Question: I hope this isn't either far too basic or redundant. I have been looking around for guidance but so far I am still uncertain of how to proceed.

My data consists of counts of a particular structure used in conversations between pairs of interlocutors. The hypothesis I want to test is the following: more frequent use of this structure by one speaker will tend to increase the frequency of the structure by the other speaker (i.e., this might be evidence of a priming effect).

So I just have two vectors, the counts for speaker A and the counts for speaker B are the columns, and if they are lined up each row represents a particular conversation, like this:

A B  

0 1  

0 2  

1 0  

3 1  

0 2  

2 0  

2 1

There are about 420 conversations (rows). There are lots of zeros in this data.

What would be the best way to analyze this data? I am using R, if that makes a difference.

Here is a plot of the frequencies (counts). The x-axis is number of uses by speaker A, the y-axis number of uses by speaker B. The distinction between speakers means only that speaker A spoke first, and there's no special reason why they did. Otherwise the distinction between speaker A and speaker B is basically meaningless:

[Valid XHTML http://phonematic.com/convplot.jpg](http://phonematic.com/convplot.jpg)

And this is frequency relative to number of sentences spoken by each speaker in each conversation. :

[Valid XHTML http://phonematic.com/rs\_plot.jpg](http://phonematic.com/rs_plot.jpg)

(I should mention that I have thrown out conversations with no hits at all, i.e. {0,0}.)

Answer: 
You seem to have ordered categorical data, therefore I suggest a linear-by-linear test as described by Agresti (2007, p229 ff). Function `lbl_test()` of package `coin` implements it in R.

Agresti, A. (2007). Introduction to Categorical Data Analysis. 2nd Ed. Hoboken, New Jersey: John Wiley & Sons. Hoboken, NJ: Wiley.
I would maybe start with a [rank correlation](http://en.wikipedia.org/wiki/Rank_correlation) analysis.  

The issue is that you may have very low correlations as the effects you are trying to capture are small.

Both Kendall and Spearman correlation coefficients are implemented in R in

```
cor(x=A, y=B, method = "spearman")  
cor(x=A, y=B, method = "kendall")

```