Great explanation! I find interesting that in this explanation it may be implied that random effects models (aka multilevel or mixed effects models) may be favoured to fixed effect ones, which instead through a lot of information away. Some researchers especially in econometrics instead would make the distinction between FE and RE models (rather than random and fixed effects) and favour fixed effects
Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. Because data allows you to measure, you will be able to establish baselines, find benchmarks and set performance goals. A baseline is what a certain area looks like before a particular solution is implemented.
Hey! thank you so much for this explanation it was truly helpful. I was wondering if you could answer a question I had about the topic. What if you wrongly assume a factor to be of random effect how would that affect your results if at all?
sad truth is that I did mixed models once for a publication and one of the reviewers said the statistics section is hard to understand and not common, so i should use anova instead... cheers to the standards of nowadays science edit: After submitting to a journal in another field where I knew from a colleague that the standards in statistics are a little higher, I had no problems anymore.
That is why we have many independent variables to capture the random effect.. but what i was expecting how these fixed vs random effecting impacting the model.. where we already tried using many independent variables
Hi Sir, if Hausman test indicates that fixed model is more appropriate than random effect model, and if in that case, in data time period (T) > cross section units (N), which FEM is to be chosen: time (T) FEM or Cross section (N) FEM?
Firstly, i would like to thanks for you interesting study, my data have two land uses(exclosure and non exclosure) with three site in each land use how to arrange my data and make analysis using liner mixed effect model
You talk about dependence within individuals. Why can you not include a dummy variable for each individual and, if desired, an interaction of this dummy variable with the covariate? This is a FE model. What is the value in pretending that the individual parameters follow a normal distribution (when they might not)?
Hi Chris, the approach you describe only works if "sphericity" is satisfied, for which you need equal variation on the dependent variable for each cluster (each individual in this case). While Mauchly's test tries to identify whether sphericity is violated, a mixed model assigning a random effect to the clustering variable avoids this requirement
I love you Tom, you managed to explain this incredibly important point to me in such an eloquent manner that I finally understand its significance!
love him too
Excellent explanation of effects in statistical models! Huge thanks Tom, you are the best!
This is brilliantly done. Wonderful presentation!
This is really well done! Great job Tom Reader!
Extremely good video, Mr. Reader. Thank you so much.
Great explanation! I find interesting that in this explanation it may be implied that random effects models (aka multilevel or mixed effects models) may be favoured to fixed effect ones, which instead through a lot of information away. Some researchers especially in econometrics instead would make the distinction between FE and RE models (rather than random and fixed effects) and favour fixed effects
This was fabulous! I really enjoy your style of presenting. It is clear, challenging, and well-crafted.
No! We need the mixed effect model video. This is the clearest explanation I've heard.
Such a great explanation and I finally understood this importing thing
Really clear explanation! Thank you!
Excellent video and crystal clear explanation
Great Video! Please upload the Mixed Effects one
Great video! Well explained, thank you. I wonder if at 6:00 it is going about the random effects and not bias measurement? Thanks!
Such a clear explanation! Very helpful.
Hi, I'm From Comoros. Thanks for the video, it was crystal clear !!!
Thank you for this clear explanation!
Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. Because data allows you to measure, you will be able to establish baselines, find benchmarks and set performance goals. A baseline is what a certain area looks like before a particular solution is implemented.
Hey! thank you so much for this explanation it was truly helpful. I was wondering if you could answer a question I had about the topic. What if you wrongly assume a factor to be of random effect how would that affect your results if at all?
That was very clear. Thanks a lot!
What an excelent video, thank you very much
Thanks Tom. Great explanation
Thank you for the explanation, this video was very easy to understand!
Very clear explanation . Thankyou !
What an awesome video! Thank you!
Very clearly explained, cheers
Amazing explanation! Would love to learn more from this professor!! Please upload more tutorials from him
Thank you so much for simplyfing such topic.
Thx Tom, great explaination :) and well pronounced btw!
Hello!! can one use a fixed effect regression on a cross-sectional dataset, if yes how?
Big up the top g Tom, shelling stats like it's Mario Kart. GG
sad truth is that I did mixed models once for a publication and one of the reviewers said the statistics section is hard to understand and not common, so i should use anova instead... cheers to the standards of nowadays science
edit: After submitting to a journal in another field where I knew from a colleague that the standards in statistics are a little higher, I had no problems anymore.
Its a major limitation of the peer review process...
Great Lectures. Many thanks. Is there a sequel into explaining more about Mixed models
Amazing explanation! I wonder if the video about mixed models is already out? I could not find it under the youtube page of Univ. of Nottingham...
We found these other videos with Tom Reader ua-cam.com/video/z45LUip6RcI/v-deo.html and ua-cam.com/video/PyNzbDbjs1Y/v-deo.html if they help at all.
Fantastic! Thank you!
great presentation!
Thank you so much!
Thank you, sir
That is why we have many independent variables to capture the random effect.. but what i was expecting how these fixed vs random effecting impacting the model.. where we already tried using many independent variables
Thank you sir
Thank you very much, sir
Superb , lucid presentation on an all too often neglected topic in stats.
very well explained
Great theoretical background
great
Hope there was a link to the next video for the mixed model
Sir, please give the lectures in written form also
very good
Hi Sir, if Hausman test indicates that fixed model is more appropriate than random effect model, and if in that case, in data time period (T) > cross section units (N), which FEM is to be chosen: time (T) FEM or Cross section (N) FEM?
Firstly, i would like to thanks for you interesting study, my data have two land uses(exclosure and non exclosure) with three site in each land use how to arrange my data and make analysis using liner mixed effect model
can "nurse" be treated as a random effects if there are only 2 nurses?
You talk about dependence within individuals. Why can you not include a dummy variable for each individual and, if desired, an interaction of this dummy variable with the covariate? This is a FE model. What is the value in pretending that the individual parameters follow a normal distribution (when they might not)?
Hi Chris, the approach you describe only works if "sphericity" is satisfied, for which you need equal variation on the dependent variable for each cluster (each individual in this case). While Mauchly's test tries to identify whether sphericity is violated, a mixed model assigning a random effect to the clustering variable avoids this requirement