I have calculated two cross-classified multilevel models where the models have different sample sizes. I was wondering whether I can compare (like say higher or lower) ICC between these two models.
Another question, from a cross-classified multilevel model, we can calculate the ICC index for different contexts (such as students living in the zone but school zone is different, student's school in the same zone but residence is different). At least can we add the ICC index to say that this total amount of variation is due to spatial dependency?
The intraclass correlation coefficient (ICC) is a measure commonly used to assess the degree of agreement or correlation among observations within the same group or cluster. It is often employed in the context of multilevel or clustered data analysis.
When comparing ICC values between two different models, it is important to consider the specific context and purpose of the comparison. Here are a couple of scenarios to consider:
In both scenarios, it is essential to consider the specific interpretation and context of the ICC values. The magnitude and interpretation of ICC values can vary depending on the research field, the specific outcome variable being assessed, and the nature of the data.
However, it's important to note that comparing ICC values alone might not be sufficient for drawing definitive conclusions. It is advisable to consider other factors such as model fit statistics, theoretical considerations, and research objectives when making comparisons between ICC values from different models.
Additionally, it's important to ensure that the ICCs being compared are estimated using similar methods and assumptions, as different estimation methods or data structures can lead to different ICC values.
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