There are four scales of measurement in statistics which are nominal scale, ordinal scale, interval scale, and ratio scale. Scales of measurement are defined as the ways to collect and analyze data. It depends on the purpose of the study and the type of data (qualitative or quantitative) on which the selection of an appropriate scale is being dependent.
|1.||What are Scales of Measurement in Statistics?|
|2.||Nominal Scale of Measurement|
|3.||Ordinal Scale of Measurement|
|4.||Interval Scale of Measurement|
|5.||Ratio Scale of Measurement|
|6.||FAQs on Scales of Measurement|
What are Scales of Measurement in Statistics?
When data is collected for a study, the next step is to analyze it which depends on the tools that we used for data collection. For example, if we want to collect qualitative data, then we can use certain labels (nominal scale) from which the respondents will select their option. For quantitative data, interval scales and ratio scales can be used which makes it possible for the researcher to represent the data using numbers. Let us take an example of data collection to find out the nature of cars people prefer to drive. This type of data can be collected using a scale with certain labels like electric cars, diesel cars, hybrid cars, etc. So, a nominal scale of measurement will be used for this purpose. Similarly, if the researcher wants to find out the weight of people in a town, then a ratio scale of measurement can be used. We will be learning about the properties of all four scales of measurement in this article in the sections below.
The four scales of measurement in statistics are listed below:
These scales of measurement are written in a fixed order which specifies that the ordinal scale contains properties of a nominal scale as well, the interval scale has properties of both nominal and ordinal scales, and at last, the ratio scale has properties of all the above three scales of measurement.
Let us learn about each measurement scale one by one.
Nominal Scale of Measurement
A nominal scale of measurement is used for qualitative data. It does not give any numerical meaning to the data. Using the nominal scale of measurement, the data can be classified but cannot be added, subtracted, multiplied, or divided. It can cover a wide variety of qualitative data. Some of the situations where nominal measurement scale can be used are given below:
- Study to find the country of birth of people in a town
- In collecting data on the eye color of people
- Classifying people into categories like male/female, working-class population/unemployed, vaccinated/unvaccinated people, etc.
Some of the properties of the nominal scale of measurement are given below:
- It can categorize variables but does not put them in any order.
- It does not show any numerical value.
- It is used for qualitative data.
Ordinal Scale of Measurement
The ordinal scale of measurement groups the data into order or rank. It contains the property of nominal scale as well, which is to classify data variables into specific labels. And in addition to that, it organizes data into groups though it does not have any numerical value. For example, the study of people's satisfaction with a company's product on a scale of #1 - Very happy, #2 - satisfactory, #3 - neutral, #4 - unhappy, and #5 - extremely dissatisfied. This is an example of an ordinal scale of measurement. This measurement scale can be used for the following purposes:
- Ranks of players in a race.
- Data collection on variables such as hottest to coldest, richest to poorest, etc.
- Data on people's satisfaction with any product, person, or government.
Some of the properties of the ordinal measurement scale are listed below:
- It displays the order or rating of the variables.
- It does not give any numerical value to the data. So, it is also used for qualitative data as similar to nominal measurement scale.
- It contains variables that can be placed in order like heaviest to lightest, ranks of players or students, etc.
Interval Scale of Measurement
The interval scale of measurement includes those values that can be measured in a specific interval, for example, time, temperature, etc. It shows the order of variables with a meaning proportion or difference between them. For example, on a temperature scale, the difference between 20 °C and 30 °C is the same as the difference between 50°C ad 60°C. It is an example of an interval measurement scale. On the other hand, the difference between the scores of the first two rankers in a race and the two runner-ups will be different, which is an example of an ordinal scale.
Some of the properties of the interval scale of measurement are listed below:
- It includes the properties of both nominal and ordinal scales.
- It shows meaningful divisions between variables.
- The difference between the variables can be presented in numerical terms.
- It includes variables that can be added or subtracted from each other.
- It gives a meaning to 'Zero" which was not possible in the above two scales. For example, zero degrees of temperature.
Ratio Scale of Measurement
The ratio scale is the most comprehensive scale among others. It includes the properties of all the above three scales of measurement. The unique feature of the ratio scale of measurement is that it considers the absolute value of zero, which was not the case in the interval scale. When we measure the height of the people, 0 inches or 0 cm means that the person does not exist. On the interval scale, there are values possible on both sides of 0, for example, temperature could be negative as well. While the ratio scale does not include negative numbers because of its feature of showing absolute zero. An example of the ratio measurement scale is determining the weight of people from the following options: less than 20 kgs, 20 - 40 kgs, 40 - 60 kgs, 60 - 80 kgs, and more than 80 kgs.
Some of the properties of the ratio scale of measurement are listed below:
- It is used for quantitative data.
- It shows the absolute value of zero which means if the value is 0, it's nothing.
- The variables can be added, subtracted, multiplied, or divided. In addition to these, calculation of mean, median, and mode is also possible with this scale.
- it doesn't include negative numbers because of the feature of true zero value.
Look at the table below showing the properties of all four scales of measurement.
|Meaningful order of variables||✖||✔||✔||✔|
|The absolute value of zero||✖||✖||✖||✔|
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FAQs on Scales of Measurement
What are the Scales of Measurement?
The scales of measurement are the ways or a specific attribute of data collection related to its purpose and analyses. For qualitative data, nominal and ordinal scales are preferred to use, while for quantitative data, interval and ratio scales are preferred. It depends on the data variables as to which scale has to be used. For example, for determining gender, favorite color, types of bikes preferred, etc the nominal scale is used.
What Differentiates Interval from Ratio Scales of Measurement?
There is a small difference in the interval and the ratio scales of measurement which is the absolute value of zero. The interval scale does include zero but fails to represent its true value. For example, on a temperature scale which is an example of an interval scale, 0 degrees has a specific value. There are values present to both sides of zero. On the other hand, on the ratio scale, for example, height, weight, etc, 0 means nothing. If the height or weight is 0, it means that the person does not exist. If the number of cars sold by XYZ enterprises is 0 on a particular day, it means there is no car sold by them. This is the basic difference between the interval and the ratio scales of measurement.
What are the 4 Scales of Measurement?
The four measurement scales are given below:
How Many Scales of Measurement are there in Research?
There are four scales of measurement in research which are nominal scale, ordinal scale, interval scale, and ratio scale.
How Many Scales of Measurement Exist?
Four measurement scales exist in math which are used for research purposes. Those scales are nominal, ordinal, interval, and ratio scales.
Why are Scales of Measurement Important?
Scales of measurement help us to interpret the numbers or variables in a detailed manner. Each scale provides a different type of information that can be kept in mind while interpreting or analyzing the data.
Nominal: the data can only be categorized. Ordinal: the data can be categorized and ranked. Interval: the data can be categorized and ranked, and evenly spaced. Ratio: the data can be categorized, ranked, evenly spaced and has a natural zero.What are the 4 types of measurement scales? ›
Psychologist Stanley Stevens developed the four common scales of measurement: nominal, ordinal, interval and ratio. Each scale of measurement has properties that determine how to properly analyse the data.What are the 4 types of data? ›
4 Types of Data: Nominal, Ordinal, Discrete, Continuous | upGrad blog.What are examples of interval and ratio scale of measurement? ›
Examples of interval level data include temperature and year. Examples of ratio level data include distance and area (e.g., acreage). The scales are similar in so far as units of measurement are arbitrary (Celsius versus Fahrenheit, Gregorian versus Islamic calendar, English versus metric units).What is an example of a nominal scale of measurement? ›
Example of Nominal Scale
Gender, marital status, religion, race, hair color, country, etc are examples of Nominal Scale. They are all examples of nouns that do not require rank or order.
An interval scale is one where there is order and the difference between two values is meaningful. Examples of interval variables include: temperature (Farenheit), temperature (Celcius), pH, SAT score (200-800), credit score (300-850).What is an example of an ordinal scale? ›
Ranking of high school students – 1st, 3rd, 4th, 10th… Nth. A student scoring 99/100 would be the 1st rank, another student scoring 92/100 would be 3rd and so on and so forth.
Most modern computer languages recognize five basic categories of data types: Integral, Floating Point, Character, Character String, and composite types, with various specific subtypes defined within each broad category.What are the 5 main types of data? ›
- Public data. Public data is important information, though often available material that's freely accessible for people to read, research, review and store. ...
- Private data. ...
- Internal data. ...
- Confidential data. ...
- Restricted data.
- String (or str or text). Used for a combination of any characters that appear on a keyboard, such as letters, numbers and symbols.
- Character (or char). Used for single letters.
- Integer (or int). Used for whole numbers.
- Float (or Real). ...
- Boolean (or bool).
Age, money, and weight are common ratio scale variables. For example, if you are 50 years old and your child is 25 years old, you can accurately claim you are twice their age.Is heart rate an interval or ratio? ›
c) The heart rates per minute (BPM) can be classified into an ordered scale with equidistantly placed values with an absolute zero. Thus the variable is measured on an interval scale.What is an example of a nominal data? ›
Common examples include male/female (albeit somewhat outdated), hair color, nationalities, names of people, and so on. In plain English: basically, they're labels (and nominal comes from "name" to help you remember). You have brown hair (or brown eyes). You are American.
- Gender: Male, Female, Other.
- Hair Color: Brown, Black, Blonde, Red, Other.
- Type of living accommodation: House, Apartment, Trailer, Other.
- Genotype: Bb, bb, BB, bB.
- Religious preference: Buddhist, Mormon, Muslim, Jewish, Christian, Other.
Ordinal data is data that can be ranked or ordered. Examples include data taken from a poll or survey. Nominal data is data that can be made to fit various categories. Examples include whether an animal is a mammal, fish, reptile, amphibian, or bird.What is an example of a ratio variable? ›
Examples of ratio variables include length, weight, age, height, income, and many others. Unlike interval variables, ratio variables have a true zero point, which represents the absence of the measured attribute.Is age an example of interval data? ›
Interval-level variables are continuous, meaning that each value of the variable is one increment larger than the previous and one smaller than the next value. Age, if measured in years, is a good example; each increment is one year.Is weight an example of interval scale? ›
Most physical measures, such as height, weight, systolic blood pressure, distance etc., are interval or ratio scales, so they fall into the general "continuous " category.What are 5 examples of ordinal data? ›
|What is your age?||0-18 19-34 35-49 50+|
|What is your education level?||Primary school High school Bachelor's degree Master's degree PhD|
|In the past three months, how many times did you buy groceries online?||None 1-4 times 5-9 times 10-14 times 15 or more times|
Ordinal data, on the other hand, is a type of data that has a natural ordering or ranking. It is categorical data that can be ranked or ordered in accordance with a specific attribute or characteristic. Examples of ordinal data are the level of education, the range of income, or the grades.
Interval data is measured along a numerical scale that has equal distances between adjacent values. These distances are called “intervals.” There is no true zero on an interval scale, which is what distinguishes it from a ratio scale.What are the 3 most common data types? ›
Most programming languages support basic data types of integer numbers (of varying sizes), floating-point numbers (which approximate real numbers), characters and Booleans.What are the 8 simple data types? ›
Primitive Data Types. The eight primitives defined in Java are int, byte, short, long, float, double, boolean and char. These aren't considered objects and represent raw values.Is height nominal or ordinal? ›
Your height in inches is a C.
Categorical or nominal variables are characteristics with no ordering such as gender or religion. On the other hand, ordinal variables are non-numerical variables that have an order to them (like small, medium, or large).
Age can be both nominal and ordinal data depending on the question types. I.e “How old are you” is used to collect nominal data while “Are you the firstborn or What position are you in your family” is used to collect ordinal data. Age becomes ordinal data when there's some sort of order to it.What is the difference between nominal and ordinal? ›
Nominal scale is a naming scale, where variables are simply “named” or labeled, with no specific order. Ordinal scale has all its variables in a specific order, beyond just naming them. Interval scale offers labels, order, as well as, a specific interval between each of its variable options.What are 5 most common data types and explain each of them with example? ›
|integer||whole numbers||-5 , 0 , 123|
|floating point (real)||fractional numbers||-87.5 , 0.0 , 3.14159|
|string||A sequence of characters||"Hello world!"|
|Boolean||logical true or false||true , false|
- Double or Real.
- Integer. Integer data types often represent whole numbers in programming. ...
- Character. In coding, alphabet letters denote characters. ...
- Date. This data type stores a calendar date with other programming information. ...
- Floating point (real) ...
- Long. ...
- Short. ...
- String. ...
Nominal variables (from Latin for name) are things like eye colour or hair colour. We might have: 1=blue eyes, 2=brown eyes, 3=green eyes.
Percent grades or scores are really not ratio or interval, but ordinal. Although we treat them routinely as interval, we should feel awkward about it. Pass/fail is not nominal, grade (at least as you have it) is not interval. Then there's ratio.Is height an interval or ratio? ›
Variables like height, weight, enzyme activity are ratio variables.Is blood pressure an interval or ratio? ›
Answer and Explanation: Clearly, this variable makes use of two values, and the difference between them tells us if we have normal or high blood pressure. Because of this, systolic blood pressure can be classified as an interval variable.Is blood pressure an interval? ›
The international BP guidelines for the use of home BP [12–14] have stated that when a series of readings is taken, a minimum of two readings should be taken at intervals of at least 1 min, and the average of those readings should be used to represent the patient's blood pressure.Is salary a ratio or interval? ›
Income, height, weight, annual sales, market share, product defect rates, time to repurchase, unemployment rate, and crime rate are examples of ratio data.Is education nominal or ordinal? ›
For example, education level (with possible values of high school, undergraduate degree, and graduate degree) would be an ordinal variable.Is yes or no nominal or ordinal? ›
Nominal data (from the Latin word “nomen” meaning “named” data), is data that names or labels variables without a numerical value. The simplest example would be “yes” or “no.” These are two categories, but there is no way to order them from highest to lowest or best to worst.Is temperature an ordinal variable? ›
This means that temperature is ordinal data because it can be expressed in a given order such as extremely hot, very hot, hot, moderately hot, a little bit hot and so on. For example temperature between 20 and 30 degree Celsius can be considered hot.What is Nominal Scale 10 examples? ›
Examples of nominal scales include gender, marital status, college major, and blood type. Binary variables are a type of nominal data. These data can have only two values. Statisticians also refer to binary data as indicator variables and dichotomous data.What things are nominal? ›
A fee can be called nominal when it's small in comparison to the value of what it buys. So, for example, you might sell a friend a good piece of furniture for a nominal amount. And the charge for a doctor's visit might be a nominal $20, since most of the cost is covered by an insurance plan.
A nominal variable is one that describes a name, label or category without natural order. Sex and type of dwelling are examples of nominal variables.What are 3 examples of ordinal data? ›
|Expertise||Beginner Intermediate Expert|
|Education level||Primary Secondary Post-Secondary|
|Income||Low Medium High|
|Agreement level||Strongly Agree Agree Neutral Disagree Strongly Disagree|
Ordinal data classifies data while introducing an order, or ranking. For instance, measuring economic status using the hierarchy: 'wealthy', 'middle income' or 'poor. ' However, there is no clearly defined interval between these categories. Interval data classifies and ranks data but also introduces measured intervals.What is interval data? ›
Interval data is measured along a numerical scale that has equal distances between adjacent values. These distances are called 'intervals'. There is no true zero on an interval scale, which is what distinguishes it from a ratio scale.What is the difference between nominal and ordinal scales? ›
Nominal scale is a naming scale, where variables are simply “named” or labeled, with no specific order. Ordinal scale has all its variables in a specific order, beyond just naming them.What are the four scales of measurement from least to most informative? ›
There are four levels of measurement – nominal, ordinal, and interval/ratio – with nominal being the least precise and informative and interval/ratio variable being most precise and informative.What are the different types of scales explain? ›
Introduction: There are 4 types of scales, based on the extent to which scale values have the arithmetic properties of true numbers. The arithmetic proper- ties are order, equal intervals, and a true zero point. From the least to the most mathematical, the scale types are nominal, ordinal, interval, and ratio.What is interval scale measurement? ›
Interval scale refers to the level of measurement in which the attributes composing variables are measured on specific numerical scores or values and there are equal distances between attributes. The distance between any two adjacent attributes is called an interval, and intervals are always equal.How do you know if data is nominal or ordinal? ›
Nominal data is classified without a natural order or rank, whereas ordinal data has a predetermined or natural order. On the other hand, numerical or quantitative data will always be a number that can be measured.What is an example of ratio data? ›
Income, height, weight, annual sales, market share, product defect rates, time to repurchase, unemployment rate, and crime rate are examples of ratio data.
Some of the common scale systems used in Western music include: Major scales. Minor scales. Diatonic scales.What are the 3 main types of scales? ›
There are three types of scales commonly used on maps: written or verbal scale, a graphic scale, or a fractional scale.What are 3 types of scales used for measuring weight? ›
- Mechanical Weighing Scales.
- Digital Weighing Scales.
- Analytical Weighing Scales.
The type of data that is required from the respondent – ratio, interval, ordinal or nominal.What type of scales are most common? ›
Pentatonic (five-note) scales are used more widely than any other scale formation. In fact, Western art music is one of the few traditions in which pentatonic scales do not predominate.What are the 7 scales? ›
In Western music, there are seven such scales, and they are commonly known as the modes of the major scale (Ionian, Dorian, Phrygian, Lydian, Mixolydian, Aeolian, and Locrian).How many major scales are there? ›
How many major scales are there? Although there are twelve chromatic pitches within the octave to build major scales on, some of them are enharmonic. This means you'll occasionally learn a major scale whose notes can be spelled two different ways. This makes a total of fifteen major scales.