Levels of variation and intraclass correlation

When working with multiple data sets, it is important to analyze the level at which individual observations vary. This can be done by calculating intraclass correlation (ICC) to quantify the variances. Using profitability data as an example, there are three levels of variation that can affect a data set: year-to-year variation within companies, variation between companies, and variation between industries.

In a small data set, graphical analysis can be used to identify patterns, while in larger data sets with manageable numbers of clusters, box plots can be used to understand between and within variances. The ICC(1) is calculated as the variance between groups divided by the total variance, helping to determine how much of the variation in the data is attributed to the groups and how much is within the groups. ICC(1) values close to 0 indicate no meaningful clustering, and values close to 1 indicate no variance within clusters. In cases where ICC(1) is around 0.5, multi-level modeling may be needed to account for the levels in the analysis.

[seeunkim]

Thank you so much for your clarification, I am being helped organizing the concepts again by your explanation!!

[Onnuuzeln]

Very clear explanation, Mr. Rönkkö. Please continue making videos, you're good at this.

[aljoscha]

Fantastic video. Very good examples and overall a clear presentation style. Thank you!

[EricGao-qs]

I am struggling in understanding multilevel modeling used in the paper and your videos really help me out with good example and clear explanation. Thanks for the amazing video

[msjahnavi]

Thank you so much for your explanation, sir. This video was very helpful!

[DasypusN]

Like the others here I appreciate the clarity. Thanks for making this video.

[summertummer]

Brilliant! You were very clear. Where can I find out about the other types of ICC?

[bignatesbookreviews]

would it be correct to say this? "multilevel models deal with separate distributions within larger, more general distributions, to the nth number of levels" thank you for these videos, they are finally helping me understand multilevel modelling!!

[butterbee_bb]

Great explanation, thank you for posting

[radekgalabov]

Hi, great video, finally I got it!

But I think there may be a mistake in the video. Shouldn't the means on the plot at 7:25 be all at the same level?

[zhezhang]

Thank you Mikko, It is really clear. Could you explain a little bit about the relationship between ICC and repeatability?

[christoffer]

Thank you for a nice video! When I calculate the variance of the 5 group means I dont get 0.00033, I get: var(c(0.220, 0.183, 0.222, 0.220, 0.236)) = 0.00039. How is the 0.00033 calculated?

[brazilfootball]

So, an ICC of 0.2 (for example) indicates most of the variation is likely coming from the variance within groups?

[cellerism]

Hi Mikko at @ 7:31 you say there is not variance between groups. Do you mean there is total variance as at at ICC1 = 1 it says no variance on the slide.

[beesanwarasna]

Should not the intraclass correlation be (within groups) and not between groups?

[jackbryant]

Hi Mikko, at 6:04 how are you calculating the within variance from the group mean centred data, I can't seem to work out how you get that value?