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## Scales of Measurements Scales of measurements are categorized using different scales of measurements. Each level of measurement has unique properties that determine the various use of statistical analysis. There are four different scales of measurements; data can be defined as being one of the f...

## Scales of Measurements Scales of measurements are categorized using different scales of measurements. Each level of measurement has unique properties that determine the various use of statistical analysis. There are four different scales of measurements; data can be defined as being one of the four scales. The four types of scales are: 1. **Nominal Scale** 2. **Ordinal Scale** 3. **Interval Scale** 4. **Ratio Scale** ### Nominal Scale The Nominal scale, also called the categorical variable scale, is defined as a scale used for labeling variables in distinct classifications and does not involve a quantitative value or order. - This scale is the simplest of the four variable measurement scales. - Calculations done on these variables will be futile as there is no numerical value of the options. - The sequence in which subgroups are listed makes no difference, as there is no relationship among subgroups - A subgroup of a nominal scale with only two categories (e.g. male/female) is called "dichotomous." - The analysis of gathered data will happen using percentages or mode. Nominal scale is often used in research surveys and questionnaires where only variable labels have significance. For instance, - **"Which brand of smart phones do you prefer?"** - Options: "Apple"- 1, "Samsung"-2, "OnePlus"-3 - **"What is your Gender?"** - Options: "Male" 1, "female" 2 - **"What is your Political preference?"** - Options: 1- Independent, 2-Democrat, 3-Republican ### Ordinal Scale The Ordinal Scale is defined as a categorical variable measurement scale used to simply depict the order of variables and not the difference between each of the categorical variables. - These scales are generally used to depict non-mathematical ideas such as frequency, satisfaction, happiness, a degree of pain, etc. - It is quite straightforward to remember the implementation of this scale as 'Ordinal' sounds similar to 'Order', which is exactly the purpose of this scale. - Ordinal Scale maintains descriptional qualities along with an order but is void of an origin of scale and thus, the distance between variables can't be calculated. - Origin of this scale is absent, due to which there is no fixed start or "true zero". - The analysis of gathered data will happened using percentages or median. These scales are generally used in market research to gather and evaluate relative feedback about product satisfaction, changing perceptions with product upgrades etc. For example, ordinal scale question such as: **"How satisfied are you with our services?"** **Very Unsatisfied-1: Unsatisfied-2: Neutral-3: Satisfied-4: Very Satisfied-5** Here, the order of variables is of prime importance and so is the labeling. Very unsatisfied will always be worse than unsatisfied and satisfied will be worse than very satisfied. ### Interval Scale The Interval Scale is defined as a measurement scale where there is a known and constant difference between these variables. - Variables, which have familiar, meaningful numbers with a defined interval, are defined as Interval scales. - Mean, median or mode can be calculated using these scales. - The only drawback of this scale is that it does not have a true zero point. - Interval scale contains all the characteristics of the Ordinal scale, i.e. it maintains the difference between variables. - The main characteristic of this scale is that the difference between values is consistent. For example, 80 degrees is always higher than 70 degrees because the difference between 70 and 80 degrees is the same as the difference between 60 and 70 degrees. - Also, the value of 0 is arbitrary. Hence, there is no absolute zero point in scales like Celsius/Fahrenheit temperature which is a common example. - Due to absence of absolute zero, we cannot calculate ratios, and thus we cannot say if 40 degrees is twice as hot as 20 degrees. - All the techniques applicable for the ordinal scale are also applicable for the interval scale. Amongst all the techniques, there are some specific techniques like correlations and regression analysis that is extensively followed in the analysis of interval scales. ### Ratio Scale The Ratio Scale is defined as a measurement scale where the difference between the values is known, and it makes the difference between values meaningful. - It is calculated by assuming that the difference between the values is the same and there is a specific, fixed starting point or absolute zero. - Ratio scale accommodates all the characteristics of the Interval scale, and hence, the significance between values is understood (e.g., they are usually equidistant). - Because of the existence of an absolute zero, we can calculate ratios on a ratio scale, the researcher can perform all the techniques applicable for interval scales along with the presence of ratios. - Ratio scale provides the best opportunity for the researcher to apply different statistical techniques such as the mean, median, mode, standard deviation, variance, range, variation or harmonic mean. **Best examples of ratio scales are:** - **"What is your daughter's height"?** - Less than 5 feet. - 5 feet 1 inch-5 feet 5 inches. - 5 feet 6 inches-6 feet. - More than 6 feet. **Summary of Levels of Measurements Offers:** - The sequence of variables. - Mode. - Median. - Mean.