Introduction
There are lots of potential outcome variables that can be used when conducting a driver analysis,
from five-point rating scales through to utilities from conjoint studies. To conduct a valid driver analysis
we need to select an appropriate regression type or generalized linear model (GLM) which is consistent with our data.
This article describes how to select the Regression Type suitable for your Driver Analysis.
Requirements
A Driver Analysis output (See: How to Do Driver Analysis).
Method
- The type of Regression type required will depend on the outcome variable used in your Regression model. To find the Outcome variable select the output and go Inputs > Linear Regression > Outcome.
- OPTIONAL: To view all potential outcomes of the dependent variable go to the Data Sets tree, select the Outcome variable and go to the object inspector > Properties > DATA VALUES > Values. Alternatively, you can also drag and drop the variable onto the Page to create a table containing all outputs.
- Go to Inputs > Regression Type and select the appropriate model depending on the number of categories of the Outcome variable.
Outcome variable Example Regression Type Two categories 1. Yes / Selected
2. No / Not selected
Binary logit (also known as
logistic regression)Three to 11 ordered
categories1. Hate
2. Dislike
3. Acceptable
4. Like
5. LoveOrdered logit 12 or more ordered
categoriesHow would you rate your
happiness on a scale of 0 to
100Linear regression Net Promoter Score
(NPS)-100: Detractor
0: Passive/Neutral
100: PromoterLinear regression Purchase or usage
quantitiesNumber of cans of coke
consumed per weekNBD (or, if you get a weird
message, quasi-Poisson
regression)Utilities from a conjoint
or MaxDiff studyThat is, a variable that
contains the estimated utilities
for each respondentLinear regression Probabilities or shares
from conjoint and
MaxDiff studiesThat is, a variable that
contains the probabilities for
each respondentUse the utilities instead of the preference shares, and then use Linear regression.
See Also
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