables. Zero battery life means the battery is completely drained.

**23. Agricultural Yield**

Agricultural yield, expressed as bushels per acre or kilograms per hectare, is a typical ratio variable. Zero yield would mean no crops were produced on the land area in question.

**24. Blood Pressure**

Blood pressure, measured in mmHg, is a ratio variable since it obeys the rules of quantity and zero. Zero blood pressure would infer no blood flow, signaling a medical emergency.

**25. Noise Levels**

Noise levels, measured in decibels (dB), is a ratio variable. Zero decibels indicates the absence of any detectable sound.

## Types Of Variables (Compare And Contrast)

**Ratio variables sit alongside nominal, ordinal, and interval variables in the categorization of variables.**

Here are brief descriptions of each:

**Nominal variables**embody categories lacking any inherent sequence or ranking (Wilson & Joye, 2016). Musical genres or breakfast cereal brands are exemplary nominal variables. Worth noting is that you cannot accomplish mathematical operations with nominal variables. The solitary task you can do is count the number of instances that fall inside a particular category.**Ordinal variables**classify the data into categories with a sense of sequence or ranking (De Vaus, 2001; Stockemer, 2018). Athletic competitions wherein athletes finish in “first”, “second”, “third” positions (their ranks) is an ordinary illustration. The precise numerical differences or intervals among ordinal categories stay unknown or inconsistent, unlike ratio or interval variables.**Interval variables**involve variables having categories with a recognized and consistent disparity among them (Lewis-Beck, Bryman & Liao, 2004). An example is temperature gauged in degrees Celsius, wherein the difference between 30 and 31 degrees is the same as between 20 and 21. However, lacking a true absolute zero renders it unwise to compare magnitudes with interval variables. For instance, you can’t profess that 30°C is ‘doubly as hot’ as 15°C.**Ratio variables**, as explained earlier, are akin to interval variables but come with a clear definition of zero, hinting at the total absence of a quantity (Katz, 2006a; Katz, 2006b). Parameters such as height in centimeters or weight in pounds satisfactorily demonstrate the trait of ratio variables.

## Conclusion

Ratio variables are significant entities in quantitative research across various disciplines. They offer an ordered categorization of data, with defined distances between categories, and a clear absolute zero. These variables allow the calculation of ratio and proportions and the conduction of diverse statistical analyses like determining mean, median and standard deviation. Their zero-establishing property distinguishes them from interval and ordinal variables and brings out their uniqueness in comparison to nominal variables. As the numerical values allow the computation of magnitude and size, ratio variables provide a comprehensive scale of measurement. However, while formulating research questions or designing experiments, researchers ought to carefully consider the choice of variable types in accordance with their research aims, the nature of the variable, and the inherent characteristics of the sample being investigated.

## References

Babbie, E., Halley, F., & Zaino, J. (2007). *Adventures in Social Research: Data Analysis Using SPSS 14.0 and 15.0 for Windows* (6th ed.). New York: SAGE Publications.

De Vaus, D. A. (2001). *Research Design in Social Research*. New York: SAGE Publications.

Katz, M. (2006*). Study Design and Statistical Analysis: A Practical Guide for Clinicians*. Cambridge: Cambridge University Press.

Katz, M. H. (2006). *Multivariable analysis: A practical guide for clinicians*. Cambridge: Cambridge University Press.

Lewis-Beck, M., Bryman, A. E., & Liao, T. F. (Eds.). (2004). *The SAGE Encyclopedia of Social Science Research Methods (Vol. 1)*. London: SAGE Publications.

Norman, G. R., & Streiner, D. L. (2008). *Biostatistics: The Bare Essentials*. New York: B.C. Decker.

Stockemer, D. (2018). *Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in SPSS and Stata*. London: Springer International Publishing.

Wilson, J. H., & Joye, S. W. (2016). *Research Methods and Statistics: An Integrated Approach*. New York: SAGE Publications.

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]