Scales of Measurement in Statistics - Nominal, Ordinal, Interval, Ratio | Level of Measurement

this short video explains the different level of measurement nominal ordinal interval and ratio and how they are classified into categories like quantitative data and qualitative data so please watch this video till the end so before we understand the different level of measurement let us first understand what do we mean by data so data is a collection of facts such as the values of the measurement or the information which can be used for the purpose of analysis to analyze and make decisions about certain business like sales or target we need to collect data the source of

data can be either be a primary data or a secondary data so primary data are the real time first-hand data collected from the surveys questionnaires experiments observations or any interviews while the secondary data is the data that uses the already available information so it can be from the books the journals the browsers or anything information that is available on the google for research now we can classify the data broadly into two categories one is the quantitative data another is the qualitative data under quantitative we have discrete and continuous type of data so discrete data can

only take discrete values or things that can be counted in a whole number so those values cannot be subverted meaningfully like if you take example like we have three heads here of green red and blue so that is the nodal number of hat is three so we cannot break this into two and a half or one and a half another example could be the number of students in the class now the continuous scale uh is the data that we can calculate so it could be something which you can measure like in this case the measurement

is coming as 27.75 or if you are measuring the temperature that is also in the continuous scale if you talk about qualitative data that is basically the categorical data so under categorical like say if we have the male and the female categories or the types or the color of the hat so that is the categories in which we can divide our data another example let's take to understand the difference let's say we have this dog if we talk about the difference between qualitative and quantitative so let's start with qualitative first so he is brown and

black in color so that is the quality that we are of data that we are collecting he has long hair and probably a lot of energy so that is the qualitative data that we are collecting here in terms of quantitative like he has four leg he has a one tail and that is the discrete data now if i talk about continuous data it could be his weight and the height so that is the how we categorize discrete data continuous data under quantitative and categorical data under qualitative in statistics we have four level of measurement starting

from nominal ordinal interval and ratio these are also called the scale of measurement which tells us how precisely the variables are recorded each scale is the incremental level starting from nominal which is the first level of measurement then we have the ordinal which is the second level of measurement interval is a third level of measurement and the finally is a ratio which is the fourth level of measurement so these level defines which statistical test type to be used now let us see each of these levels one by one in more detail with some relevant examples

let's start with the first level of measurement nominal this is probably the simplest of the four level of measurement which describes a variable that do not follow in natural order or ranking all of these scales are mutually exclusive that means there is no overlap here nominal scale is a naming scale where the variables are simply named or labeled without any specific order and it cannot be ordered or measured nominal data can be qualitative and qualitative both for instance a customer survey it was asked which brand of smartphone do you prefer an apple phone a samsung

or a nokia in this case only the names of the brand are significant for researchers not any order of the preference other type of example could be collecting what is the gender male or female so we are specifying the category here not the order or where do you live in subs in cities or a town so these are some of the examples so other examples could be the type of pet you own dog cat fish type of a house so these are some of the nominal examples the second level of measurement is the ordinal scale

ordinal sound similar to the order which follows a natural order which is exactly the purpose of this scale due to which there is no fixed start or true zero ordinal scale is one where the order definitely matters but not the difference between the values ordinal scale can be presented in the tabular or a graphical form it is exactly like the nominal skill but with specific orders let's take an example here uh the best could be the hot hotter or hottest cold so these are like we have a definite order here with some nominal features or

how satisfied are you with our service like strongly agree agree neutral disagree or strongly disagree so in both these cases you will see there is some specific natural order here like strongly agree and angry uh neutral and disagree so it follows a natural order but the difference between the values is not known here the third level of measurement is the interval scale so interval in itself means a space in between which is an important thing to remember like interval scales do not only tell us about the order but also about the values between each item

so interval scale contain all the properties of ordinal scale in addition to which it offers a calculation of a difference between the particular values so interval scale are numerical scales in which we both know the order and the exact difference between the values but the only drawback that it have is that there is no predefined starting point or true zero value the classical examples is the celsius temperature scale where the difference between each value is same like for example the difference between 20 degree and 30 degree is 10 degree and same is the difference between

60 degree and 70 degree that is 10 degree so in this case 10 degree plus 10 degree is equal to 20 degree so there is no problem here but 20 degree is not as hot as the 10 degree so apart from temperature scale the time and the calendar years that we have also follow the interval scale the fourth or the fun level of measurement is the ratio scale so ratio are basically the ultimate nirvana's when it comes to data measurement skill because they do not only tell us about the order but also tell us about

the exact difference between the values and also an absolute zero so we have three things here the order the exact difference between the values and the absolute zero ratio scale is defined as in variable which not only produce the order of variable but also makes the meaningful difference between the variables a ratio variable has all the properties of an interval variable and also has a clear definition of absolute zero so if the interval scale are amazing but they do not have the absolute zero which is why the ratio scale come into picture very good examples

of ratio scale include like height weight and length measurement so example like if we are measuring the height of two people one with three feet and one with six feet so we have all the properties here which is exactly same as the interval scale except the zero for example the weight of zero does not exist but the age of zero also does not exist on the other hand when we talk about the temperature which is not an ratio scale but zero exists like 0 at 0 degree it is a freezing point so we have this

age height and weight which are typical examples of ratio let's do a quick summary the first characteristics that we have is the measure of central tendency under nominal it would be mode ordinal mode and median interval we can have the mode median and arithmetic mean ratio we can have the mode median arithmetic mean and geometric mean difference between the variables can be evaluated in nominal it is not possible even in ordinal interval you can evaluate in ratio yes addition and subtraction of variables which is not possible in nominal ordinary interval and ratio it is possible

multiplication and division of variables not possible in nominal ordinal an interval and ratio it is possible last is the absolute zero which is not possible in nominal ordinal and interval but it's possible only in the ratio so i think we have covered what is nominal ordinal interval ratio let's do a quick check to understand the concept read these questions and you can leave your answers in the comment section below so if we have to find out the iq of 10 people now what level of measurement that would be nominal ordinal interval and ratio second question

could be list of popular video games determine the level of measurement nominal ordinal interval and ratio and third age of students nominal ordinal interval and ratio so with this given information just try to figure out what is the level of measurement for all these three questions and you can leave your answers or comments in the comment section below so that is all i have on this video if you like it click here to subscribe do hit the like button as well share this video with all your friends on different social media platforms and if you

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