I am dealing with a large data set (~100000 observations, roughly 13 variables). Most variables are of categorical nature and are stable across different observations concerning the same individual/entity, e.g. social class or income, however some are not.
My Problem is rooted in the fact that there is a varying number of observations per individual.. so there might be 3 observations belonging to the same individual or there might be 17 observations.. Therefore, I want to aggregate all observations for each individual which is rather unproblematic for variables like social class (in this data set no individual is changing social classes). So, the aggregated social class variable will be of the same categories as the original one.
However, there is one variable "brand loyality" which takes on the values of 0 (=brand loyal) or 1 (=not brand loyal). Each observation describes a purchase of a certain type of homogenous good. If 2 consecutive purchases come frome the same brand the value is 0, otherwise 1. The variable as a whole merely checks what brand was bought last (so it is kind of a lagged variable somehow) Aggregating this variable yields a number between 0 and 1 and is not of categorical nature anymore (e.g. 0.34)
Nevertheless, I believe this variable, even though not categorical anymore, conveys some information, e.g. if the variable would take on a value of 0.2 then the individual is on average rather brand loyal. If the value would be 0.8 then I would say the individual is on average by trend not loyal.
Is it good statistical practice (probably not :p) to include this aggregated variable in my regression model? And if so, I would like to know some more about the theoretical idea behind this (Why is it ok?/Why is it not ok?)
I was just wondering because this variable is highly significant in my linear model and I don't know why - at least not from the statistical point of view. From the theoretical point of view/according to my intuition this makes a lot of sense.