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
@@GradCoachI have a doubt. In interval level data equal spacing between points means difference between consecutive points be same like in arithmetic progression??.....
One of your videos was assigned for our class this week. After watching that one, I continued to watch several more. Your content is incredibly helpful. I posted your video links on our discussion board informing everyone how helpful I found them to be. Thank you for what you do!
ohh! i had to watch the video twice first fime i simply forgot to listen what she saying i just seeing her without taking my eye away, oh god she is so adorabloe and nxt time i listened and she explained very simply with simple examples. she is intelligent too🤩
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"
-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)
Thanks so much Emma. Quick Q? If I am using a Likert scale which sounds ordinal but has a numerical number attached to each rank, example Strongly Disagree = 1 and Strongly Agree = 6, would this still be ordinal data or is it now numerical data?
It is going to be ordinal; the reason is; your Likert-scale values are categorical data depending on the type of scale for example (1. very poor 2. poor 3. acceptable 4. good 5. very good) or (1. very likely 2. unlikely 3. Neutral 4. likely 5. very likely). Given the example you sighted, there is a meaningful order in the categories and it is ranked with 1 being strongly disagreed, and 6 strongly agree. Also, because you're assigning numbers to them (1,2,3,4,5) does not make it numerical. The number assigning is to allow easy analysis of the data because likert scale is mostly qualitative. Hope this helps..
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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
Thanks for that acronym :)
Ditto. I made good notes too!
I learned them with the same acronym 😂❤
This was so freaking helpful for my Data analytics grad class. This sounded like foreign in class and now i understand it
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.
Great to hear that. Good luck!
@@GradCoachI have a doubt. In interval level data equal spacing between points means difference between consecutive points be same like in arithmetic progression??.....
how is it possible that you explain this way better than any of my professors and google search could, thank you!! :)
One of your videos was assigned for our class this week. After watching that one, I continued to watch several more. Your content is incredibly helpful. I posted your video links on our discussion board informing everyone how helpful I found them to be. Thank you for what you do!
a born teacher.. what a caliber what a performance.
Thanks!
This is super detailed and straight forward. Thank you for sharing your knowledge with the world👏
Our pleasure :)
Brief and to point . Thank you
Thank you! I think your Grad Coach page is going to be my lifesaver for my research class. Bless you Emma, my knight and shinning armor
You explained it so well for my PhD exam. Love from Mumbai
This is the BEST BEST BEST video ever! So easy to follow, straight and clear! Thank you so much! 🥰😍
Thank you :)
i love the way you break it down even a Prof couldn't do better
Glad to hear that :)
I am highly impressed with your great skill of impaction of knowledge God bless you
Great teachings and explanations. Thanks to Grad Coach, I have been able to understand a lot of terms in research and data analysis. @Grad Coach.
This is my first time to understand these concepts! You're such good teachers.
Happy to hear that!
Yes I was so lost in social statistics now it makes sense!
Brilliant. Concise. Insightful.
I am loving these videos! Makes data, stats and quant so much more fun and understandable! Thanks a lot!
I love the concise but very informative...keep it up... Looking forward of more of your stats video
Most amazing explanation I ever heard, very brief and in a detailed manner. Thank you ❤
OMG I've watched so many videos, but this is the only one that made sense to me!! Thank you
I am from India , it’s really helpful
I subscribed. You didnt made us to long for knowledge , you just put it on plate clearly.
Thanks
Thankyou so much Miss For this video . This clear pot my most confusing topics in research gracefully.
Much better explanation than I got from my professor
First time watching your video and you easily cleared my doubts
Very easy to understand with easy and suitable examples .....thnkew very much for such a simple and conversant explanation
It's my pleasure
Thank you for the plain, informative, and ethically delivered content.
Our pleasure!
Amazing. LOVE THIS! Great info and delivery.
Thanks a lot for all of your fantastically understandable and greatly articulate videos!
You're very welcome!
Thank you Guys !Made it easy to digest! NOIR acronym will remind all content of this video!
What a fantastically simple but informative video. Thankyou :)
ohh! i had to watch the video twice first fime i simply forgot to listen what she saying i just seeing her without taking my eye away, oh god she is so adorabloe and nxt time i listened and she explained very simply with simple examples. she is intelligent too🤩
Thanks for being a good teacher
Great descriptions! Enjoyed the video!!!
worded in a way that's easy to understand. Thank you
thank you it was simple and full of good information and the quality was awesome!
I don't know what to say except thank you. It really helps me out
Glad it helped :)
Thanks for your lovely explanation with simple example.
Ugh the breakdown at the end. This was art to me. Thank u 🙏
Hehe, thanks!
Good job I learn a lot thanks
Great Concept Clearing Video. Thanks.
Hi I'm From india. I enjoyed the way u taught and got good information.
Thanks and welcome
Clear, concise tutorial - thank you! :D
Explained so well! Thank you!
Thank you!!!! I READY FOR MY FIRST STATS QUIZ!!!
You got this!
❤This is the best explanation I have seen. Great job!!
Glad it was helpful!
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"
You're welcome. Thanks for the suggestion.
Haw can i use quality data for research wich program can i use for this research
I much loved this vedio really it's usefull for me and everyone thanks my teachers
Glad to hear that
Very well explain. I wonder if a cheat sheet exists with breakdown examples for all types of data?
Elucidating explanations
Wow super helpful, I’m gonna nail this quiz!
Good learning. very clear and accurate. Thank you very much.😀
Just wow... Nicely explained ❤
Thanks , described so well, made the topic so easy to understand.
Fabulous explanation. So helpful.
Glad it was helpful!
Great explanation, thanks!
-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)
Thank you very much. You did a great job
Superb and simple explanation. Its indeed a great service. Keep it up
You're most welcome :)
very helpful and more interesting thank you so much .
Super !!! thank you for the clear description.
You are welcome 😊
Amazing explanation.
This is just fantastic.❤
That was a great overview, thanks!
Glad it was helpful!
Thanks for making such a great video. The info was presented and explained in a way that is easy to understand.
Super quality video 👍👍
Omg i loved the video! 😊❤❤❤❤
Tnxs a lot very helpful and interesting to watch too
You're welcome :)
Hey you are amazing teacher
Thank you! This helps a lot!
Glad it helped!
Wow amazing explanation ❤
Glad you liked it
This video is very helpful ❤
I'm so glad!
This was so helpful thank you
Glad it was helpful!
Very understandable.... Thank you
You are very welcome
It’s explained so well, thank you!!!
You're welcome :)
Thanks a lot, simple and helpful!
Glad it helped!
Thank you for this video!
Which one of the following is true about ordinal data
Much love to you Girl and to your team ❤
So helpful! Thank you!!!!
Glad it was helpful!
what if the data is in logarithmic scale so the spaces between points are not equal?
Excellent video.
Thank you very much!
Great Video! Thanks for sharing
Thank you ma'am
Nice explanation❤
Thank you 🙂
best video! Thank you!!!!!!
You're welcome!
Soooo helpful thank you!
thanks u made it so easy
You're welcome!
NICELY DONE!!
Hi, can I ask, for true and false questions, it should be categorized as nominal or ordinal?
Nominal
Excellent
you are Lovely.I like your way of explanation.
Thank you! 😃
Thank you ❤
You're welcome 😊
Nice tutorials
Glad you like them!
Thank you! This was super helpful.
Glad it was helpful!
Insightful
Here. before examinations tomorrow
Thanks so much Emma. Quick Q? If I am using a Likert scale which sounds ordinal but has a numerical number attached to each rank, example Strongly Disagree = 1 and Strongly Agree = 6, would this still be ordinal data or is it now numerical data?
Yes. Good luck.
It is going to be ordinal; the reason is; your Likert-scale values are categorical data depending on the type of scale for example (1. very poor 2. poor 3. acceptable 4. good 5. very good) or (1. very likely 2. unlikely 3. Neutral 4. likely 5. very likely). Given the example you sighted, there is a meaningful order in the categories and it is ranked with 1 being strongly disagreed, and 6 strongly agree.
Also, because you're assigning numbers to them (1,2,3,4,5) does not make it numerical. The number assigning is to allow easy analysis of the data because likert scale is mostly qualitative.
Hope this helps..
I observed some researches treating likert scale as interval data...But formally likert scale is ordinal data