Lee's research on social robots found that personalized conversation with snackbot increased the likelihood customers cooperated with the social robot. but lee wonders if maybe a customer's gender might be complicating this finding. lee might consider treating gender as:

Lee might consider treating gender as: an independent variable.

The research detailed above examines the effect of an independent treatment variable (personalized conversation with snackbot) on a dependent variable: likelihood customers cooperated with the social robot. The independent variable involves a treatment and is dicotomic: equals 1 if there is personalized conversation and 0 if that option is not available.

But it might arise the idea that the results, the likelihood of cooperation with the robot, could be different depending on the gender of the subjects. It is probable that gender affects the dependent variable on its own, or it can also modify the effectiveness of the treatment. This is why the gender variable needs to be controlled for in the statistics model: on one side to know its effect on the dependent variable, and on the other hand, to obtain the pure treatment effect, not contaminated by the gender distinction.

Lee's research on social robots found that personalized conversation with snackbot increased the likelihood customers cooperated with the social robot. but Lee wonders if maybe a customer's gender might be complicating this finding. Lee might consider the treating gender as an independent variable.