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In doing some multilevel modeling, my ICC appears to be quite low (barely .01). My thought is that it would be foolish to run a multilevel model with such a low ICC. To still acccount for this very slight effect of clusters, I was thinking I should run the analysis in TYPE=Complex as conceptually and by design there are in fact clusters. Please tell me if that's logical enough. |
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It is not the size of the intraclass correlation alone that determines whether non-indpendence of observations needs to be accounted for. It is the design effect which is a function of the size of the intraclass correlation and average cluster size. TYPE=TWOLEVEL and TYPE=COMPLEX are two different ways to take non-independence of observations into account. With TWOLEVEL, non-independence of observations is modeled. With COMPLEX, it is taken into account. The choice depends on whether you want to know something about the cluster-level parameters or just control for non-independence of observations. |
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Thanks Linda! |
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Hi Lucy, I would also calculate the Design Effect which is based on the ICC and the average cluster size, where DEFF = 1 + (Average cluster size - 1)*ICC. If you find that DEFF >= 2, then you should address the clustering. |
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Note that this formula holds for means with equal cluster sizes. It is probably a decent approximation in other cases though. |
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Lucy Morgan posted on Tuesday, February 03, 2015 - 3:44 am
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Hi I hope this hasn't been answered elsewhere (I did have a good search first!). My study was designed to be multilevel, measuring care assistant variables (Level 1) within nursing homes (manager variables - level 2). The ICCs for dependent variables are very low (.062 and .086), both DEFF values are < 2 and all manager (L2) variables are non-significant predictors so I have decided that multilevel analysis is not suitable. I have then run the analyses with TYPE = COMPLEX to account for the clustered data but this in fact makes the overall model fit worse. My questions are 1) am i correct to get rid of multilevel modelling given the low ICC AND low DEFF? And would it be better NOT using the TYPE = COMPLEX analysis since it makes model fit worse? (In another post you state that TYPE = COMPLEX is the right analysis to use with clustered data but in that instance the person posting found that model fit improved using TYPE = COMPLEX) Many thanks for your help Lucy |
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I think you should use Type=Complex if you have at least 20 clusters. |
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Lucy Morgan posted on Wednesday, February 04, 2015 - 1:27 am
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Hi, thanks so much. I have 38 clusters so will go ahead with Type = complex analyses. And thanks so much for this forum and your speedy and helpful responses, this is an invaluable resource! |
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Hi, is there a citation for the need to address clustering when DEFF >=2 but ICCs are low? What about the reverse: high ICCs and low DEFF? Also, does non-independence mean that there is sufficient between group variation to say that the measure distinguishes among clusters? |
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Q1: Not that I know of. Q2: With DEFF = V_C/V_SRS = 1+(s-1)*rho, a high ICC (rho) and a low DEFF can happen for a low cluster size, say s = 2. Q3: A high ICC says that the measure has a relatively large variation across clusters, so yes, that can perhaps be seen as distinguishing between clusters. I think the important aspect is how different the SEs are when assuming independent clusters (Type=General) versus when allowing for cluster variation using Type=Twolevel or Type=Complex. |
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Thanks so much, Dr. Muthen! |
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