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This is my first post here so apologies for the rather basic question, but after days of reading I still can't find a satisfactory answer. I want to run a cross-lagged panel model on three waves of data to look at interactions and stability effects over time between drunkenness and illicit drug use in a normal early-adolescent population. Both these behaviours were measured on an ordinal count scale, e.g. Never=0, Once last year=1, and so on up to Daily=5. In the first two waves at least (N=1300), there are approximately 95% zero scores and the positive scores are bunched heavily around the lower scores, e.g. 15 x 1s, 5 x 2s, 4 x 3s, 3 x 4s and just 1 x 5-score.

Because of the zero-inflation and under-dispersion I looked at zero-inflated poisson link function in the cross-lagged panel model, but I have two concerns/questions about this: 1) would it adequately model the extreme amount of zeros? If not, what other models could I consider? 2) presuming teh ZIP-function is the most appropriate, how to interpret the two separate coefficients for each aspect of the cross-lagged model?

Or should I use an ordinal link function? Any advice on how to proceed is very welcome!

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First of all the zero-inflated poisson is not a link function, it is an error distribution. Secondly, your data are not counts but ordinal scores, the numbers assigned are purely arbitrary. As a result, count models are inappropriate for this type of data.

Ordinal logistic regression is what you are looking for.

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  • $\begingroup$ Thanks for your reply @Knarpie. Would the ordinal regression still work though with the very heavy skew I have ? E.g. where 3 of the ordinal categories have less than 5 observations each and over 95% of observations are in the "0" category (total sample is 1300). $\endgroup$
    – Russell
    Mar 13, 2017 at 12:30
  • $\begingroup$ That could be a problem indeed, maybe you can pool the higher categories together, e.g. into "1" and ">1"? $\endgroup$
    – Knarpie
    Mar 13, 2017 at 19:48

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