no he entendido un carajo de lo que haz dicho (no entiendo el inglés) pero estos 4 minutos han sido mejores que la hora de mi profesor. Muchas gracias.
This is pretty quick and effective it seems. Understanding the formula and how it works in depth surely takes more than 5 minutes, but it sure saves some work lmao Thank you for this
Thank you so much! Problem is, I don't have a "YES" or "NO" answer from each rater. I have a grade of 1-5 given by each rater. Can I still calculate Kappa?
The explanation is quite clear, the numbers can be a bit optimized. Agreement: 63.1578947368421%, Cohen’s k: 0.10738255033557026. Thanks for the video!
How to calculate the agreement between "strongly disagree,disagree,agree,strongly disagree" , what is the formula only to calculate 'observed agreement'
The only thing I do not understand is the "Chance Agreements", the AC calculation of .58. I understand where the numbers come from, but I do not understand the theory behind why the arithmetic works to give us this concept of "chance" agreement. All of the numbers in the table are what was observed to have happened...how can we just take some of the values in the table and call it "chance" agreement? Where is the actual proof they agreed by chance in .58 of the cases?
for the "chance" of agreement, we are essentially multiplying the probability of rater 1 saying yes and the probability of rater 2 saying yes and doing the same for the no(s). The same way you would calculate the "chances" of getting both heads on two coins, we would multiply the probability of obtaining heads in coin 1 (0.5) and the probability of obtaining heads in coin 2 (0.5). The chance of us obtaining heads by mere luck for both is hence 0.25, the same way the chance of the two raters agreeing by chance is 0.58
i did my own research and figured id post it here in case anyone ever has the same question. taken from the source, article name "interrater reliability: the kappa statistic" it reads... Similar to correlation coefficients, it can range from −1 to +1, where 0 represents the amount of agreement that can be expected from random chance, and 1 represents perfect agreement between the raters. While kappa values below 0 are possible, Cohen notes they are unlikely in practice (8). As with all correlation statistics, the kappa is a standardized value and thus is interpreted the same across multiple studies. Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01-0.20 as none to slight, 0.21-0.40 as fair, 0.41- 0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as almost perfect agreement.
Thank you. If I have data with high agreement between both observers, should i choose the results of any one of the raters or should i use the mean of rating of both?
I have a very strange Kappa result: I have checked for a certain behavior in fotage of animals, which I have assessed twice. For 28 animals, I have agreed 27 times that the behavior is present and have disagreed only once (the behavior was present in the first assessment, but not in the second). My data is organized as the following matrix: 0 1 0 27 And that gives me a Kappa value of zero which I find very strange because in only 1 of 28 assessments I disagree. How come it is considered these results as pure chance?
Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01-0.20 as none to slight, 0.21-0.40 as fair, 0.41- 0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as almost perfect agreement.
no he entendido un carajo de lo que haz dicho (no entiendo el inglés) pero estos 4 minutos han sido mejores que la hora de mi profesor. Muchas gracias.
Simple, very well explained, nicely presented, clear voice. Excellent, thank you so much, this video is very useful.
So helpful, watching a 4.5 min video sure beats a 50 minute lecture
try 2 hours..
this was VERY helpful and simplified the concept.. thank you. please do more videos !
This is pretty quick and effective it seems.
Understanding the formula and how it works in depth surely takes more than 5 minutes, but it sure saves some work lmao
Thank you for this
What I though its not possible to understand, needed only a great 4 minute video to understand. Thanks a lot!
Great explanation (with nice sketches as a bonus)- thank you!
great concise explanation thank you. I will be passing this on
accurate, sharp and on the point. thank you sir! :)
Thank you so much! Problem is, I don't have a "YES" or "NO" answer from each rater. I have a grade of 1-5 given by each rater. Can I still calculate Kappa?
Thank you . You make it easy
This was made so clear thank you!
The explanation is quite clear, the numbers can be a bit optimized. Agreement: 63.1578947368421%,
Cohen’s k: 0.10738255033557026. Thanks for the video!
How to calculate the agreement between "strongly disagree,disagree,agree,strongly disagree" , what is the formula only to calculate 'observed agreement'
A very well explained topic
I like this video so much, you explained it very clearly. Thank you
Thanks so much, VERY helpful and simplified the concept
Superb explanation. Thanks sir.
The only thing I do not understand is the "Chance Agreements", the AC calculation of .58. I understand where the numbers come from, but I do not understand the theory behind why the arithmetic works to give us this concept of "chance" agreement. All of the numbers in the table are what was observed to have happened...how can we just take some of the values in the table and call it "chance" agreement? Where is the actual proof they agreed by chance in .58 of the cases?
for the "chance" of agreement, we are essentially multiplying the probability of rater 1 saying yes and the probability of rater 2 saying yes and doing the same for the no(s). The same way you would calculate the "chances" of getting both heads on two coins, we would multiply the probability of obtaining heads in coin 1 (0.5) and the probability of obtaining heads in coin 2 (0.5). The chance of us obtaining heads by mere luck for both is hence 0.25, the same way the chance of the two raters agreeing by chance is 0.58
just awesome !!! THANKS . Plz make more such video regarding biostatistics.
Still not clear how come to the final Kappa equation? Why (OA-AC)? Why divided by (1-AC)? The rationale is obscure to me.
very good simple clear video
what is the range of kappa values that indicate good agreement and low agreement?
i did my own research and figured id post it here in case anyone ever has the same question. taken from the source, article name "interrater reliability: the kappa statistic"
it reads...
Similar to correlation coefficients, it can range from −1 to +1, where 0 represents the amount of agreement that can be expected from random chance, and 1 represents perfect agreement between the raters. While kappa values below 0 are possible, Cohen notes they are unlikely in practice (8). As with all correlation statistics, the kappa is a standardized value and thus is interpreted the same across multiple studies.
Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01-0.20 as none to slight, 0.21-0.40 as fair, 0.41- 0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as almost perfect agreement.
can you please eloborate on the meaning of a high or low Kappa value? I can now calculate kappa, but what does it mean?
A bit late. But in school we learned the following.
how about weighted kappa?
Thank you. If I have data with high agreement between both observers, should i choose the results of any one of the raters or should i use the mean of rating of both?
Question please:
We are supposed to do kappa scoring for dentistry but we have 5 graders. How do we do such thing?
+Danise Candeloza Fleiss' Kappa
This is phenomenal
Great explication. Thanks
a lot.
I have a very strange Kappa result: I have checked for a certain behavior in fotage of animals, which I have assessed twice. For 28 animals, I have agreed 27 times that the behavior is present and have disagreed only once (the behavior was present in the first assessment, but not in the second). My data is organized as the following matrix:
0 1
0 27
And that gives me a Kappa value of zero which I find very strange because in only 1 of 28 assessments I disagree. How come it is considered these results as pure chance?
i am wondering the same thing
I think that's the answer: pubmed.ncbi.nlm.nih.gov/2348207/
Great video. Could you also explain if 0.12 is significant or not?
Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01-0.20 as none to slight, 0.21-0.40 as fair, 0.41- 0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as almost perfect agreement.
thank you for your clear explanation.
Thank you for this video!
Danke, hat mir geholfen! :)
thank you, you help me so much
Well explained. Thank you sir
It a value clock-agreement chart.
Great explanation!!!!
Awesome. can you make more of such video. the are so simple & nice.
thanks
Thank you. More videos please :-)
great explanation
Gracias teacher lo he entendido :)
this video is so good
thank you, this is really helping.
Thank you!
Thank you
great..thank you
THANK U!!!
thank you for yourvideo
👍♥️♥️🐾
love u