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I have count data measuring the wildlife (animals) crossing a passage. A modification was made to the connecting passage. I want to measure whether this affected the rate of wildlife crossings.

The event data are taken daily over the course of 36 months: 18 months before the modifications until 18-months after the modifications. The species of the animal is measured as well. The frequencies are non-normal. The same animal may cross more than once. Due to the distribution of these data and the possibility for dependence, I don't think the T-test is an appropriate method to compare pre- and post- wildlife crossing rates.

  1. Am I correct in using a Wilcoxon-Signed rank test procedure, matching the number of daily crossings before and after, to compare before and after?

  2. I want to perform an aggregate analysis as well as a species stratified analysis. When stratifying by species, there are many 0-crossing days, so lots of days with zeros by species which result in a large number of ties. Does a Pratt correction fix this problem?

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As I see it the limitations of your proposed analysis are:

  1. The data are not, in fact, paired the first day post-intervention takes place a year and a half after the first day pre-intervention.
  2. You do not account for seasonality in time series data
  3. The Wilcoxon does not actually quantify the differences in rates pre/post modification
  4. The Wilcoxon does not handle small, unmeasured sources of dependence in the data (repeat crossings by same animal).
  5. The Wilcoxon, even with Pratt correction, downweights/discards zero observations which is undesirable when such observations are very informative regarding the actual rate of crossing.

All of these issues can be solved by fitting a quasipoisson GLM. I can let an ecologist correct me if I'm wrong in my thinking here. The intervention can be handled with a pre/post indicator. You can adjust for seasonality using fixed effects.

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In order to use the signed rank test, you have to be able to logically match an observation in the one group (pre-) to an observation in the other group (post-). It sounds like you don't have a way to do this, unless, for example, you could match by calendar date from one year to three years later.

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