In statistics, there are four types of data and measurement scales: nominal, ordinal, interval and ratio. This approach to sub-order various types of data (here’s an outline of measurable information types). This theme is typically examined with regards to scholastic educating and less frequently in “the  present reality.” If you are looking over this idea for a measurement test, thank an analyst scientist named Stanley Stevens for thinking of these terms. 

These four information estimation scales (ostensible, ordinal, interim, and proportion) are best comprehended with a model, as you’ll see underneath. 


How about we start with the easiest one to understand. Nominal scales are utilized for marking variables, with no quantitative worth. “Nominal” scales could essentially be classified “names.” Here are a few models, underneath. Notice that these scales are totally unrelated (no cover) and none of them have any numerical centrality. A decent method to recollect the majority of this is “nominal” sounds a great deal like “name” and nominal scales are somewhat similar to “names” or names. 

Note: a sub-sort of a nominal scale with just two classes (for example male/female) is classified as “dichotomous.” If you are an undergrad , you can utilize this  to intrigue your educators. 

Reward Note #2: Other sub-sorts of nominal information are “nominal with order” (like “cold, warm, hot, very hot”) and nominal without order (like “male/female”).


With ordinal scales, the request for the qualities is what’s significant and huge, however, the contrasts between each one aren’t generally known. Investigate the model underneath. For each situation, we realize that a #4 is superior to a #3 or #2, yet we don’t have the foggiest idea and can’t measure how much better it is. For instance, is the distinction between “alright” and “despondent” equivalent to the contrast between “Exceptionally Happy” and “Glad?” It is difficult to judge. 

Ordinal scales are commonly proportions of non-numeric ideas like fulfillment, satisfaction, inconvenience, and so on. 

“Ordinal” is anything but difficult to recall in light of the fact that sounds like “order” and that is the way to recollect with “ordinal scales”– the order matters, yet that is all you truly get from these. 

Advanced note: The most ideal approach to decide focal propensity on a lot of ordinal information is to utilize the mode or middle; a perfectionist will disclose to you that the mean can’t be characterized from an ordinal set. 


Interval scales are numeric scales in which we know both the order and the careful contrasts between the qualities. The great case of an interval scale is Celsius temperature on the grounds that the contrast between each worth is the equivalent. For instance, the distinction somewhere in the range of 60 and 50 degrees is a quantifiable 10 degrees, similar to the contrast somewhere in the range of 80 and 70 degrees. 

Interval scales are nice because the realm of statistical analysis on these data sets opens up.  For example, the central tendency can be measured by mode, median, or mean; standard deviation can also be calculated.  Like the others, you can recall the key purposes of an “interval scale” pretty effectively. “Interval” itself signifies “space in the middle of,” which is the significant thing to recall interim scales inform us regarding the order, yet additionally about the incentive between each item. Here’s the issue with interval scales: they don’t have a “genuine zero.” For instance, there is nothing of the sort as “no temperature,” at any rate not with celsius. On account of interval scales, zero doesn’t mean the nonappearance of significant worth, however, is really another number utilized on the scale, similar to 0 degrees celsius. Negative numbers likewise have meaning. Without a genuine zero, it is difficult to process proportions. With interval information, we can include and subtract, however, we can’t duplicate or gap.  Confused? Alright, think about this: 10 degrees C + 10 degrees C = 20 degrees C. No issue there. 20 degrees C isn’t twice as hot as 10 degrees C, in any case, in light of the fact that there is nothing of the sort as “no temperature” with regards to the Celsius scale. At the point when changed over to Fahrenheit, it’s unmistakable: 10C=50F and 20C=68F, which is plainly not twice as hot.. The bottom line is that  interval scales are great, but we cannot calculate ratios, which brings us to our last measurement scale…


Ratio scales are the ultimate nirvana when it comes to data measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied.  At the risk of repeating myself, everything above about interval data applies to ratio scales, plus ratio scales have a clear definition of zero.  Good examples of ratio variables include height, weight, and duration.

Ratio scales provide a wealth of possibilities when it comes to statistical analysis. These variables can be meaningfully added, subtracted, multiplied, divided (ratios). The central tendency can be measured by mode, median, or mean; measures of dispersion, such as standard deviation and coefficient of variation can also be calculated from ratio scales.

Ratio scales are a definitive nirvana with regards to information estimation scales since they enlighten us concerning the request, they reveal to us the accurate incentive among units, AND they likewise have an outright zero–which takes into consideration a wide scope of both graphic and inferential insights to be applied. At the danger of rehashing myself, everything above about interim information applies to proportion scales, in addition to proportion scales that have an unmistakable meaning of zero. Genuine instances of proportion factors incorporate stature, weight, and span. 

Proportion scales give an abundance of potential outcomes with regards to the measurable investigation. These variables can be meaningfully added, subtracted, multiplied, divided (ratios). Focal inclination can be estimated by mode, middle, or mean; proportions of scattering, for example, standard deviation and coefficient of variety can likewise be determined from proportion scales.


In outline, ostensible factors are utilized to “name,” or name a progression of qualities. Ordinal scales give great data about the request for decisions, for example, in a consumer loyalty review. Interim scales give us the request for values + the capacity to evaluate the contrast between each one  At long last, Ratio scales give us a definitive request, interim qualities, in addition to the capacity to compute proportions since a “genuine zero” can be characterized.