Low incidence vs missing data
Posted: Wed Jan 17, 2018 5:03 pm
Hi,
I come from a regression background, which allow us to consider a data stream for which some independent variables are zero.
For instance, in a Market Mix model, if a price promotion, promotion, advertising, etc. does not happen, I just enter zero as the value of this variable. The data is not missing, it just happen that the activity did not take place.
Currently, I am evaluating client interactions by using surveys (scales are in a 5, 7 or 10-point scale). My final and target variable is "Overall Satisfaction" score, only using observed variables; there was no need to create latent constructs. The observed variables are the satisfaction scores of specific touch point interactions (call center, web center, etc.); each one with certain incidence. Not every one will have the same type of interactions.
My current issues with SmartPLS:
- Let's say I have 1,000 interactions with the call center, and 100 with the web site, but only 50 had interactions with both.
- To use both interactions in the same model, SmartPLS will consider that I have 950 missing variables for the people who had interaction with the call center only, and 50 for the people who interacted with the web
- Actually, the data is not missing, instead, the interaction did not take place - however, if I enter zero, SmartPLS will account for it as a valid number, and standardize before entering the model
My questions:
- In the context of analyzing survey data (in which zero might be part of the scale), how can SmartPLS handle an interaction that did not take place, without considering it a missing value?
Thank you,
Tks,
I come from a regression background, which allow us to consider a data stream for which some independent variables are zero.
For instance, in a Market Mix model, if a price promotion, promotion, advertising, etc. does not happen, I just enter zero as the value of this variable. The data is not missing, it just happen that the activity did not take place.
Currently, I am evaluating client interactions by using surveys (scales are in a 5, 7 or 10-point scale). My final and target variable is "Overall Satisfaction" score, only using observed variables; there was no need to create latent constructs. The observed variables are the satisfaction scores of specific touch point interactions (call center, web center, etc.); each one with certain incidence. Not every one will have the same type of interactions.
My current issues with SmartPLS:
- Let's say I have 1,000 interactions with the call center, and 100 with the web site, but only 50 had interactions with both.
- To use both interactions in the same model, SmartPLS will consider that I have 950 missing variables for the people who had interaction with the call center only, and 50 for the people who interacted with the web
- Actually, the data is not missing, instead, the interaction did not take place - however, if I enter zero, SmartPLS will account for it as a valid number, and standardize before entering the model
My questions:
- In the context of analyzing survey data (in which zero might be part of the scale), how can SmartPLS handle an interaction that did not take place, without considering it a missing value?
Thank you,
Tks,