James, yes, you can use the term lurking variable since you did not omit this variable intentionally. Below is a quote from a link and the corresponding link that explains lurking variable and its effect on your data. Did you consider confounders when you developed your models?

A lurking variable is a variable that is not included as an explanatory or response variable in the analysis but can affect the interpretation of relationships between variables. A lurking variable can falsely identify a strong relationship between variables or it can hide the true relationship. For example, a research scientist studies the effect of diet and exercise on a person's blood pressure. Lurking variables that also affect blood pressure are whether a person smokes and stress levels.

In statistical models, the error term explains lurking variables that affect the process. To discover lurking variables, you must take the time to understand your data and the important variables that can affect a process. You can also create a plot of the data to look for non-linear trends that can identify the presence of lurking variables.

http://support.minitab.com/en-us/minitab/17/topic-library/modeling-statistics/regression-and-correlation/regression-models/what-is-a-lurking-variable/

Hello James,

Please follow the attached link

Regards

http://muse.jhu.edu/login?auth=0&type=summary&url=/journals/merrill-palmer_quarterly/v052/52.3von_eye.pdf

Thank you.

I'm just trying to get the terminology straight.

Thanks for this in particular Joan: "...you can use the term lurking variable since you did not omit this variable intentionally."