Nominal, Ordinal, Interval & Ratio Data: Simple Explanation With Examples

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I am on my way to meet my tutor for research methods in Psyc. I took Stats last semester but for some reason levels of measurement never took root. I just watched your utterly fantastic video, made concise notes, and I now have a working understanding of LOM!! Thank you. ❤🌟Also, I always forgot their order until I realized the concept had the best acronym ever:NOIR

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Ditto. I made good notes too!

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I learned them with the same acronym 😂❤

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I am a person who is getting back to Uni after 16 years for a masters in data analytics. You guys have explained the concepts really well. Thank you.

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Great to hear that. Good luck!

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​@@GradCoachI have a doubt. In interval level data equal spacing between points means difference between consecutive points be same like in arithmetic progression??.....

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This was so freaking helpful for my Data analytics grad class. This sounded like foreign in class and now i understand it

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Brief and to point . Thank you

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Thanks

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Thank you Guys !Made it easy to digest! NOIR acronym will remind all content of this video!

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Great descriptions! Enjoyed the video!!!

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worded in a way that's easy to understand. Thank you

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[@tyto811]

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[@Hsm140]

Hello emma, thanks for good explanation on the four levels of measurement in statistics i.e nominal, ordinal, interval and ratio data, keep more coming, then, can you kindly show a video on "clinical research on drug efficacy"

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You're welcome. Thanks for the suggestion.

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Very well explain. I wonder if a cheat sheet exists with breakdown examples for all types of data?

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Nice explanation❤

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Thank you 🙂

[@sghaierlulu7307]

-Types of data +how each type should be measured:(hierarchical order)
-***Categorical: (only nominal(no order) or ordinal(categories but ordered)
1-Nominal:the data is put into categories. The data type is categorical. no category is better than other, there is No inherent value,order or rank between catgories(such as gender,ethnicity,color…)
2-Ordinal:the data is put into categories but these categories of data have a Natural order or ranking between the options. But it is the same as nominal in categories(agree or disagree).
Example:
-Income levels(low income,medium income,high income).
-levels of agreement(disagree,neutral,agree)
-levels of average(poor,average,excellent)
You can’t numerically measure the differences between the options because they are categories. But u can rank/order the categories.
Numerical:scale data=Quantative: data measured in numbers/ can be ordered/naturally numerical+ 0 points whether arbitrary or not)
1-Interval: (numerical,ordered,equal distance between points;measurable; the spaces between measurements are equal)
Data is naturally numerical and ordered. But This type of data have something in common is that their zero point is arbitrary. For example: (you can measure the distance between points)
0 value is arbitrary.(0 doesnt mean nothing, it doesnt reflect 0)
0 in fareinheit, doesn’t mean 0 temperature but its cold.
2-Ratio data:The 🤴🏻 king (numerical,ordered,equal distance between points(measurable)and 0 is meaningful)
The data is naturally numerical,ordered , and the data 0 value means literally 0. For example: the variables : weight,height,length or temperature in kelvin, length of time(duration).
0 weight means weightless./ 0 time means 0 duration.(absolute zero value)
In spss, in the measure we have 3: nominal, order(categorical) or scale(numerical)

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