Multivariate Statistical Analysis Part 3: MANCOVA (with R Demonstration)
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- Опубликовано: 21 авг 2024
- This is the 3rd part of my Multivariate Statistical Analysis video series on Multivariate Analysis of Covariance (MANCOVA). MANCOVA is a method for looking at if the means of the multiple outcome variables between each group (specified by the group variable) are statistically different while controlling for a covariate which can influence the outcome but is not of study interest.
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Thank you for making these very informative videos. Looking forward to your next one on repeated measures! :)
Hi! Very helpful videos! I wonder if you could upload the rest of your videos?
Please we want to see MANCOVA with the jmv package! Thank you
Thank you so much for making this tutorial. I found it extremely helpful. Please can you show how to use MANOVA function in jmvcore package.
i would like to see a video including MANCOVA package :-) Great video!
Muchas gracias!
What should be done if we see violations of the assumptions? What could be alternative methods?
Thank you for the interesting lecture. It seems there are mistypes in the lines 72-73: not 'jmvcore' but 'jmv' is correct.
it is a good topic but i suggest you to add mulitivariate probit regression model topic in your presentation
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I am trying to add multiple covariates (n=6 covariates) in a MANCOVA model in R and I cannot find any guidance as how to go about it. My code using only one covariate is the following:
MANCOVAModel
So what's the actual difference between covariable and independent variable since they enter the formula in a same way.
Saw your video and tryed to use 'jmvcore' but din't recognize the function; with 'jmv' I made a MANCOVA :)
Please I don't see the new video you uploaded.