thank you so much for this!!! I just was NOT understanding confounding variables but you made it so easy so thank you sincerely from the bottom of my heart! -a psychology student
I think a confounding variable is an extraneous variable (non-treatment) variable which we are not testing in our experiment / study but it (the confounding / extraneous variable) has an effect on the response variable. I will be glad if I'm corrected but that's how I understand this concept. Thank you from Uganda East Africa
Firstly. Thank you Liz for this, you saved my Life. Put playback speed at 1.5x if you are native speaker. Put playback speed at 1.25x if English is second language. Thank me later
I am wondering does the present of confounding always mean a spurious association between risk factor and outcome? Is it possible that confounding can also mask the association between them?
The arrows are correct. in this example she was saying that’s it’s a negative (inverse) correlation, meaning that the younger you are the less likely you’re getting MI, and the more you engage in physical activity the less likely you’re of getting MI
Association does not imply causation! This is what my statistic book has written down on every page. How come you are throwing this causes this and that causes this all over the place? :)
These examples are so cut and clear that your argument is basically invalid. But yes sometimes it can be difficult to deem something an association or causation.
Your example with age is throwing me off. Usually age is an effect modifier. Is it because you portrayed age as a dichotemous variable i.e young or not young that it works? Age and physical exercise would be a continuum spectrum where physical activity would drop gradually as age increases, therefore this is a bad example since there is no singular point where you suddenly shift from being young to not being young anymore. Age is almost always an effect modifier in my opinion, as effect modifiers are usually biologically rooted.
thank you so much for this!!! I just was NOT understanding confounding variables but you made it so easy so thank you sincerely from the bottom of my heart! -a psychology student
Put playback speed at 1.25x
Thank me later
I'd say 1.5x. And it's still too slow..
I would say 2x
I think a confounding variable is an extraneous variable (non-treatment) variable which we are not testing in our experiment / study but it (the confounding / extraneous variable) has an effect on the response variable. I will be glad if I'm corrected but that's how I understand this concept.
Thank you from Uganda East Africa
Oh my goodness, you take the absolute sting out of epidemiology. Thank you!
Thank you so much for posting these videos! Very well explained and clear. It will definitely help me doing my Epidemiology exam. ;)
Madam you are truly extraordinary! Very well and clearly explained!
Excellent video! Liked how it's clear regarding the issue of establishing causal relationships! :)
Come before my epid exam. Clear and helpful. Thank you!
At last i came to understand the concept of confounding.. thank you indeed
Thank you!! Very excellent video
Best explanation ever! Thank you so much
Thank you so much!! That was made so easy to understand xx
It was great explained, this really helps me to complete my assignment. Thank you for making this video.
Awesome explanation
Thank you so much for explaining ❤️❤️ anyone else from 2024 😍??
This was amazing, thank you!
Super helpful... really appreciate the effort
Awesome! Thanks.
Thank you, understood it better watching this video
Omg I love u after like 8 years... u just saved my test
Great!! Helpful. Thanks
classic explanation
Great explanation... Many thanks.
Excellent video, thanks..
I was looking through my playlist from when I was in 2nd-5th grade and I came across this- I’m honestly so confused and concerned
Best explanation ever!!!!! 🤩🤩🤩🤩🤩
thank you u are so good at explaining that i understood just with the first example thank you so much
Thank you Elizabeth! greatly appreciated! Do you have any videos for Effect modifier by any chance?
thank you, i hope my doctor teach like you
Best explanation ever
last two examples cleared everything
really merci ...v beutiful videos
Firstly. Thank you Liz for this, you saved my Life.
Put playback speed at 1.5x
if you are native speaker.
Put playback speed at 1.25x
if English is second language.
Thank me later
I'm not a native speaker. I put it at 1.75x
Excellent 👌👍
Thank you a lot . its so helpful
thank you so much :D
WTH--i mean look how good you are--thanks alot
thank you, I was about to give up.
clearly explained.
Thanks for a good video. Not sure if the arrow from smoking coronary heart disease should be double though.
Agreed - coronary heart disease does not cause smoking so it should be a one way arrow. Otherwise this is very good.
Nice explanation thank you mam
I WISH you were my professor. Mine is so bland. I like your teaching
total? partial? and balanced confounding? please :)
Thanks mam
What kind of variable now blood pressure is .....
Which is caused by during experiment
thank you
Where were you? I finally find my place to rest. Thank you so much
Well done
Could it be both?
Isn't fluid intake related to blood pressure though?
Thanku mam
how does confounding variable affect the validity of the study?
good vid
Can you explain about blocking variable in statistics, please?
How do I include confounding in a review question?
I am wondering does the present of confounding always mean a spurious association between risk factor and outcome? Is it possible that confounding can also mask the association between them?
It can! It just depends on which relattionships are positive and which ones are negative.
Thanks
I am wondering whether those arrow directions are right, in particular to physical activity and age
Looks like there is an error in there
The arrows are correct. in this example she was saying that’s it’s a negative (inverse) correlation, meaning that the younger you are the less likely you’re getting MI, and the more you engage in physical activity the less likely you’re of getting MI
The confounding factor is that younger people are more likely to to exercise so it’s hard to tell which of these two is protective from MI
Yeah they were wrong- she drew young age as a mediator. To be a confounder you need arrows pointing from young age to both physical activity and MI
@@aidangollaglee3531 i agree you right. i was thinking like that.
wouldn't age and physical activity be negatively related. As age goes up, physical activity goes down?
I'm 68 and planning on competing in the olympics, I see a positive relationship between age and physical activity.
Can u explain confounding in handedness
Last two examples confused me again . Its not an easy task when you are doing confounding, mediation and interaction simultaneously
Association does not imply causation! This is what my statistic book has written down on every page. How come you are throwing this causes this and that causes this all over the place? :)
These examples are so cut and clear that your argument is basically invalid. But yes sometimes it can be difficult to deem something an association or causation.
Your example with age is throwing me off. Usually age is an effect modifier. Is it because you portrayed age as a dichotemous variable i.e young or not young that it works? Age and physical exercise would be a continuum spectrum where physical activity would drop gradually as age increases, therefore this is a bad example since there is no singular point where you suddenly shift from being young to not being young anymore. Age is almost always an effect modifier in my opinion, as effect modifiers are usually biologically rooted.