My research involves peer nominations, in which a participant's score is based on the number of times the individual is nominated out of a list of peers as fitting a given criterion (e.g., "Circle the names of everyone in your class who is really popular").

Recently, I've been involved in an argument about the nature of peer nomination data-- specifically, whether it is count data or parametric data. Although scoring is based on the count of nominations, it is also possible to see each nomination or non-nomination as a binary data point which, when combined, measure a continuously distributed latent variable.

I'm wondering whether there are any statistical properties that should differentiate count data and parametric data. In other words, is there an aspect of my peer nomination data that I can analyze, or a test I can run, that will allow me to determine whether the data is parametric or nonparametric?

I'm wondering whether there are any statistical properties that should differentiate count data and parametric data. In other words, is there an aspect of my data that I can analyze, or a test I can run, that will allow me to determine whether the data is parametric or nonparametric?

My specific research involves peer nominations, in which a participant's score is based on the number of times the individual is nominated out of a list of peers as fitting a given criterion (e.g., "Circle the names of everyone in your class who is really popular").

Recently, I've been involved in an argument about the nature of peer nomination data-- specifically, whether it is count data or parametric data. Although scoring is based on the count of nominations, it is also possible to see each nomination or non-nomination as a binary data point which, when combined, measure a continuously distributed latent variable.

If there's a general way to differentiate count vs. parametric data, it would provide me with a way of addressing this argument.